diff --git a/CHANGELOG.md b/CHANGELOG.md index f20677fa0..9ec27638d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -30,8 +30,17 @@ - Extend `NystroemSketchInfluence` with block-diagonal and Gauss-Newton approximation [PR #596](https://github.com/aai-institute/pyDVL/pull/596) +- Extend `ArnoldiInfluence` with block-diagonal and Gauss-Newton + approximation + [PR #598](https://github.com/aai-institute/pyDVL/pull/598) +- Extend `CgInfluence` with block-diagonal and Gauss-Newton + approximation + [PR #601](https://github.com/aai-institute/pyDVL/pull/601) -### Fixed +## Fixed +- Replace `np.float_` with `np.float64` and `np.alltrue` with `np.all`, + as the old aliases are removed in NumPy 2.0 + [PR #604](https://github.com/aai-institute/pyDVL/pull/604) - Fix a bug in pydvl.utils.numeric.random_subset where 1 - q was used instead of q as the probability of an element being sampled @@ -61,7 +70,30 @@ to `regularization` and change the type annotation to allow for block-wise regularization parameters [PR #596](https://github.com/aai-institute/pyDVL/pull/596) - + - Renaming of parameters of `ArnoldiInfluence`, + `hessian_regularization` -> `regularization` (modify type annotation), + `rank_estimate` -> `rank` + [PR #598](https://github.com/aai-institute/pyDVL/pull/598) + - Remove functions remove obsolete functions + `lanczos_low_rank_hessian_approximation`, `model_hessian_low_rank` + from `influence.torch.functional` + [PR #598](https://github.com/aai-institute/pyDVL/pull/598) + - Renaming of parameters of `CgInfluence`, + `hessian_regularization` -> `regularization` (modify type annotation), + `pre_conditioner` -> `preconditioner`, + `use_block_cg` -> `solve_simultaneously` + [PR #601](https://github.com/aai-institute/pyDVL/pull/601) + - Remove parameter `x0` from `CgInfluence` + [PR #601](https://github.com/aai-institute/pyDVL/pull/601) + - Rename module + `influence.torch.pre_conditioner` -> `influence.torch.preconditioner` + [PR #601](https://github.com/aai-institute/pyDVL/pull/601) + - Refactor preconditioner: + - renaming `PreConditioner` -> `Preconditioner` + - fit to `TensorOperator` + [PR #601](https://github.com/aai-institute/pyDVL/pull/601) + + ## 0.9.2 - 🏗 Bug fixes, logging improvement ### Added diff --git a/docs/influence/influence_function_model.md b/docs/influence/influence_function_model.md index 36047a860..c4642d5aa 100644 --- a/docs/influence/influence_function_model.md +++ b/docs/influence/influence_function_model.md @@ -23,37 +23,45 @@ gradient method, defined in [@ji_breakdownfree_2017], which solves several right hand sides simultaneously. Optionally, the user can provide a pre-conditioner to improve convergence, such -as a [Jacobi pre-conditioner -][pydvl.influence.torch.pre_conditioner.JacobiPreConditioner], which +as a [Jacobi preconditioner +][pydvl.influence.torch.preconditioner.JacobiPreconditioner], which is a simple [diagonal pre-conditioner]( https://en.wikipedia.org/wiki/Preconditioner#Jacobi_(or_diagonal)_preconditioner) based on Hutchinson's diagonal estimator [@bekas_estimator_2007], -or a [Nyström approximation based pre-conditioner -][pydvl.influence.torch.pre_conditioner.NystroemPreConditioner], -described in [@frangella_randomized_2023]. +or a [Nyström approximation based preconditioner +][pydvl.influence.torch.preconditioner.NystroemPreconditioner], +described in [@frangella_randomized_2023]. ```python -from pydvl.influence.torch import CgInfluence -from pydvl.influence.torch.pre_conditioner import NystroemPreConditioner +from pydvl.influence.torch import CgInfluence, BlockMode, SecondOrderMode +from pydvl.influence.torch.preconditioner import NystroemPreconditioner if_model = CgInfluence( model, loss, - hessian_regularization=0.0, + regularization=0.0, rtol=1e-7, atol=1e-7, maxiter=None, - use_block_cg=True, - pre_conditioner=NystroemPreConditioner(rank=10) + solve_simultaneously=True, + preconditioner=NystroemPreconditioner(rank=10), + block_structure=BlockMode.FULL, + second_order_mode=SecondOrderMode.HESSIAN ) if_model.fit(train_loader) ``` -The additional optional parameters `rtol`, `atol`, `maxiter`, `use_block_cg` and -`pre_conditioner` are respectively, the relative +The additional optional parameters `rtol`, `atol`, `maxiter`, +`solve_simultaneously` and `preconditioner` are respectively, the relative tolerance, the absolute tolerance, the maximum number of iterations, -a flag indicating whether to use block variant of cg and an optional -pre-conditioner. +a flag indicating whether to use a variant of cg to +simultaneously solving the system for several right hand sides and an optional +preconditioner. + +This implementation is capable of using a block-diagonal +approximation, see +[Block-diagonal approximation](#block-diagonal-approximation), and can handle +[Gauss-Newton approximation](#gauss-newton-approximation). ### Linear time Stochastic Second-Order Approximation (LiSSA) @@ -78,7 +86,7 @@ from pydvl.influence.torch import LissaInfluence, BlockMode, SecondOrderMode if_model = LissaInfluence( model, loss, - regularization=0.0 + regularization=0.0, maxiter=1000, dampen=0.0, scale=10.0, @@ -114,16 +122,22 @@ the Hessian and \(V\) contains the corresponding eigenvectors. See also [@schioppa_scaling_2022]. ```python -from pydvl.influence.torch import ArnoldiInfluence +from pydvl.influence.torch import ArnoldiInfluence, BlockMode, SecondOrderMode if_model = ArnoldiInfluence( model, loss, - hessian_regularization=0.0, - rank_estimate=10, + regularization=0.0, + rank=10, tol=1e-6, + block_structure=BlockMode.FULL, + second_order_mode=SecondOrderMode.HESSIAN ) if_model.fit(train_loader) ``` +This implementation is capable of using a block-matrix +approximation, see +[Block-diagonal approximation](#block-diagonal-approximation), and can handle +[Gauss-Newton approximation](#gauss-newton-approximation). ### Eigenvalue Corrected K-FAC @@ -201,7 +215,7 @@ see also [@hataya_nystrom_2023] and [@frangella_randomized_2023]. The essential parameter is the rank of the approximation. ```python -from pydvl.influence.torch import NystroemSketchInfluence +from pydvl.influence.torch import NystroemSketchInfluence, BlockMode, SecondOrderMode if_model = NystroemSketchInfluence( model, loss, diff --git a/notebooks/influence_synthetic.ipynb b/notebooks/influence_synthetic.ipynb index 6a58dbc79..25b7ada53 100644 --- a/notebooks/influence_synthetic.ipynb +++ b/notebooks/influence_synthetic.ipynb @@ -848,7 +848,7 @@ "influence_model = CgInfluence(\n", " model,\n", " F.binary_cross_entropy,\n", - " hessian_regularization=0.0,\n", + " regularization=0.0,\n", ")\n", "influence_model = influence_model.fit(train_corrupted_data_loader)\n", "influence_values = influence_model.influences(\n", diff --git a/notebooks/influence_wine.ipynb b/notebooks/influence_wine.ipynb index b4735dcfd..7ec902438 100644 --- a/notebooks/influence_wine.ipynb +++ b/notebooks/influence_wine.ipynb @@ -272,7 +272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48f669980b6d49a08c84fcd250dd5287", + "model_id": "e2cb0003db824943874a926f33a57b58", "version_major": 2, "version_minor": 0 }, @@ -334,7 +334,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABSMAAAKvCAYAAACRY9qRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABn/ElEQVR4nO3deZxlZ10n/s9T1UvSW2VPd2erhCQkQlgSCIsikV0bg6MgoihuKOjMbxRFC3ScixutIDqOjMvosCgoGFHBUnBgFFDWRBL2JCSppLuT7qSTzu1OOr3W+f1xT6VvKlXddbuq7q2q+36/Xud1zvPcc8793s6pm+pPP+c8paqqAAAAAADMt4FeFwAAAAAA9AdhJAAAAADQFcJIAAAAAKArhJEAAAAAQFcIIwEAAACArhBGAgAAAABdIYwEAAAAALpCGAkAAAAAdMWyXhfQa6WUkmRjkj29rgUAAAAAFqm1Se6sqqo62k59H0amFURu7XURAAAAALDInZ1k29F2EEYeGRF5doyOBAAAAIBOrU1rsN8xszVh5BF7qqra3esiAAAAAGAxaT0FcWZMYAMAAAAAdIUwEgAAAADoCmEkAAAAANAVwkgAAAAAoCtMYAMAAADAgnXdddetTbIhBtX1wniSu6644opjzpI9U6Wqqrk616JUSlmXpJlkyGzaAAAAAAvDddddN5DkjYODgz9USlmeZOZTNjNXqqqqDh4+fPjdSX7ziiuuGJ9qp07yNSMjAQAAAFiI3rh8+fLXrl+//sDq1av3llL6e0RdD1RVVR588MFV27dvf+3BgweT5Ndne04jI42MBAAAAFhQrrvuunWDg4PXbty4cfkZZ5xxb6/r6Xd33333qXfeeefBw4cPXzHVLdud5GvutQcAAABgoVlfSlm+evXqvb0uhKQembo8rWd3zoowEgAAAICFZiBJcWv2wlD/dyiZgyxRGAkAAAAAdIUwEgAAAADoCmEkAAAAACwh3/M93zN81llnXdbrOqayrNcFAAAAAEA/KKVcMZP9PvShD9304he/+FGzVi8FwkgAAAAA6IK3v/3tt7W33/Oe95z6qU99at3k/ic+8YkPzeZ93vve994+Pj4+m1PMG2EkAAAAAHTBT/3UT93X3v7MZz6z5lOf+tSj+ifbs2fPwNq1a2ecLq5cuXLBzkLumZEAAAAAsEBceeWVj73ooose98lPfnLVU57ylMeeeOKJT/6v//W/npUkf/EXf3HSVVdddeEZZ5zxhBUrVlx+zjnnPP71r3/9hkOHDj3iHJOfGXnjjTeuKKVc8Su/8itnvvWtbz3tnHPOefyKFSsuf/zjH3/pxz/+8VXd/HxGRgIAAADAAnL//fcPvuQlL7no6quvvu/7vu/77l2/fv2hJHnXu9516urVq8df85rX7FizZs34v/7rv65961vfunH37t2Df/zHf7z1WOe95pprTnnwwQcHX/WqV91TSskf/MEfrH/5y19+4e233/6lbo2mFEYCAAAAsCiMV1X27j+0YO70XbVy2fhAKXN+3p07dy7/7d/+7dtf//rX72zv/9u//dtb16xZ83Bo+Au/8Av3fP/3f/+57373u0//vd/7vW0nnnjiUQPFu+66a8VNN9305dNPP/1wklxyySX7XvnKV174gQ98YN0rXvGK5px/kCkIIwEAAABYFPbuPzTw+MY/P7nXdUz4cuMFX1hzwvI5nylmxYoV1X/5L//l3sn97UHkrl27Bvbt2zfwrGc964G//Mu/PP36668/4RnPeMZRJ7558YtfvGsiiEySF7zgBQ8kyS233LJyLus/GmEkAAAAACwgZ5xxxoETTjjhUaMcr7322hPe8IY3nPWZz3xm7QMPPDDY/tr9998/OHn/yc4999wD7e2JYHLXrl3HPHauCCMBAAAAWBRWrVw2/uXGC77Q6zomrFq5bM5HRSbJVEHkzp07B5///Oc/dvXq1eO/8Au/cOeFF164/8QTTxy/9tprV/3Gb/zG2YcPHz7m/eKDg4NT3sZdVdXc32s+DWEkAAAAAIvCQCmZj9uiF4N/+qd/Wnv//fcve+9733vjt3/7tz8w0d/NW6zngjByiRseGd2Q5GlJ7h3bvOmTva4HAAAAgM5NjGocHz+Sxe7bt6/86Z/+6Rk9K+o4CCOXvh9M8ltJ3pdEGAkAAACwCD3nOc95YN26dYd/4id+4vxXv/rVd5dSqve9732nVtVRJ9BecBbMVOjMmy/W6yf0tAoAAAAAjtv69esPX3PNNTeffvrpB3/rt35r49vf/vb1z372s3f/5m/+5tZe19aJstjS07lWSlmXpJlkqKqq3b2uZ64Nj4xuTLItyXiSNWObNx11incAAACAXrvuuusuWbZs2YcvuuiiB1atWrWv1/X0u717955w8803rzl06NCLrrjiiq9Pfr2TfM3IyKXvriT3pvXf+pt6XAsAAAAAfUwYucSNbd5UJbmhbrpVGwAAAICeEUb2B8+NBAAAAKDnhJH9QRgJAAAAQM8JI/vDRBj5xOGR0dLTSgAAAADoW8LI/vDVtGbTPjXJ+h7XAgAAAECfEkb2gbHNmx5KcmPdfGIvawEAAACgfwkj+4fnRgIAAADQU8LI/iGMBAAAAKCnhJFLXWPo6jSG/vl9K371krpHGAkAAABATwgjl77Tkjz/CeXWC+r2pcMjoyt6WRAAAAAA/UkYufRdmyQn5MBlJeP3J1mW5JKjHgEAAADAonDjjTeuKKVc8fu///un9rqWmRBGLn1fTbKvlKy7tNxxc91nRm0AAACAHnjOc55z4YknnvjkXbt2TZvLXX311ecvX7788u3btw92s7ZuEEYudY3moSRfSJLnDHxhV93ruZEAAAAAPfCKV7zi3n379g285z3vOXmq1/fs2TPw0Y9+9KRnPetZu9evX3+42/XNN2Fkf7g2Sb518IulbgsjAQAAAHrgFa94xf2rV68ef9/73nfKVK+/973vPemhhx4aeMUrXnFvt2vrBmFkf7g2SR5btpxet4WRAAAAAD2wZs2a6oUvfOGuT3/602u3bdu2bPLr73vf+05ZvXr1+POe97wHfuInfuLsiy+++JtWrVr15DVr1jz5W7/1Wy/69Kc/fWIv6p4rwsj+cG2SrMveiwYyXiVZPzwyekaPawIAAADoS6985SvvO3z4cHnXu971iFu1d+zYMfjJT35y3Qte8IJdW7ZsWf7hD3/4pOc///nNX/3VX93y2te+dvuNN9544gte8ILHjo2NLe9V7bP1qPSVJenGJA+WktWPLXfc/rVq+LwklyX5WI/rAgAAAJi5ajw58ODCGVy3YvV4SuflfOd3fufu008//eBf//Vfn/rGN77xnon+d7/73ScfOnSovPKVr7zvqU996kO33XbblwcHj8xh8+M//uP3PuEJT3j829/+9tPe8pa33DU3H6K7hJH9oNE8nMbQfyR51rcOfOnurx0ePi/JkyOMBAAAABaTAw8O5M1nP7nXZTzsDVu/kJVrxzs9bNmyZbn66qvv+7M/+7Mzb7zxxhWPfexjDyTJ+9///lNPPfXUQ1dfffXuZcuOxHaHDh3Kzp07B9etWzc+PDy87/rrr181h5+iqxZOksx8a01iM/DFg3X7yh7WAgAAANDXXvWqV92XJO94xztOSZJbbrll+XXXXbfm6quvvm/ZsmU5fPhw3vSmN51x3nnnPf6EE064YsOGDU/auHHjE2+66aYT9+zZM3j0sy9cRkb2j88nyeMGxobq9tN6WAsAAABA51asHs8btn6h12U8bMXqjkdFTnjWs5619/zzz9/3gQ984JTNmzdvf+c733lKVVV51atedW+SvOENb9jwlre8ZePLXvaync973vO2nXbaaYcHBgaq17/+9eeOj4+XufsQ3SWM7B/XJslQHnzMshyqDmXZucMjoxvGNm9alM8XAAAAAPpQGcjx3Ba9UL30pS+97y1vecvGz372sydec801p5533nn7n/3sZ+9Nkg9+8IMnP+1pT9vz/ve///b2Y37yJ39y8OSTTz7Um4pnz23a/eOWJM1ScsLjy9g36j6jIwEAAAB65Ed/9EfvTZJf+qVf2vj1r3/9xO/5nu+5d+K1wcHBqqqqR+z/f/7P/zn57rvvXrQzaSfCyP7RaI4nuS5JnjP4he11rzASAAAAoEcuueSSA09+8pMf/NjHPnZSkvzIj/zIfROvveAFL2h+7nOfW/vSl750+Hd+53dO++Ef/uFzXve615139tln7+9ZwXNAGNlfJiaxOVy3n97DWgAAAAD63vd+7/femySXXXbZg49//OMfDhp/8zd/865Xv/rVOz7xiU+s++Vf/uVzvvSlL636m7/5m5s3bNhwcPqzLXxl8nDPflNKWZekmWSoqqrdva5nXjWGXpbk/XurlV/5pv3veFySB5KcNLZ50+FjHAkAAADQNdddd90ly5Yt+/BFF130wKpVq/b1up5+t3fv3hNuvvnmNYcOHXrRFVdc8fXJr3eSrxkZ2V+uTZITs//iFTn4QJI1Sb6ptyUBAAAA0C+Ekf1lLMl9pWT55QM3f63uc6s2AAAAAF0hjOwnjWaVenTkCwaunZidySQ2AAAAAHSFMLL/fD5JvmXgS4N1WxgJAAAAQFcII/vPtUlyftm+sW4/bnhkdF0P6wEAAACgTwgj+8+1SbK8HL7khOy/I0lJ8pTelgQAAABAPxBG9p9tSXYkGXzmwFdurvtMYgMAAAAsJONJUlVVr+sgj/jvMD7bcwkj+03bJDbfPvC5Zt3ruZEAAADAQnJvVVWHDh06tKzXhZAcOnRoWVVVh5LsnO25hJH96dokeebgV06o208bHhktPawHAAAAoN2uqqq2N5vNtb0uhKTZbK6tqmp7kvtney7pcn+6Nkk25L7hJIeSnJnkvCRjvSsJAAAAoOWKK64Yv+666956//33/8+VK1eeumbNmgdLMY6q26qqygMPPLD6/vvvHx8fH3/LFVdcMevbtIWR/em6JBko1SVrs/f6PVl1eVq3ao/1tCoAAACAIz548ODBx911112vKKWs6XUx/aqqqgcOHz78l0k+NBfnE0b2o0bzrjSGtiU566qB68c+NP7My9OaxOZ9Pa4MAAAAIElrdGSS37juuuv+Z5L18bjBXhhPsv2KK67YPVcnFEb2r2uTnPXiwc/s/dD4MxOT2AAAAAALUB2EzVkYRm9JlPvX55PkyoGvr6vblw+PjK7oYT0AAAAALHHCyP51bZKclAcuSXJfkpVJntjTigAAAABY0oSR/eu6JCklF5+S3dfVfW7VBgAAAGDeCCP7VaO5M/Xs2d8++Lltda8wEgAAAIB5I4zsb9cmyYsHPn24bj+9h7UAAAAAsMQJI/vbtUny5IFvnFq3LxweGT31KPsDAAAAwHETRva3a5PkhHLwCUluqvvcqg0AAADAvBBG9rf/qNcXnJn7vlBvCyMBAAAAmBfCyH7WaO5K8o0k+e7BT95d9wojAQAAAJgXwki+kCSbBj87MYnN04ZHRl0XAAAAAMw5oRPXJ8ml5fYzkuxLclKSi3pYDwAAAABLlDCS65NksFRPSnJd3ff0XhUDAAAAwNIljOT6en3J2jw4EUZ6biQAAAAAc04YyV1J7k4y8LLBj99V9wkjAQAAAJhzCyqMLKV8aynlQ6WUO0spVSnlu2ZwzFWllP8opewvpXyjlPLD81/pEtJoVqlHR/7A4MequveJwyOjJ/asJgAAAACWpAUVRiZZneSGJD89k51LKecnGU3yL0melOT3kvxpKeWF81TfUnV9klxQ7jo3yc4kg0ke38uCAAAAAFh6lvW6gHZVVf1Tkn9KklLKTA55TZLbqqr6ubr9tVLKtyT52SQfmZcil6brk6SUPCnJF5I8P8mTk3y+dyUBAAAAsNQstJGRnXpGko9O6vtI3T+lUsrKUsq6iSXJ2vkscJG4vl4/cVkOfaHevrxHtQAAAACwRC32MHJ9kh2T+nYkWVdKme6Zh29I0mxbts5feYvGTUkeSrL6hQOfn5jE5sk9rAcAAACAJWixh5HH481JhtqWs3tbzgLQaB5O8sUk+bFl/zRe9z5heGR0Qd3GDwAAAMDittjDyO1JzpzUd2aS3VVVPTTVAVVV7a+qavfEkmTPfBe5SFyfJE8qt6xP68/khCSX9LIgAAAAAJaWxR5GfjrJcyf1Pb/upzPXJ8lAqZ6U1ozmiedGAgAAADCHFlQYWUpZU0p5UinlSXXX+XX73Pr1N5dS3t12yB8luaCU8tullEtKKT+V5HuT/G53K18Srq/XT07yH23bAAAAADAnFlQYmeQpSb5QL0nytnr7V+v2hiTnTuxcVdVtSTalNRryhiQ/l+THq6r6SLcKXkK+lKRKsv5J5Ru31n1GRgIAAAAwZ0pVVb2uoadKKevSmlV7qH6GZP9qDH0tySUfPvyU177m4Ov+MMnuJCePbd40fowjAQAAAOhTneRrC21kJL11fZI8d+ALJyfZn2RdkvN7WRAAAAAAS4cwknbXJ8nycvgJad22nbhVGwAAAIA5Ioyk3fX1+sk58txOk9gAAAAAMCeEkbS7oV5ffEp2GxkJAAAAwJwSRtJuR5JdScoPL/vIfXXf5cMjo6WHNQEAAACwRAgjOaLRrJJ8LUleNfiRkuRwktOTbOxlWQAAAAAsDcJIJvtakgyVvRcm+Xrd57mRAAAAAMyaMJLJvlavL03yH/W250YCAAAAMGvCSCabGA15acyoDQAAAMAcEkYy2cTIyItPzL4v19uP61UxAAAAACwdwkgmuz3JviQrf2nZe/bWfRcMj4yu6GFNAAAAACwBwkgeqdE8nOTGJHnF4P87OcmDSQaTXNDLsgAAAABY/ISRTOXrSTJYqkuT3FT3PbZ35QAAAACwFAgjmUr7jNo31tvCSAAAAABmRRjJVCbCyEsijAQAAABgjggjmcrDIyMHMi6MBAAAAGBOCCOZys1JxpOctGngMzvrPmEkAAAAALMijOTRGs19SW5Lkl9c/lfL6t7ThkdGT+ldUQAAAAAsdsJIpvO1JDm77Dw/yba6z+hIAAAAAI6bMJLpmMQGAAAAgDkljGQ6X6/Xl0YYCQAAAMAcEEYynYdn1I4wEgAAAIA5IIxkOhNh5FkXly1b6m1hJAAAAADHTRjJ1BrN+5NsT5L/uuxvDte9Fw6PjA72rCYAAAAAFjVhJEfz9SR5wcB1JyXZl2RFkuEe1gMAAADAIiaM5Gi+liTLy+FLktxc97lVGwAAAIDjIozkaExiAwAAAMCcEUZyNBMB5EURRgIAAAAwS8JIjubWen3BQMaFkQAAAADMijCSo7kjyXiSE7594LM76z5hJAAAAADHRRjJ9BrNA2kFkvmZZR84XPduGB4ZXde7ogAAAABYrISRHMutSXLRwLb1SXbUfRf3rhwAAAAAFithJMfy8HMjYxIbAAAAAGZBGMmxCCMBAAAAmBPCSI7llnp9QY4Ek8O9KQUAAACAxUwYybFMBJCPSXJ7vX1ej2oBAAAAYBETRnIsE2Hk+gvKndvrbWEkAAAAAB0TRnIsu5I0k+Rnl10z0Xf28Mjosp5VBAAAAMCiJIzk6BrNKvVzI7994HNrkxxMMphkYy/LAgAAAGDxEUYyE7cmybIyfkGSLXWfW7UBAAAA6IgwkpmYeG7kBUnG6m1hJAAAAAAdEUYyE+1hpBm1AQAAADguwkhm4pZ63R5GDvemFAAAAAAWK2EkM/HwyMhlOXRHvW1kJAAAAAAdEUYyE1uSHE6y8rkDX3ig7hNGAgAAANARYSTH1mgeTHJHknzv4L+Wuvfc4ZHRMv1BAAAAAPBIwkhm6pYkeebAV9YmqZKckOSMnlYEAAAAwKIijGSmbk2SE8uB85LcWfe5VRsAAACAGRNGMlMPT2KTIzNqCyMBAAAAmDFhJDMljAQAAABgVoSRzNREGPmYCCMBAAAAOA7CSGbqlnp9xvrce1e9PdyjWgAAAABYhISRzEyjeX+SXUnyosHPH6h7jYwEAAAAYMaEkXTi1iR53sB1g3VbGAkAAADAjAkj6cStSfKkgVvW1u11wyOjJ/WuHAAAAAAWE2Eknbg1SdaUfWcl2Vn3GR0JAAAAwIwII+lE+yzaZtQGAAAAoCPCSDohjAQAAADguAkj6cREAHluhJEAAAAAdEgYSSfuqNcnbczO7fW2MBIAAACAGRFGMnON5p4ku5LkqsHr99W9wz2rBwAAAIBFRRhJp25Pkm8buKGq20ZGAgAAADAjwkg6dXuSXD5w88q6ffrwyOiqHtYDAAAAwCIhjKRTtyfJKdl9RpI9dd+5vSsHAAAAgMVCGEmnbk+SUsyoDQAAAEBnhJF0qj2A3Fpvn92jWgAAAABYRISRdOqOen1ekm319sYe1QIAAADAIiKMpFMTIyM3rM5D2+vts3pVDAAAAACLhzCSTt2T5KEkuXLg6w/VfcJIAAAAAI5JGElnGs0q9a3azxj4alX3CiMBAAAAOCZhJMfj9iS5fODm5XVbGAkAAADAMQkjOR63J8ljyp2r6/YZwyOjK3pYDwAAAACLgDCS43F7kgzlgdOTHKz7NvSuHAAAAAAWA2Ekx+P2JBkoOS/JnXWfW7UBAAAAOCphJMfjjnp9XpJt9bYwEgAAAICjEkZyPG6v1+cMZHwijNzYq2IAAAAAWByEkRyPbUkOJ1l+Udl2f91nZCQAAAAARyWMpHON5qHUt2c/aeDm/XWvMBIAAACAoxJGcrxuT5Inllurui2MBAAAAOCohJEcr9uT5NKBO1bUbWEkAAAAAEcljOR43Z4k55S719Tts4ZHRksP6wEAAABggRNGcrxuT5KT8sCpdfvEJCf1rBoAAAAAFjxhJMfrjiRZVsbPSbKr7nOrNgAAAADTEkZyvG6v1+eVVNvqbWEkAAAAANMSRnK87qjXa07N7h319sZeFQMAAADAwieM5Pg0mnuT3JMklw7cvqfuNTISAAAAgGkJI5mN25PkcWVsf90WRgIAAAAwLWEks3FHklwycEdVt4WRAAAAAExLGMlsbEmSC8pdK+q2MBIAAACAaQkjmY0tSbK+3Le6bgsjAQAAAJiWMJLZuCNJTsqDJ9ftM4ZHRpf3sB4AAAAAFjBhJLOxJUmW59CGJAeTlCQbeloRAAAAAAuWMJLZ2JIkpWTjQA7fVfe5VRsAAACAKQkjmY3tSQ4lGTy77Lyn7tvYw3oAAAAAWMCEkRy/RvNwkm1JcnHZurvuNTISAAAAgCkJI5mtLUny2LJlX90WRgIAAAAwJWEks3VHklw0sO1w3RZGAgAAADAlYSSztSVJhsv2FXVbGAkAAADAlISRzNYdSbK+3LeqbgsjAQAAAJiSMJLZ2pIkJ+WBk+v2WcMjo6WH9QAAAACwQAkjma0tSbIyB8+o26uSDPWuHAAAAAAWKmEks3VHkpSS00/I/mbdt6GH9QAAAACwQAkjma1dSfYmyTnlnp11nzASAAAAgEcRRjI7jWaV+lbtC8qdu+vejb0rCAAAAICFShjJXLgjSS4sd+6r20ZGAgAAAPAowkjmwpYkeczAneN1WxgJAAAAwKMII5kLdyTJuWXHYN0WRgIAAADwKMJI5sKWJFlfdp1Ytz0zEgAAAIBHEUYyF7YkycnZM1S3jYwEAAAA4FGEkcyFO5LkxBw4vW4LIwEAAAB4FGEkc2FLkgyUavXa7E2SNcMjo2t7WxIAAAAAC40wktlrNPcmuS9Jzi737K17jY4EAAAA4BGEkcyVO5LkvLJjV90WRgIAAADwCAsujCyl/HQpZayUsq+U8tlSypXH2P9nSik3llIeKqVsKaX8binlhG7Vy8O2JMkF5S4jIwEAAACY0oIKI0spL0/ytiRvSnJ5khuSfKSUcsY0+39/ks31/pcm+bEkL0/ym10pmHZbkuT8ctfBur2xh7UAAAAAsAAtqDAyyeuS/O+qqt5RVdVXk7wmyd4kPzrN/s9M8u9VVb23qqqxqqr+OclfJjnqaErmxR1Jcu7A3aVuGxkJAAAAwCMsmDCylLIiyRVJPjrRV1XVeN1+xjSHfSrJFRO3cpdSLkjyHUn+cX6rZQpbkmRD7l1Zt4WRAAAAADzCsl4X0Oa0JINJdkzq35HkkqkOqKrqvaWU05L8WymlpPV5/qiqqmlv0y6lrEyysq1r7ayqZsIdSXJK2bOmbrtNGwAAAIBHWDAjI49HKeWqJG9M8lNpPWPyu5NsKqX8t6Mc9oYkzbZl6/xW2Te2JMmq7D+5ZDwxMhIAAACASRZSGLkzyeEkZ07qPzPJ9mmO+bUkf15V1Z9WVfWlqqr+Nq1w8g2llOk+25uTDLUtZ8+6cpLkziTjA6Vaflp2J8JIAAAAACZZMGFkVVUHklyX5LkTfXWg+Nwkn57msFVJaxhem8MTh0/zPvurqto9sSTZM6vCaWk0D6YOjdeX+5JkaHhkdFVPawIAAABgQVkwYWTtbUleXUp5VSnl0iR/mGR1knckSSnl3aWUN7ft/6Ekry2lfF8p5fxSyvPTGi35oaqqDk8+OfNua5JsLDsP1G2jIwEAAAB42EKawCZVVb2vlHJ6kl9Nsj7J9UleVFXVxKQ25+aRIyF/PUlVr89Kck9aAeUvdatmHmFrkivPKzt2pzUh0YYkt/S2JAAAAAAWigUVRiZJVVV/kOQPpnntqkntQ0neVC/03pYkGS479tdtIyMBAAAAeNhCu02bxW1rkpxT7p64RX5jD2sBAAAAYIERRjKX6mdG3jtYt42MBAAAAOBhwkjm0pYkObXsPrFuCyMBAAAAeJgwkrm0NUnWZu+60ppnyG3aAAAAADxMGMlcujNJNViqZadkT2JkJAAAAABthJHMnUbzYJLtSbKh3JsIIwEAAABoI4xkrm1Nkg3lviQ5ZXhkdGVvywEAAABgoRBGMtcmZtQ+VLeNjgQAAAAgiTCSubclSc4rO/bWbWEkAAAAAEmEkcy9rUlyTrn7QN0WRgIAAACQRBjJ3NuaJGeVnRPtjb0rBQAAAICFRBjJXNuSJGeWXSvqtpGRAAAAACQRRjL3tibJSXlwdVIlwkgAAAAAasJI5tqdSarBMj54SvYkbtMGAAAAoCaMZG41mgeS7EiSDeXexMhIAAAAAGrCSObD1iTZUO5LjIwEAAAAoCaMZD5sSZL1rTDytOGR0ZW9LQcAAACAhUAYyXzYmiRnlZ2H6/b6HtYCAAAAwAIhjGQ+bE2Sc8vd++r2WT2sBQAAAIAFQhjJfNiSJGeXeyZGRnpuJAAAAADCSObF1iRZX+4brNvCSAAAAACEkcyLrUlyanafkFSJMBIAAACACCOZH9uSZFkZHzw5exLPjAQAAAAgwkjmQ6N5IMmOJNlY7kuMjAQAAAAgwkjmT/3cyHsTYSQAAAAAEUYyf7YkyYbWyEi3aQMAAAAgjGTebE2SDa2RkWuHR0bX9rYcAAAAAHpNGMl82ZokZ5WdB+v2hh7WAgAAAMACIIxkvmxJknPKPRNhpFu1AQAAAPqcMJL5sjVJNpZ7S902iQ0AAABAnxNGMl+2JMlpaa5MqkQYCQAAAND3hJHMl21JquXl8MCp2Z0IIwEAAAD6njCS+dFoHkiyPXl4Rm3PjAQAAADoc8JI5tOWJNnYCiONjAQAAADoc8JI5pMwEgAAAICHCSOZT1uSh2/T3jg8MlqOvjsAAAAAS5kwkvl0R/LwyMiVSU7paTUAAAAA9JQwkvm0JUnOLvccqttu1QYAAADoY8JI5tOWJDmr7Kzqthm1AQAAAPqYMJL5dEeSnJbdywdzODEyEgAAAKCvCSOZTzuSHBwoVc7I/YkwEgAAAKCvCSOZP43meJJtSbKx7EyEkQAAAAB9TRjJfGufUdszIwEAAAD6mDCS+bYlSTa0wkgjIwEAAAD6mDCS+bYleXhkpDASAAAAoI8JI5lv7bdprx8eGV3W23IAAAAA6BVhJPOt/TbtgSRn9LQaAAAAAHpGGMl825IkZ5Wd43XbrdoAAAAAfUoYyXzbkiSnlAcGVuZAYkZtAAAAgL4ljGS+7UryYGJGbQAAAIB+J4xkfjWaVR5+buR9iTASAAAAoG8JI+mGiedGJsnZvS0FAAAAgF4RRtINdyTJhtybJOf0thQAAAAAekUYSTfUt2nfmyTn9bYUAAAAAHpFGEk31Ldp35sk5w6PjLruAAAAAPqQUIhuaN2m3QojVyQ5o6fVAAAAANATwki6YWJkZFW33aoNAAAA0IeEkXTDliRZXfaVtdmbJOf2thwAAAAAekEYyfxrNPcmuS9JNpadiZGRAAAAAH1JGEm3tD83UhgJAAAA0IeEkXTLliTZWO5L3KYNAAAA0JeEkXRLHUa6TRsAAACgXwkj6ZY7kuQsYSQAAABA3xJG0i23Jsk55Z4kOWl4ZHRdb8sBAAAAoNuEkXTLrUkyXLZXddtzIwEAAAD6jDCSbrklSU4ru8uJ2ZcIIwEAAAD6jjCS7mg070+yK0nOLXcnnhsJAAAA0HeEkXTTLYkwEgAAAKBfCSPppluTh8NIt2kDAAAA9BlhJN1Uh5E7EiMjAQAAAPqOMJJuar9N28hIAAAAgD4jjKSb2m/T3jg8Mrq8t+UAAAAA0E3CSLrp1iQ5p9yTgYwPJDm7x/UAAAAA0EXCSLppS5JDK8qhnJldiVu1AQAAAPqKMJLuaTQPJxlLHr5V2yQ2AAAAAH1kVmFkKeXcUsq3TOp7Yinl3aWU95VSvmtW1bEUtZ4bObAjMTISAAAAoK8sm+Xxv59kTZLnJUkp5cwk/5JkRZI9SV5aSnlZVVUfmOX7sHS0z6htZCQAAABAH5ntbdpXJvm/be0fSnJikicmOSvJx5L8/Czfg6Xl1iQ5r+xIhJEAAAAAfWW2YeQpSe5ua784ycerqrqlqqrxJB9Icsks34OlpXWbdmtkpNu0AQAAAPrIbMPIe1KPbiulnJTk6Uk+0vb6ssz+VnCWlluS5Jw6jBweGS29LQcAAACAbpltUPjRJP9fKWV3kqvSCjf/ru31b0qyZZbvwdJyW5KcWvZkTfae+EBWnZZWqA0AAADAEjfbkZEjSb6W5K1JXpDk56uqui1JSikrk3xvWs+NhJZGc3eSnYlJbAAAAAD6zaxGRlZVtSPJN5dShpI8VFXVgbaXB5I8N0ZG8mi3JDntnHJ3vloNn5fk2l4XBAAAAMD8m+3IyCRJVVXNSUFkqqp6qKqqG6qqum8u3oMlpX1G7Qt6WwoAAAAA3TKrMLKU8txSyusn9f1oKeWOUsqOUsrvllIGZ1ciS1D7jNoX9bYUAAAAALpltiMjG0meONEopVyW5I/TmpDkX5P8f0l+fpbvwdJzSyKMBAAAAOg3sw0jL80jn/f3g0l2J3lWVVUvT/K/k/zQLN+DpcfISAAAAIA+NNswcnVa4eOEFyX5cFVVe+v252O2ZB7t1iQ5q+zMYA6fNTwyuqrXBQEAAAAw/2YbRm5J8tQkKaVcmOTxSf657fVTkuyf5Xuw9GxLcmB5OZwN5d4kubDH9QAAAADQBbMNI9+T5CdKKR9M8pEku5L8fdvrVyS5aZbvwVLTaI4nuS1xqzYAAABAP5ltGPkbSTYnOSfJHUm+q6qq+5OklHJKkquSfHCW78HSdEuSnF+2J8JIAAAAgL6wbDYHV1V1KMkv1cvk1+5Lsn4252dJ+1qS77iwbEuEkQAAAAB9YVZhZLtSypq0RkgmyZaqqh6Yq3OzJH01SYSRAAAAAP1jtrdpp5Ty1FLKv6T1vMgv18uuUsr/K6U8ZbbnZ8n6apJcPLA1EUYCAAAA9IVZhZGllKcl+USSy5P8aZKfrZc/rfs+UUq5crZFsiR9NUnOLPdnXR5YPzwyurbXBQEAAAAwv+ZiApttSR5bVdVrq6r6/Xp5bZLHJrmz3gceqdHcnWRrklxY7kySC3taDwAAAADzbrZh5NOS/HFVVdsnv1BV1Y4kf5Lk6bN8D5aurybJRQPbEmEkAAAAwJI32zByPEefBGew3gem0npuZPHcSAAAAIB+MNsw8lNJfrqUct7kF0op5yb5qST/Psv3YOn6SpJcJIwEAAAA6AtHG9U4E29MawKbr5dS/jbJTXX/Y5O8JMnhJG+Y5XuwdH01SS5s3aYtjAQAAABY4mYVRlZV9YV6Ru3fSHJ1klX1S3uTfDhJI8nO2bwHS9rXkmRjuS9rs/fiXhcDAAAAwPya7W3aqarqq1VV/ack65JsqJd1VVV9d5LvTLJltu/BEtVo7hqvyvYkubBsO314ZHRdr0sCAAAAYP7MOoycUFXVeFVVO+rFpDXMyECpvpy4VRsAAACgH8xZGAnH6atJclERRgIAAAAsdcJIeq0OI82oDQAAALDUCSPpta8kyUVu0wYAAABY8jqeTbuUcnkHu2/s9Pz0na8lydllZ9blwcf2uhgAAAAA5k/HYWSSa5NUM9y3dLAv/ajRvPfgfz/l3uXl8KmPKXcKIwEAAACWsOMJI39kzqugr5VUX0nyrReUu4aGR0ZPHtu8aVevawIAAABg7nUcRlZV9a75KIT+tayMfzHJt140sDUZzyVJPt3rmgAAAACYeyawYSGoZ9TeliSX9rYUAAAAAOaLMJKFoA4jtybJN/W2FAAAAADmizCSheCrSXLuwD1Zm72X9boYAAAAAOaHMJLeazTvOVgN3p8kF5ctwkgAAACAJWrBhZGllJ8upYyVUvaVUj5bSrnyGPufVEp5eynlrlLK/lLKTaWU7+hWvcyNKuWLSXLBwF0bhkdG1/S6HgAAAADm3oIKI0spL0/ytiRvSnJ5khuSfKSUcsY0+69I8n+TDCd5aZLHJnl1km3dqJe5s6Icui5JLi13JCaxAQAAAFiSFlQYmeR1Sf53VVXvqKrqq0lek2Rvkh+dZv8fTXJKku+qqurfq6oaq6rq41VV3dClepk7X0weDiNNYgMAAACwBC2YMLIe5XhFko9O9FVVNV63nzHNYVcn+XSSt5dSdpRSvlxKeWMpZfAo77OylLJuYkmydu4+BbPwxSS5ZOCOlIwLIwEAAACWoAUTRiY5Lclgkh2T+nckWT/NMRekdXv2YJLvSPJrSX4uyS8f5X3ekKTZtmw9/pKZQ18dr8r4yeWBnFfuvrzXxQAAAAAw9xZSGHk8BpLcneQnqqq6rqqq9yX5jbRu757Om5MMtS1nz3uVHFujue+hrLgjSS4uWx7X63IAAAAAmHsLKYzcmeRwkjMn9Z+ZZPs0x9yV5Kaqqg639X0tyfr6tu9Hqapqf1VVuyeWJHtmWTdzpKT6QpJcUO5aPzwyuqrX9QAAAAAwtxZMGFlV1YEk1yV57kRfKWWgbn96msP+PcmF9X4TLk5yV30+FpFV5cDnk+SSgTtKkkt6XA4AAAAAc2zBhJG1tyV5dSnlVaWUS5P8YZLVSd6RJKWUd5dS3ty2/x+mNZv2/yilXFxK2ZTkjUne3uW6mRtm1AYAAABYwpb1uoB2VVW9r5RyepJfTWvSmuuTvKiqqolJbc5NMt62/5ZSyguT/G5aQda2JP8jyW91s27mzBeT5DHlzqzOQ5f1uhgAAAAA5taCCiOTpKqqP0jyB9O8dtUUfZ9O8vR5Lovu2Lq/WvbQynLoxMsGbruy18UAAAAAMLcW2m3a9LNGs3owJ96cJBeUOz0zEgAAAGCJEUayoAxk/PNJcn7ZfubwyOgJva4HAAAAgLkjjGRBWZe9n0mSx5YtJa2Z0QEAAABYIoSRLCgDpWrNqD1gRm0AAACApUYYyULzlfEqOb00c2HZZhIbAAAAgCVEGMnC0mg+uDur70mSx5Wxp/a6HAAAAADmjjCSBefBnPD1JDmv7PDMSAAAAIAlRBjJgrM8hz+bJOcNbD/djNoAAAAAS4cwkgXntDT/LUkuac2obRIbAAAAgCVCGMmCM1CqLyXJhWVbTsz+J/W4HAAAAADmiDCShWjsQLXs0MpyKJeVW5/V62IAAAAAmBvCSBaeRnO8mdVbk+SCgbuu6HU5AAAAAMwNYSQL0qEMfjlJNpadjxkeGS29rgcAAACA2RNGsiCdlAc+lSQXlO2rkqzvcTkAAAAAzAFhJAvSieXADUlyUdmaJE/sbTUAAAAAzAVhJAvVV5Lk/HKXGbUBAAAAlghhJAvVHQeqZQdWlMN5XBn75l4XAwAAAMDsCSNZmBrN6oGccFuSnD9wl9u0AQAAAJYAYSQL1kCq65Pk7HLPWcMjoyt7XA4AAAAAsySMZMFalwc/lyQXlW0DSS7pcTkAAAAAzJIwkgVroLQmsbnYjNoAAAAAS4IwkoXsK0kyXLZnVR56cq+LAQAAAGB2hJEsZNsOVMseWlbGc2m542m9LgYAAACA2RFGsnA1mtW+LL85Sc4rO76p1+UAAAAAMDvCSBa0lTl4XZKcP7B9aHhk9Mxe1wMAAADA8RNGsqCtLIduSB6exOYJva0GAAAAgNkQRrLQfSVJLjKjNgAAAMCiJ4xkoftykpxXdmRNHrq818UAAAAAcPyEkSx0Ow5Ug3sGS5WLytan97oYAAAAAI6fMJKFrdGsqpSvJMl5Zcfw8Mjo2l6XBAAAAMDxEUay4K0sh65PkosHtpYkV/S2GgAAAACOlzCSxaB9Epun9rYUAAAAAI6XMJLF4CtJcrEwEgAAAGBRE0ayGHw5Sc4p92RVHrqy18UAAAAAcHyEkSx8jeY941V2DpQqjyl3nTc8Mnp6r0sCAAAAoHPCSBaFgdIaHVnfqv2U3lYDAAAAwPEQRrJYtCaxGfDcSAAAAIDFShjJYmESGwAAAIBFThjJYtG6TbseGTk8Mlp6Ww4AAAAAnRJGslh8JUnOLjuzOg+dmeTsHtcDAAAAQIeEkSwOjeZ9SbYnyUVlW+JWbQAAAIBFRxjJYmISGwAAAIBFTBjJYmISGwAAAIBFTBjJYtKaxKYVRj5leGTU9QsAAACwiAhzWExaIyMHtlZJhpJc2NtyAAAAAOiEMJLF5KtJsqHcV9Zmb5Jc2dtyAAAAAOiEMJLFo9G8P8m2JLmodav2N/eyHAAAAAA6I4xksZm4VTtJvq23pQAAAADQCWEki037jNqPHR4Z3dDbcgAAAACYKWEki82Xk+QJA7fsqdtX9a4UAAAAADohjGSxmRgZOXHtulUbAAAAYJEQRrLYfDVJ1pWHVg/lgcTISAAAAIBFQxjJ4tJo7klyR5JcXLaOJ7loeGT0rN4WBQAAAMBMCCNZjL6SJJcP3LylbrtVGwAAAGAREEayGH0pSb554Mu76vZVvSsFAAAAgJkSRrIY3ZAkjx8YW1G3jYwEAAAAWASEkSxGNyTJydlzTsn44SQXDI+MntvjmgAAAAA4BmEki9FNSQ6UkrWPKXd+qe67qof1AAAAADADwkgWn0bzYOpJbJ438B+31r1u1QYAAABY4ISRLFY3JMkLB6/dW7eFkQAAAAALnDCSxao1iU25bV2SQ0nOGx4ZPb+3JQEAAABwNMJIFqsvJsnycvjxST5d931H78oBAAAA4FiEkSxWN9TrCzbk3o/U29/Vo1oAAAAAmAFhJItTo3lvkm1J8ivL//wbde9VwyOjJ/WsJgAAAACOShjJYnZDknz74OdOSfLVJMviVm0AAACABUsYyWL2xXr9xCR/V2+/pDelAAAAAHAswkgWs4nnRj4xyd/X298xPDK6skf1AAAAAHAUwkgWs4kw8rKfWXbNdUnuSrImybf1riQAAAAApiOMZDG7Ocn+JKt/ZtkHLsiR0ZHf1bOKAAAAAJiWMJLFq9E8lOTLdesJORJGXj08MuraBgAAAFhgBDYsdu3PjfyXJHuSbEjylJ5VBAAAAMCUhJEsdg+HkWObN+1P8k91+7t6Uw4AAAAA0xFGsth9sV4/sV57biQAAADAAiWMZLGbGBl5XhpDJyX5xyQHklw6PDL6xGmPAgAAAKDrhJEsbo3mriRb6taTxjZvuj/JB+v2q3pSEwAAAABTEkayFHyuXj+9Xr+rXr9yeGR0eQ/qAQAAAGAKwkiWgk/V62fU648k2ZHk9CTf3pOKAAAAAHgUYSRLwafr9TPTGCpjmzcdTPKeus+t2gAAAAALhDCSpeA/0pq05rQkj6n7Jm7V/s7hkdHTelIVAAAAAI8gjGTxazT3pxVIJvWt2mObN32x7lue5BU9qgwAAACANsJIlorJz41MjoyOdKs2AAAAwAIgjGSpOPLcyCPem+RgkiuGR0Yf3/2SAAAAAGgnjGSpmAgjL0tjaG2SjG3etDPJaN3/w70oCgAAAIAjhJEsDY3mtiR3pHVNP7XtlXfU6x8ZHhld3fW6AAAAAHiYMJKlZGJ0ZPtzI0eT3JrklHh2JAAAAEBPCSNZSh713MixzZsOJ/nduvm64ZHRwa5XBQAAAEASYSRLy0QY+fQ0hkpb/zuS7ErymCRXd70qAAAAAJIII1lark+yL61bsi+e6BzbvOnBJH9UN3+u+2UBAAAAkAgjWUoazQNJrq1bz5z06v9McjDJNw+PjD69q3UBAAAAkEQYydIz1SQ2Gdu86a4k76mbRkcCAAAA9IAwkqXmU/X6GVO89jv1+ruHR0Yv6FI9AAAAANSEkSw1EyMjH5fG0OntL4xt3vTlJB9J67of6XZhAAAAAP1OGMnS0mjuSHJDkpLkhVPs8Wv1+seGR0Yv7VpdAAAAAAgjWZL+sV5/x+QXxjZv+vckf5/Wtf/mbhYFAAAA0O+EkSxFE2HkC9MYGpzi9ZEkh5O8ZHhk9FndKwsAAACgvwkjWYo+k+T+JKckuXLyi2ObN309yZ/Wzd8eHhkt3SsNAAAAoH8JI1l6Gs1DaU1Uk0xxq3btTUn2Jnl6ku/uRlkAAAAA/U4YyVI17XMjk2Rs86a7kry1br55eGR0eVeqAgAAAOhjwkiWqg/X68vTGNowzT5vTXJ3kouSvK4rVQEAAAD0MWEkS1OjeXeSz9etF021y9jmTXuS/OLEEcMjoxd1ozQAAACAfiWMZCk76q3atXcl+b9JTkjyv4dHRv1MAAAAAMwTwQtL2UQY+YI0hqZ8JuTY5k1Vkp9IazKbZyd5dZdqAwAAAOg7wkiWsmuT3JNkXZJnTrfT2OZNY0neWDd/e3hk9Oz5Lw0AAACg/wgjWboazfEcmcjmaLdqJ8kfJPlsWsHlHw6PjJb5LA0AAACgHwkjWeombtX+rjSGpg0YxzZvOpzkx5IcTPLiJD/dhdoAAAAA+oowkqVuNMm+JBcnefLRdhzbvOkrSX6hbr5teGT0afNcGwAAAEBfEUaytDWae5J8qG59/wyO+B9J/ibJ8iTvHx4ZPXW+SgMAAADoN8JI+sF76/X3pTF01Gu+nl37x5J8I8m5Sd49PDLq5wQAAABgDghZ6Af/lKSZ5KwkzzrWzmObNzWTvDSt27u/I0dm2gYAAABgFoSRLH2N5v60br1OklfM5JCxzZtuyJFJbH5teGT0VfNRGgAAAEA/EUbSLyZu1X5ZGkMrZnLA2OZN/yfJ79TNPxseGf3OeakMAAAAoE8II+kX/5pke5JTkrygg+N+Icm7kwymNaHNMW/zBgAAAGBqwkj6Q6N5OMn76taMbtVOkrHNm8aT/HiSf0hyQpIPDY+MPmnO6wMAAADoAwsyjCyl/HQpZayUsq+U8tlSypUzPO77SilVKeXv5rlEFqe/rNfflcbQ6pkeNLZ508EkL0/yb0mGkvzr8Mjos+ehPgAAAIAlbcGFkaWUlyd5W5I3Jbk8yQ1JPlJKOeMYxw0neWuST853jSxan0tya5JVSa7u5MCxzZv2JvnOHAkkPzI8Mvrdc14hAAAAwBK24MLIJK9L8r+rqnpHVVVfTfKaJHuT/Oh0B5RSBpO8J8l/TytsgkdrNKscmcjmlZ0ePrZ50/1pPW/y75KsTHLN8Mjoa+eqPAAAAIClbkGFkaWUFUmuSPLRib6qqsbr9jOOcuivJLm7qqo/m98KWQL+ol6/MI2h9Z0ePLZ500NJXprkj5OUJP9reGT07cMjoyfMYY0AAAAAS9KCCiOTnJbWrMU7JvXvSDJlcFRK+ZYkP5bk1TN5g1LKylLKuoklydpZ1Mti02jemOQzaV1nP3A8pxjbvOlwktemFYInyU8l+dTwyOiFc1IjAAAAwBK10MLIjpRS1ib58ySvrqpq5wwPe0OSZtuydZ7KY+F6V71+VRpD5XhOMLZ5UzW2edOvJfn2JPcmeXKS/xgeGX35HNUIAAAAsOQstDByZ5LDSc6c1H9mku1T7P+YJMNJPlRKOVRKOZTkh5JcXbcfM8Uxb05rApKJ5ew5qp3F431J9ie5LMkTZ3Oisc2bPpzkSWlNbLM2yV8Nj4xeMzwyunG2RQIAAAAsNQsqjKyq6kCS65I8d6KvlDJQtz89xSFfTytQelLb8sEk/1Jvb5niPfZXVbV7YkmyZy4/A4tAo7krreskSV4129ONbd60Ncm3JfmNtML070ny1eGR0Z8cHhldUD9jAAAAAL20EIOStyV5dSnlVaWUS5P8YZLVSd6RJKWUd5dS3pwkVVXtq6rqy+1LkvuT7KnbB3r0GVj43l2vfyCNoeWzPdnY5k2HxjZv+uW0JmD6fFqjbv8oyb8Pj4x+y2zPDwAAALAULLgwsqqq9yX5+SS/muT6tEY4vqiqqolJbc5NsqEnxbGUfCTJ3UlOT/KiuTrp2OZNN6Q18/t/TfJgkqcn+eTwyOjfDo+MXjJX7wMAAACwGJWqqnpdQ0/VM2o3kwzVt23TLxpDb0vys0n+Jo3mS+f69MMjoxuSNJL8eFrB/+G0JlzaPLZ5041z/X4AAAAAvdBJviaMFEb2r8bQE9MafXsgyYY0mvfNx9sMj4xemtbESS+pu6ok1yT5zbHNm66fj/cEAAAA6BZhZAeEkX2uMXR9WjNq/1QazT+cz7caHhl9WpI3Jrm6rfv/JfmDJB8a27zp0Hy+PwAAAMB86CRfW3DPjIQue1e9nvWs2scytnnTZ8c2b3pJkick+csk40mek+QDSW4ZHhl9w/DI6Mb5rgMAAACgV4yMNDKyvzWGzkyyLclgkkvSaHbtWY7DI6PnJnlNklcnOa3uHk9rcp13Jvng2OZN+7pVDwAAAMDxcJt2B4SRpDH0D0k2JXlzGs03dvvth0dGT0jyfUl+LMm3tL20O8kHk/x1kn8WTAIAAAALkTCyA8JI0hh6WZL3J9ma5Lw0muO9KmV4ZPSitG4Zf1WSs9te2pPkw0lGk/zT2OZNd/egPAAAAIBHEUZ2QBhJGkMnJLkryUlJnpdG82O9LSgZHhkdSPKMJC9L8tIkZ7W9XCX5fFqT33w8yb+Pbd60p+tFAgAAAEQY2RFhJEmSxtAfpvX8xj9Po/lDvS6nXR1MXpnWreSbkjx50i6Hk/xHWsHkx5P829jmTfd3s0YAAACgfwkjOyCMJEnSGHp6kk8n2ZtkfRrNBTvSsJ5x+wVJnl0v50/apUrypbRGT34+ybVJvjS2edOBbtYJAAAA9AdhZAeEkSRJGkMlyY1JLkryI2k039nbgmZueGT0nBwJJp+d1meY7ECSG9IKJj+f5PokXzMpDgAAADBbwsgOCCN5WGPol5L8epJ/SaP5nF6Xc7yGR0Y3JHlakqcmeUq9PnmKXQ8nuSmtUZRfbFvfPrZ5U39/MQAAAAAzJozsgDCShzWGzk0ylqQkGU6jeXtvC5obwyOjJa1buSfCyackeUKSU6Y55IG0Qsob6+Xr9fqmsc2b9s57wQAAAMCiIozsgDCSR2gMfSzJc5L8tzSav97rcuZLHVBuSCuUvKxt/U1Jlh/l0C1JbklyW73c2ra93YhKAAAA6D/CyA4II3mExtAPJXlXkpuTPDaNZl/9gAyPjC5P8pgkj62XS9q2Tz3G4fvSGll6W9tye5Jt9XKXSXQAAABg6RFGdkAYySM0htYk2Z5kdZJvTqP5qR5XtGAMj4yeluTiJBekddt3+3JOkoEZnObuHAknJy93JtmR5N6xzZsOz3X9AAAAwPwQRnZAGMmjNIbeleSHkvxxGs3X9LqcxaAeUXlOpg4pz6qXFTM83XiSnWkFl3enFVDePUX73iT3JWkKLwEAAKB3hJEdEEbyKI2h5yT5WJL7k2xIo7mvtwUtfvUzKk9NK5Q8O0cCyvZlY1qT6pQOT1+l9d/qvrbl3knt+5PsSbJ7iuVBz7oEAACA4yeM7IAwkkdpDA2k9bzDc5O8PI3m+3tcUd8YHhldluS0JGckObNeT7d9SpI1c/C24zkSVE4VWD6Q5KEke+ulfXtye/L2vrHNm8bnoEYAAABYsISRHRBGMqXG0K8n+aUk/5hGc1Ovy2FqwyOjK5KcnFYweWq9nrycmmQoydok6yYtM3nO5WwdSnIgyf56fbTt9vbB+tjD9TKb7fG0RpC2L5nHvhxlfbTX5uPYmewzF3/Gh42wBQAA+pUwsgPCSKbUGLo4yY1phQxnp9Hc3uOKmGP1reOrMnVIua7uH0prMqNVSU6s1zPZnunzMVlaDufRofJUwfNM1vsz81G4D7c9PxUAAOgFYWQHhJFMqzH0qSTPSPJzaTTf1utyWDyGR0YH0womT0wrmFyRZOUxtif3LU8ymGRZvR6coj3T1wbSehbnxJJJ7bnsy1HWR3ttPo6d6b4z+TOc2O70mabddiCtcPKBtB47MHmZrn/itd1p/T/x/iR7PGYAAACYCWFkB4SRTKsx9Jokf5jkK0kuS6PZ3z8sQIZHRgcyfVi5LK0QeXLA3Ol6RZITMv0I3Ml9J87Tx61yJJic6XJfkp1J7h3bvOnAPNUFAAAsMMLIDggjmVZj6KQk29L6y/6z02h+orcFATxaHZC2h5er05rcaW3bMrl9tP6h+nyz9UDqYLJt2XmM7b2evQkAAIuPMLIDwkiOqjH0R0l+Msk1aTRf1utyALpheGT0hLRCyZM6WE7OkYmkjndyqP05Ek7enWRH2zK5fY/RlwAAsDAIIzsgjOSoGkOXJfliWhNTDKfR3NrjigAWtHqk5klpBZOnJjltmu3J7eOZ+GlXpg4rJweXd41t3rTveD8TAABwdMLIDggjOabG0L8kuSrJr6fR/G89rgZgyalnt1+TI8Hk6UnOSHJm2/rMtvYZaT2rsxP3JbkryZ1ty+T29rHNm/bP8uMAAEDfEUZ2QBjJMTWGvifJNWmNtDk3jaa/qAL0UD368pRMHVRO3l6f1uRAM3Vvpg4q29vb3SIOAABHCCM7IIzkmBpDy5LcluTsJD+YRvMvelwRADNUj7o8KcnGetkwzfbGdHar+M60gslt9dK+PbHcO7Z50/hcfA4AAFjIhJEdEEYyI42hX0ry60k+m0bz6b0uB4C5VYeWJ+fYoeWGzDy0PJDWiMr2gPJRoeXY5k0PzdkHAQCAHhBGdkAYyYw0hs5IsiWtv4BemUbz8z2uCIAeqEPLU5KclSMB5VmTlo1p3SZeZnja+/PoUZWTQ8u7jbIEAGChEkZ2QBjJjDWG3p3kB5P8VRrNV/S6HAAWruGR0eVpjaJsDygnh5ZnJVk1w1MeSrI9xwgtxzZvemDuPgUAAMyMMLIDwkhmrDH0pCRfSFIleXwaza/2tiAAFrN6lOW6PHpU5eTA8swkAzM87Z4ce5TljrHNmw7N2QcBAKDvCSM7IIykI42hv0ny3Unen0bz5b0uB4Clb3hkdFlageSxQsu1MzzleJIdOXZouXts86b+/kURAIAZEUZ2QBhJRxpDT0hyQ1qjI5+YRvNLPa4IAJIkwyOja/PokHJye0OSwRme8sE8OqCc3L5rbPOmg3P3KQAAWIyEkR0QRtKxxtD7k7wsyd+k0Xxpr8sBgJkaHhkdTGtynekm3pnYPmmGp6yS3JNWMLk9rRGXd0+z3un2cACApUkY2QFhJB1rDD0uyZfSmiX1SWk0b+hxRQAwp4ZHRldl+tvBN7atl3dw2irJvWmFk9MFlg8Hl0kecJs4AMDiIIzsgDCS49IY+ssk35fk79Jo/qdelwMA3TY8MjqQ5LQcCSnPSOvZllOtT8vMJ+GZcCCt8HJnvbRvT25PbAswAQB6QBjZAWEkx6UxdEmSr6T1F6unptG8tscVAcCCVd8efmqmDysn1hPbJxznW00VYO6aYrl/cnts86bDx/meAAB9TxjZAWEkx60x9O4kP5jk2iRPT6PpLzEAMEvDI6Mlyaq0wsvT6mUm28cbYE7YnamDymkDzLR+h9ydZK8RmQBAPxNGdkAYyXFrDG1I8rUkQ0l+Jo3m/+hxRQDQt+rnXE4VUp6U5ORplpOSrJmDtz+cVig5sTSPc3ufUBMAWIyEkR0QRjIrjaGfTPJHSR5M8k1pNO/ocUUAQAeGR0aX59GB5eT2VH0nJVmXzp+FeTSH0gol99TLA23LsdpT9pnBHADoBmFkB4SRzEpjaCDJJ5J8c5J/SHJ1Gs3+/qECgD7Rdkv5UFrB5LoZbE/VtzZJmacy9+XoAebeenmwbXum7f1GcgIAiTCyI8JIZq0x9E1Jrk+yPMnL0mhe09uCAIDFpJ6ZfE2OBJRr2pa1HbYn+pZ1ofQqxx9m7kvyUL2e0bZJhgBg4RJGdkAYyZxoDP1akl9Osj3JZWk0d/a4IgCgT9UjNldkZgHmiUlWpzXCc2I5VntF9z7NIxzMkZCy4zCzXvanNev6dOujvfbwPmObN43P94cFgMVEGNkBYSRzojF0QlqjIx+b5KNJXmR2bQBgKRoeGV2Wo4eVMwk0T0grCD1hiu329vIufaxOHcrMw8wDaQWpE8uhSeu56pvq9cNTLOPT9D/8mrAVgE4JIzsgjGTONIYen+Szaf2CvTmN5ht6XBEAwKI2PDI6mKlDyuPdXlEvKyetp+prf22hhqLzaaYh5lT942ndxl9N2p6qPZN9Om1P9E1luuezTtXfyb5zfY7SYV+n+/db32zOMeFY1+2x+o/nmE7ONZ5H/jyO59E/o3Ox3elxh/PIfyg5NE17ur5Dnk+8OAgjOyCMZE41hl6e5K/q1vek0fxAL8sBAGD26ud6Lk/nIebKellWHz+xXj6Pfe3bg9Ms8zVhEsB8mAg0ZxpiznSf9pHrB2bQNxf7H1yq4aowsgPCSOZcY+h3krwurRkqn5pG8+s9rggAAB5WP1d0INOHlYOzfH0grcCzfZlJ3/EeN925jvaX3eleW0jHTF7Ppq8X51isdU/VN/l6O9q1eLT+4zlmpucayJGfy/afz6P1zdX20V5fNsWyfJr2YJa+fWObN53Y6yLmQyf5Wjdm2YN+84tJLk9yVZIPpjH0rDSaO3pbEgAAtNSjciZunwRYEOp/KBnM1IHl0ULM49lnYlkxaT1V39FeO9b+kx/zcXC2f05LgZGRRkYyHxpDZyT5fJJzk9yQ5NvSaO7qbVEAAABAt9QB67IcCSiXjW3etLO3Vc0Pt2l3QBjJvGkMXZTkk0nOTPLpJC9Io/lAb4sCAAAAmFud5GsD3SkJ+lCjeXOS5yfZleQZSf4ujaETelsUAAAAQO8II2E+NZpfSvLtaU1m89wkf5vG0KreFgUAAADQG8JImG+N5meTfGeSh5K8KMmH0xga6m1RAAAAAN0njIRuaDT/NckLkuxO8qwk/y+NodN6WhMAAABAlwkjoVsazX9LclWSe5JcnuQTaQyd09OaAAAAALpIGAnd1Gh+Ia2RkVuTXJrkM2kMPamnNQEAAAB0iTASuq3RvDHJNyf5SpKNST6ZxtB39LYoAAAAgPknjIReaDTvSPItST6WZE2SD6Ux9NreFgUAAAAwv4SR0CuN5v1JviPJO9L6WfxfaQy9PY2hFT2tCwAAAGCeCCOhlxrNA0l+LMkvJamS/FSSj6UxdGZP6wIAAACYB6Wqql7X0FOllHVJmkmGqqra3et66GONoRcneU+SdUm2JfnuNJqf621RAAAAAEfXSb5mZCQsFI3mPyS5MsnXk5yV5N/SGPrZNIZKbwsDAAAAmBvCSFhIWjNtPy3JB5IsT/K2JP+QxtAZPa0LAAAAYA4II2GhaTR3J3lpWs+P3J/WJDc3pDH0op7WBQAAADBLnhnpmZEsZI2hy5L8VZJvqnv+PMnr0mju7F1RAAAAAEd4ZiQsFY3ml5I8Ncn/SGu27R9M8rU0hn7AsyQBAACAxcbISCMjWSwaQ09L8qdJHl/3fCLJ6824DQAAAPSSkZGwFDWan01yRZJfTrIvybcm+WwaQ+9LY+gxPa0NAAAAYAaMjDQyksWoMXR2kl9L8qokJcmhJO9N8ttpNL/Sy9IAAACA/tJJviaMFEaymDWGnpDkt5K0z7T9oSRvSfJvaTT7+wccAAAAmHfCyA4II1kSGkNXJvnFJP8prZGSSfKVJH+S5M/TaO7qVWkAAADA0iaM7IAwkiWlMfTYJD+X5AeSrKp79yX5QFq3cf9zGs2DPaoOAAAAWIKEkR0QRrIkNYaG0gokfzLJE9peuTfJXye5JsknBJMAAADAbAkjOyCMZElrDJUkT0nyyiQvT3Jm26vNJP+U5O+TfDiN5v1drw8AAABY9ISRHRBG0jcaQ8uSfFuS70vynUlOb3v1UJKPJ/lgkg+l0byt+wUCAAAAi5EwsgPCSPpSY2gwydOSXF0vl07a47YkH6uXf0mjuaO7BQIAAACLhTCyA8JISNIYuiitUPIlSZ6ZZHDSHl9OK5j8f0n+LY3mfd0tEAAAAFiohJEdEEbCJI2htUm+Nclzkjw3yROn2OumJJ9pW76URvNQ12oEAAAAFgxhZAeEkXAMjaHTk1yVVjD5bUkunmKvh5Jcm+RzSW5I8qUkX0ujub9LVQIAAAA9IozsgDASOtQYOjXJlUmeXi9XJjlpij0PJ7kxyRfrpRVQJrcbRQkAAABLhzCyA8JImKXG0EBaoyWfnuSKJJcleUKSk6c54mBaE+TcPMWyLY3mwfkuGQAAAJg7wsgOCCNhHjSGSpKNaYWST8iRgPKiJCcc5cjxJHcmub1e7mhbb02yPcm9aTQPz1vtAAAAQEeEkR0QRkIXtUZRnpVWKHlxvZ5YLkiyYgZnOZzknrSCyemWu5Pcl2RXGs0Dc/shAAAAgHbCyA4II2GBaAWVZyY5N8l5U6w3JDk9SenwzA8m2ZWJcPLR611J9tTLA5OWVp9AEwAAAKYljOyAMBIWkcbQsrQCyfXHWM5IMpTOg8vpHMyRcPKhJPuT7DvKerrXDiY5VC+HO1yP17VUbctcttPWPtp2J/t2co4qRz7roUnb42k0+/t/VgAAAAuYMLIDwkhYohpDg0nWJTklrcl0plufnGRNvaxt216Toz/fku6aLqicavtgHh0Kd7JMHiFrlCwAAMBRCCM7IIwEptUYWp5kdR4ZVJ6YZGVaQeWx1pO3l9XL4KT1VH3try1La5TnxJKjtKfbPtZ+mcF2J/t2etxAvSx0E6NkJ5ZmkvvTut3//rZlcvv+HHmO6XgAAACWEGFkB4SRAAtEaxb2gTwyhJ0cyh6rvSzJ8jwyBJ4qGJ5uOTFHAuj2EHrlHH3K8ST3JtmZ1kRM7evJfa3JmBrNQ3P03gAAAPNCGNkBYSQAx/TIUbLtQeVQkpPq5eS27cnticcBdKpKK5i8K61w8q5J2xPrbWk0HzqO8wMAAMyaMLIDwkgAuqIxtCLJqUlOS2sipvb15O0z0ppdvpNb1+9NsuUoyzbPvAQAAOaDMLIDwkgAFqTWJEynpTVD/Ia29YZJfRuTrJrBGaskO/LokPL2JLfVyy4zlwMAAJ0SRnZAGAnAotZ61uZJSc5Jcna9nmqZyXMvd+dIMDl5GUuj+eAcVw8AACwBwsgOCCMBWPJageVpmTqkHE5yflqjLI/l7jwyoLwlyTfq5U6jKgEAoD8JIzsgjASAJI2hVUnOSyuYnGo56RhneChHgslvJLm5bXtbGs3xeakbAADoOWFkB4SRADADjaGT8shw8oJ6uSit0ZWDRzl6Xx45irI9sNyaRvPwfJUNAADMP2FkB4SRADBLjaHlaY2qvLBeLmrbviDJsqMcfSDJrWkFk5OXrUZUAgDAwieM7IAwEgDmUWNoWVrPpmwPKCeWxyRZcZSj9+XICMr25aYk2z2jEgAAFgZhZAeEkQDQI42hwbRmAJ8IKi+uty9Ka0Tl8qMc/UAeHVTeVK93CioBAKB7hJEdEEYCwALUGlF5bh4ZUE4s5ycZOMrRzUwdUt6cRnPXPFYNAAB9SRjZAWEkACwyjaEVaQWSk0PKi9O6Jbwc5eh7M1VI2Qoq98xj1QAAsGQJIzsgjASAJaQxdEJaz6KcHFJelGTjMY7ekckBZWv5RhrNvfNVMgAALHbCyA4IIwGgTzSGVufIbN+Tb/8+4xhHb83UM37fkkZz/3yVDAAAi4EwsgPCSAAgjaGhPHoSnYnllKMcOZ7kjkw94/dYGs2D81g1AAAsCMLIDggjAYCjagydmkcHlBOjK9ce5cjDSW7LxK3ere0jS6Pp9w4AAJYEYWQHhJEAwHFpDJW0bu+e6vmUFyZZdYwz7MqRcHIsjwwrb/ecSgAAFgthZAeEkQDAnGsFlRtzJKS8MMlwWrOAn5/ktBmcZUceHVSO1cvWNJoPzW3RAABwfISRHRBGAgBd1xhak0eGk+3b5ydZN4Oz7ExrYp0tk5aJvm0m1wEAoBuEkR0QRgIAC05j6ORMHVKen+TcJKtneKYdeWRAuTXJXfWyvV7fl0azv38hBABgVoSRHRBGAgCLSusW8JOSnJPk7Hrdvkz0nTDDMx5IK5icCCcnh5UTy91mBwcAYCrCyA4IIwGAJacVWJ6aRweUZyXZ0Lac0uGZ709yT73snMH2XqMuAQCWPmFkB4SRAEDfagytTHJmHhlQti/r6/WZSQaP4x325UhAuSutMLN9PX2f510CACwawsgOCCMBAI6hMTSY1kjL0+rl9Ho5bdK6fXvlLN/1oRwJKPfMYtmbZL8RmgAA80cY2QFhJADAHGvdJr46RwLKU5OcnNazLk8+xvbQPFRUpRVu7m1bd7r9UJL9aT1jc/9Rtif3HRKEsqS1ft5LkoFJy1R9nfZ3eo5OdfKz2em+423L5PZM+47nuENpNMc7qBVgTiz6MLKU8tNJXp/WrUE3JPkvVVV9bpp9X53kh5I8vu66Lskbp9t/iuOFkQAAC0VrFOa6HAkoT0qy9jiX2Y7OnAtVpgspp18OHuP1o+1zOFOHFdOFGJ0EHu39s/lLRJnlsQPHse7FvnMVyM1VUDdf52DhqdL6Lmj/Xjg0Td98vX4gre+pg/O4fdg/9sDCsajDyFLKy5O8O8lrknw2yc8keVmSx1ZVdfcU+78nyb8n+VRazyX6xST/KcnjqqraNoP3E0YCACxFjaHlSU5MsqptPZPt6fpW1suKY2zPJmyDfjAXgfhU/Z385baTn9NO952P0aK+V6Z2rMByqpHs3Wk3mofn84PDQrPYw8jPJvl8VVX/uW4PJNmS5H9WVbV5BscPpvVsof9cVdW7Z7C/MBIAgLnRumV1WY4dWA7W+023LD/G69MdM5cj4Y6172z+IjGbY6capXm0dbf3nRiV1o0Rqr0+d+fnMJLt+By5Hf5oP5fL0vpuGWzbXjZp+2h9ne4/Xd/E99GKej1X28czkVovHc7Mw8upln0z2KeTfQ/4+WM+LdowspSyIq1n8ry0qqq/a+t/V5KTqqp6yQzOsTbJ3UleVlXVP0zx+sQvghPWJtkaYSQAAAAsTI2hgRwJKGcSXk73j0JTtWeyz0zaC91Mw81OwtDjDU2Fo0tMJ2Hksu6UNGOnpfWvHTsm9e9IcskMz/FbSe5M8tFpXn9Dkv9+XNUBAAAA3deamGciyFp4WqNXl+f4w8yVSU6Yom+6ZSb7Lp9U5eTBWb3VGJpuhOh8B6FTL8LRrlloYeSslFJGknxfkquqqto3zW5vTvK2tvbEyEgAAACAzrWCrAP1sqfH1bS0RpPONNzsJAw93tB0cji6ol7WzvEnPz6NoYOZ+9vj9+eRzzPdl0bzQ137TAvUQgsjd6b1XIUzJ/WfmWT70Q4spfx8kpEkz6uq6ovT7VdV1SP+JaUUzwEGAAAAlpjWaNKH6qX3WuHoVCNC52NU6Ez2m3xr/cSt/vMZjj6U1sR4fW1BhZFVVR0opVyX5LlJ/i55eAKb5yb5g+mOK6X8QpJfSvLCqqqu7UKpAAAAAMxUKxzdVy+917q1fkXmJwid2Lf9GabL0xoh2fcWVBhZe1uSd5VSrk3yuSQ/k2R1knckSSnl3Um2VVX1hrr9i0l+Ncn3Jxkrpayvz/NAVVUPdLl2AAAAABa61q31C/c5pEvYggsjq6p6Xynl9LQCxvVJrk/yoqqqJia1OTfJeNshr00rYb5m0qnelKQxr8UCAAAAADNWqqq/JwvqZOpxAAAAAOCROsnXBrpTEgAAAADQ74SRAAAAAEBXCCMBAAAAgK4QRgIAAAAAXSGMBAAAAAC6QhgJAAAAAHSFMBIAAAAA6AphJAAAAADQFcJIAAAAAKArhJEAAAAAQFcIIwEAAACArhBGAgAAAABdIYwEAAAAALpCGAkAAAAAdIUwEgAAAADoCmEkAAAAANAVwkgAAAAAoCuEkQAAAABAVwgjAQAAAICuEEYCAAAAAF0hjAQAAAAAukIYCQAAAAB0hTASAAAAAOiKZb0uYAFZW0rpdQ0AAAAAsNisnemOwsgjf1hbe1oFAAAAACxua5PsPtoOpaqqLtWyMJXWcMiNSfb0upZ5tDatsPXsLO3PCYnrnf7jmqefuN7pN655+onrnX6zFK/5tUnurI4RNvb9yMj6D2hbr+uYT223n++pquqo6TQsdq53+o1rnn7ieqffuObpJ653+s0SveZn9DlMYAMAAAAAdIUwEgAAAADoCmFkf9if5E31GpY61zv9xjVPP3G9029c8/QT1zv9pm+v+b6fwAYAAAAA6A4jIwEAAACArhBGAgAAAABdIYwEAAAAALpCGAkAAAAAdIUwcgkrpawspfxWKeXOUspDpZTPllKe3+u6YDZKKVeVUqpplqdP2veZpZR/K6XsLaVsL6X8fillTa9qh2MppawppbyplPLhUsp99XX9w9Pse2m93wP1vn9eSjl9iv0GSim/UEq5rZSyr5TyxVLKK+b9w8AxzPR6L6W8c5rv/K9Psa/rnQWplPLUUsoflFK+Ukp5sJRyRynl/aWUi6fY1/c7i95Mr3nf8SwFpZTHlVL+upRya/13z52llE+UUr5zin19xydZ1usCmFfvTPLSJL+X5OYkP5zkH0sp31ZV1b/1riyYE7+f5POT+r4xsVFKeVKSjyX5WpLXJTk7yc8nuSjJt3enROjYaUl+JckdSW5IctVUO5VSzk7yiSTNJG9Msiat6/uyUsqVVVUdaNv9N5KMJPnfaf3MvCTJe0spVVVVfzVPnwNmYkbXe21/kh+f1NecYj/XOwvVLyb55iR/neSLSdYn+c9J/qOU8vSqqr6c+H5nSZnRNV/zHc9id16StUneleTOJKuSfE+SD5ZSfrKqqj9JfMe3K1VV9boG5kEp5cokn03y+qqq3lr3nZDky0nurqrqmb2sD45XKeWqJP+S5GVVVV1zlP3+McmTklxSVdXuuu/H0/oyf2FVVf8878VCh0opK5OcXFXV9lLKU9L6xeNHqqp656T9/lda/8B0SVVVd9R9z0vyf5O0/8JzVpLbkvxJVVX/ue4rST6e5Pwkw1VVHe7GZ4PJOrje35nkpVVVHXVku+udhayU8swk17b/RbOUclGSLyW5pqqqV9Z9vt9ZEjq45t8Z3/EsQaWUwSTXJTmhqqpL6j7f8TW3aS9dL01yOMmfTHRUVbUvyZ8leUYp5ZxeFQZzpZSytpTyqBHepZR1SZ6f5C8mgsjau5M8kOR7u1QidKSqqv1VVW2fwa7fk+QfJn6JqY/9aJKb8sjr+yVJlif5X237VUn+MK3Rws+Yi7rheHRwvSdp/VJff79Px/XOglVV1acmjXhJVVU3J/lKkkvbun2/syR0cM0n8R3P0lOHhVuSnNTW7Tu+Joxcup6c5KZJQUySfK5eP6m75cCce0eS3Un2lVL+pR5VM+GytB5DcW37AfUvRNen9fMBi1L9L6VnZNL1XftcHnl9PznJg2k9rmDyfomfBRaPVWl95zfr5yu9vTz6GcCudxaVepTLmUl21m3f7yxpk6/5Nr7jWRJKKatLKaeVUh5TSvnZtB4P9rH6Nd/xbTwzcunakOSuKfon+jZ2sRaYSweS/E2Sf0zrF5lvSus5G58spTyzqqovpHX9J9P/DDyrG4XCPDnW9X1KKWVlVVX76313VI9+Jov/F7CY3JXkt5P8R1r/kP6iJD+V5ImllKuqqjpU7+d6Z7H5gSRnpfXs1MT3O0vf5Gs+8R3P0vI7SX6y3h5P8oG0npWa+I5/BGHk0nViWg8Cnmxf2+uw6FRV9akkn2rr+mAp5Zq0Hoz95rR+gZm4vqf7GXD9s5gd6/qe2Gd//L+AJaCqqjdM6vqrUspNaT3Y/aVJJh7i7npn0SilXJLk7Uk+ndaEB4nvd5awaa553/EsNb+X5Jq0wsLvTTKYZEX9mu/4Nm7TXroeSrJyiv4T2l6HJaGqqm8k+fsk31Y/KHji+p7uZ8D1z2J2rOu7fR//L2Cp+t20Rhw8r63P9c6iUEpZn2Q0rdlUX9o2CYHvd5ako1zz0/Edz6JUVdXXq6r6aFVV766q6sVpzZb9ofoRBb7j2wgjl667cmQYcLuJvju7WAt0w5a0/tVpdY4MX5/uZ8D1z2J2rOv7vvr2jol919e/AE3eL/GzwCJVVdVDSe5Nckpbt+udBa+UMpTkn9Ka0OBFVVW1X5e+31lyjnHNT8l3PEvINUmemuTi+I5/BGHk0nV9kounmJHsaW2vw1JyQVrD1h9I8uUkh5K0T2qTUsqKtCZvur7LtcGcqapqW5J7Mun6rl2ZR17f16f1UPjJs1b6fwGLWillbZLT0vpZmHB9XO8sYKWUE5J8KK2/lL64qqqvtr/u+52l5ljX/FGO8x3PUjFxO/WQ7/hHEkYuXdek9XyCn5joKKWsTPIjST5bVdWWXhUGs1FKOX2KvicmuTrJP1dVNV5VVTPJR5O8sv5lZsIPpjVU/q+7UizMn79J8uJSyjkTHaWU56b1y3779f33SQ6m9SD4if1Kktck2ZZHPn8VFpxSygmTvscn/LckJcmH2/pc7yxY9WNk3pfkGUleVlXVp6fZ1fc7S8JMrnnf8SwVpZQzpuhbnuSH0rqleiKI9x1fM4HNElVV1WdLKX+d5M31D8Y3krwqyXCSH+tlbTBL7yulPJTWF/Ddac2m/RNJ9iYZadvvl+p9Pl5K+ZMkZyf5ubQCyw8HFqhSyn9O61amiVnyvrOUcna9/T/rsP03k7wsyb+UUv5HWiH765N8Kck7Js5VVdXWUsrvJXl9/QvR55N8V1ozyv/ADJ7ZBPPqWNd7kpOTfKGU8pdJvl73vzDJd6T1l9S/nziX650F7nfS+ofTD6U1Y+or21+squov6k3f7ywVM7nm18d3PEvDH9d3pX4irbBwfVqzx1+S5Oeqqnqg3s93fK08eqZwlop6WPyvJXllWr/MfzHJf6uq6iM9LQxmoZTy/6X1xX5hknVpDXX/WJI31RPZtO/7LUl+K8nlSfYkeX+SN1RVtaerRUMHSiljSc6b5uXzq6oaq/d7XJK3JfmWJAfSejD8z1VVtWPS+QaS/GKSn0zrOTM3J3lzVVXvmY/6oRPHut6T3J9WKPn0tALLwbT+gfU9Sd5aVdXBSedzvbMglVL+Ncmzp3u9qqrStq/vdxa9mVzzpZST4jueJaCU8n1pDfq6LMmpaf3d87q0BhJ8cNK+vuMjjAQAAAAAusQzIwEAAACArhBGAgAAAABdIYwEAAAAALpCGAkAAAAAdIUwEgAAAADoCmEkAAAAANAVwkgAAAAAoCuEkQAAAABAVwgjAQCYM6WUd5ZSxnpdR7eUUn64lFKVUp7S61oAABYDYSQAQB+oA7OZLFf1ulYAAJauZb0uAACArvjBSe0fSvL8Kfq/Nsv3eXX8gzcAANMQRgIA9IGqqv6ivV1KeXqS50/un6yUsqqqqr0dvM/B4ywRAIA+4F+tAQBIkpRS/rWU8uVSyhWllE+UUvYm+c36tZeUUkZLKXeWUvaXUm4ppfy3UsrgpHM84pmRpZTh+vbvny+l/ER93P5SyudLKU+dYV0nlVJ+r5SypT72G6WUXyylDLTt0/4+P1tKub2U8lAp5eOllMdPcc7nlFI+WUp5sJRyfynl70spl06x31mllD9r+9y3lVL+sJSyYtKuK0spbyul3FOf829LKafP5PMBAPQTIyMBAGh3apJ/SvJXSf4iyY66/4eTPJDkbfX6OUl+Ncm6JK+fwXm/P8naJH+cpEryC0k+UEq54GijKUspq5J8PMlZ9bF3JHlmkjcn2ZDkZyYd8kP1+7w9yQlJ/muS/1dKuayqqh31OZ9Xf8ZbkzSSnJjkvyT591LK5VVVjdX7bUzyuSQnJfmTJF+v63hpklVJDrS97/9MsivJm5IM13X9QZKXz+DPBgCgbwgjAQBotz7Ja6qq+uNJ/d9fVdVDbe0/KqX8UZKfKqX8clVV+49x3nOTXFRV1a4kKaXcmOTvk7wwyT8c5bjXJXlMkidXVXVz3ffHpZQ7k7y+lPI7VVVtadv/wvp9ttXv8+Ekn03yi/W5kuQtSe5L8oyqqu6r9/u7JF9IK0x8Vb3fm+s/j6dVVXVt23v8SimlTKrz3iQvqKqqqs83kOT/K6UMVVXVPPofDQBA/3CbNgAA7fYnecfkzvYgspSytpRyWpJPpjVC8JIZnPd9E0Fk7ZP1+oJjHPeyet9dpZTTJpYkH00ymORbJ+3/dxNBZF3359IKI7+jrn1DkicleedEEFnv98Uk/7dtv4Ek35XkQ5OCyIn9q0ldfzKp75N1fecd4/MBAPQVIyMBAGi3raqqA5M7SymPS/Lrad2evW7Sy0MzOO8d7Y2qqnbVgwtPPsZxFyV5QpJ7pnn9jEntm6fY56Yk31tvT4SDN06x39eSvLCUsjrJmrQ+55ePUd+EOya1J4LXY30+AIC+IowEAKDdQ5M7SiknpfXcxt1JfiXJLUn2Jbk8yW9lZnfbHJ6mf/LtzpMNpDVi8benef2mGbx3Nxzv5wMA6CvCSAAAjuWqtCa2+e6qqj4x0VlKOb8L731LkjVVVX10hvtfNEXfxUnG6u3b6/Vjp9jvkiQ7q6p6sJTyUFrh66Nm4gYA4Ph5ZiQAAMcyMerv4VF+pZQVSX6qC+/9/iTPKKW8cPILpZSTSimT/3H9u0opZ7Xtc2WSp6U1e3aqqroryfVJXlWP+JzY7/FJXpDkH+v9xpP8XZLvLKU8ZYr3NuIRAOA4GBkJAMCxfCqtZyC+q5Ty+0mqJD+Y7tyC/JYkVyf5h1LKO5Ncl2R1ksuSvDTJcJKdbft/I8m/lVL+MMnKJD+T1kzX7bd5vz6tcPLTpZQ/S3Jikv+SpJmk0bbfG9MKKD9eSvmTtJ4puSGtSXW+Jcn9c/UhAQD6hTASAICjqqrq3lLKi5P8TlqT2OxK8hdJPpbkI/P83ntLKc9OKxh8WZIfSuv26ZuS/Pe0AsR2704ynlYIeUaSzyX5z/WIyIlzfrSU8qIkb0ryq0kOpvVMzF+squq2tv22lVKeluTXkvxAWhPabEsryNw75x8WAKAPlKqqel0DAADMSillOMltSV5fVdVbe1wOAADT8MxIAAAAAKArhJEAAAAAQFcIIwEAAACArvDMSAAAAACgK4yMBAAAAAC6QhgJAAAAAHSFMBIAAAAA6AphJAAAAADQFcJIAAAAAKArhJEAAAAAQFcIIwEAAACArhBGAgAAAABdIYwEAAAAALri/wdspDS8z4CapgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -373,7 +373,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -410,7 +410,7 @@ { "data": { "text/plain": [ - "0.943730275125624" + "0.9633110554163186" ] }, "execution_count": 13, @@ -524,7 +524,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -571,8 +571,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average influence of corrupted points: -1.0840776\n", - "Average influence of other points: 0.11192768\n" + "Average influence of corrupted points: -1.076019\n", + "Average influence of other points: 0.10683939\n" ] } ], @@ -638,7 +638,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -742,16 +742,16 @@ }, "outputs": [], "source": [ - "from pydvl.influence.torch.pre_conditioner import NystroemPreConditioner\n", + "from pydvl.influence.torch.preconditioner import NystroemPreconditioner\n", "\n", "nn_model.to(\"cpu\")\n", "cg_influence_model = CgInfluence(\n", " nn_model,\n", " F.cross_entropy,\n", - " hessian_regularization=0.1,\n", + " regularization=0.1,\n", " progress=True,\n", - " use_block_cg=True,\n", - " pre_conditioner=NystroemPreConditioner(rank=5),\n", + " solve_simultaneously=True,\n", + " preconditioner=NystroemPreconditioner(rank=5),\n", ")\n", "cg_influence_model = cg_influence_model.fit(training_data_loader)\n", "cg_train_influences = cg_influence_model.influences(\n", @@ -792,7 +792,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Percentage error of Cg over direct method:38.18922936916351 %\n" + "Percentage error of Cg over direct method:30.799928307533264 %\n" ] } ], @@ -818,7 +818,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABS4AAALGCAYAAABLZg+zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvsElEQVR4nOzdd5hcZ3k34N8jyQWwJeOKbGNkIPQOoSRxsOmJAwFjSCCEknyBQAolIZgkhBowkAQICb0YQscQmgCHZjAl9JYQig0ybrhgI7nKlvR+f5yzZjzelWZHW2a1931de43mnPec88zszKz2t2+p1loAAAAAACbJisUuAAAAAABgmOASAAAAAJg4gksAAAAAYOIILgEAAACAiSO4BAAAAAAmjuASAAAAAJg4gksAAAAAYOIILgEAAACAiSO4BAAAAAAmjuASAOZYVa2uqn+tqg1VtaWqWlXdoaqO7P/9nMWucVdTVferqi9W1S/65/gDi13TsKo6uaraHJznOf1jPHLnq1o6qmpd/7hPWOxaWDhVdUL/fV83sM1rYYFMws+t/vonL9b1AVhcgksAyNW/GO10qNR7SZK/SPLdJC9K8twkP5ujczOkDzQ+mOTwJG9K93y/awfHPLb/nj923gtk4vR/VNiw2HXMpKp2r6o/rqr1VXVOVW2uqour6ltV9fKqut1i18g1LdXPFCEwAJNu1WIXAAC7oN9J8sPW2gMHN1bVLRapnl3dfZLsmeSvWmvvWOxituPRSa47B+f5t3TB7E/n4FxLyVlJbplk42IXMp+q6mZJPpDusV6Q5BPpvte7J7lVkj9N8pdV9eDW2ocWq85FtixeCwCA4BIA5sPBST632EUsIwf3t2cvahU70Fqbk6CxtXZBukBrWWmtXZXk+4tdx3yqqoOSfCrJoUlenuRvW2uXD7U5MMmzk1x/wQucEMvhtQAAdAwVB4AZDA6h6//9rqq6oKquqKqvVdXvDLWfmsOwktxzavj5jubm2t6w1e3NZ1hVt+hrO6Oqrqyqc6vqHVV182naXj1PXFU9oaq+2z+Oc6vqdVW1ZobrH9rP1/mjqrq8qi6sqq9U1bNmaPtvVfXjfmjrz6vqQ1X1q9t7/DNc9+FV9bmq2thf97tV9cyq2mOgzZH98/3cftNnBp7zaz1fA8ednOTN/d03Dxxz9Tx6g897VT2yqr5cVZcMfp/6oaHv6x/v5VW1qaq+UFWPmum6w9MRDM4fV908qOurm6fzsqr6bFX92jTnmfY1MfVaq6r9++/p1BDj/62qx81Q0x79+aa+Zz+pqhf020eeV27ovXKLqvpA/1q5tKo+X1X32871j+u/v5f1z+EpVfXw7V1jaPvIr+2B18yNktxo6Ht/wkC7I6rqw1V1Zv+8/Kyq/ruqnj3K87ETXpAutHxna+2pw6FlkrTWzmut/VmGpkOoqpv1r8eL+uf9i1V1dM1iCHNVvaZv+7sz7L9bv//EgW0HVdU/VdUP+uv+ov/3CVV141EfeFXdp//eX9q/dj5QM/RSH+G1cOOq+ouq+k7/3jx5oM2+VfWiqvq/ft/GqvrUTK/R/pjf69tc2L+2NlTVO6vqLv3+k7ODz5TtnHvwvXOTqjqxus/Oi6vqv6rqNn27Awbe11dU1Ver6qgZzrmqqp7Uv2Y39e+tb1bVn1fVioF2z0nyk/7uY4bqfuw05x3pM6pvu6Z/nn/Q13tRVZ1UVfeZof3uVfWsqjqthj6Ltvf8AbDr0+MSAHbsRkm+kuTHSf4jyb5Jfi/JB6vqPq21z/TtTkhycrreUKf395Nkw1wXVFUPSPL+JLsl+XCSU9MFHsckObqqjmqtfWOaQ1+S5P79Mf+V5Kgkf5LkpknuNXSNuyQ5Kd3j/Vx/veumG676nCTPH2h7p/58+/bHvD/J/kkenOTzVfWQ1tpHR3xsL0zyzHS9Ct+R5JIkv5XkhUnuX1X3a61dme55fW6SI5PcM8lb8svnekNmdkKSXyT53XRzY35rYN8vhtr+VZL7pnu+PpNkMOB9dZL/TffcnJNkvyS/neQ/qurmrbVrhbvbcZckf5PkS0nekOSwJA9N8qmqukNr7QcjnmefJF9IcmWSE5PskeRhSd5UVdtaa2+ZalhVleR9SY5O8qN0Q9B3S/LYJLeeRe2DDu8fw3eTvDbJ2nTvlY9V1SNba+8euP7u6V4r90zXe+7f072+jk3y7v5x/+0srj3Ka3tDutfMU/r7Lx84/lt9XQ9Isj7JpiQfSjcsed90Q5OflF8G5XOqqq6T5A/7uzu8Rmtt88Cxt0jyxXS9MNcn+U6SGyf5zyQjve96b0nyhHTTGnxwmv2P6W9P6K973XSvt5ukG9L+4XR/uLlRuvfXiek+N7erqo5N8u50r9t3p3s//Ua619J3ZlH/lFckOSLdc/HRJFv769wo3Wf0uiSnJPl4kuulm97j41X1hNba6wfqqnSB5GPSfR69P8n56T5rj0rygyRfy+w+U2ayLsmXk/xff751SR6S5OSqukdf66Z0z8++SX4/3fvqZoO9uatq6mfC/fv63pHkir7eVya5W375Ojs53WfGk5N8O90UBVMGH0Myi8+oqton3eviVkm+mu59tn+Shyf5r6p6YmvttQPtK8l70j1/p6X7LNo9yR8lue0OnjcAdnWtNV++fPny5WvZfyVp3Y/Fa2xbN7U9ybOH9t2/3/7RGc518jTbj+z3PWdo+4YkG2ao6zn9MUcObLt+kovS/SJ9q6H2t0kX9H1jaPsJ/Xl+muSwge2r0gVvLcldB7bvnq4nTkvyyGnqOnToHKem++X4nkPtDk4X/JyTZI8Rvg/3GKjzBkPX+HC/72939ByNcJ3H9sc8dgfP+6VJ7jhDm5tMs233dEN9r0pyyNC+k6d5jU29Jq5VS7oAqSV51SiPd+A8b0iycmD7rZJsSfK9ofZ/2Lf/XJLdB7bvky5InPZ1PMNzsW7g+i8d2neX/vm4KMnqge3P7Nt/NMmqge0HpntPtCS/Ns01TtiZ1/YI77n39cfcfpp9+4/6GpvtV7qgrSU5c4xjP9Uf+8Sh7b810+trO+f6QZLNSfYd2r5HkguTnDv1/UrywP7cL5vhvbD3CNfbK8nP+9fIXYb2vWyg/nWzeC2cleTwaa51cpJtSX5/aPs+6YK6y5McNLD98f35vpJkzdAxK5OsHbj/2Nk8zzO8d/5uaN+z+u0XJnlNkhUD+6bevy8bOuY5/fZX5pqfAyuTvLHf97s7ei4H9h8502soM39Gvbbf/tokNbD9V9LNS7p56Pv5yL79l5LsObB933RB5sifRb58+fLla9f7MlQcAHbs9HRDOK/WWjspXVBy10Wo59HpftF+dmvte0N1/U+S1ye5Y1Xdappjn9cGeue01rbkl0McBx/LA9P9QvuhNs2CN621MwfuHp2ux9UrW2ufHWp3drqecDdIcu8RHtsf9bcvaK1dvRJ7X+dfpQsd/t8I55krr2utfXO6Ha2106bZdmW6noOrMtrjnfKF1toJQ9velC5wnM1r7LIkT2utbR2o6Xvpej/dsqr2Gmj7mP727/u6p9r/IgO9aWdpY5LnDW5orX0tydvTvWYfMrDrj9IFEk/rv79T7c8buP5svtejvrZHNd0w7fmcW3Rtf3vmdlsNqaobputRemq6oOhqrbWPJfnkLOt4S7rQ8RFD2x+Y7o8mbx/8fvWme66ubK1dPML1fjddQPWO/rUy6DkZbwGel7TWfjK4oapun6537/taa9cYZt+/5p+dbpGvhw7s+ov+9gmttY1Dx2xtrZ0zRm0z2ZDk+KFtUz2k90jy9NbatoF970j3+XCHqQ39MPC/SPKzJE8d+hzYmu4ztCX5gzHqG+kzqu9J/ah0f0B7ZmutDdTwoyT/mu719eiB80xNZfG3rbUrBtpfmPE/iwDYRRgqDgA79q3BXwAHnJGuh+BCm7rm7fs5yobdrL+9ZZLvDe0bDgaS7nEk11zs4+797cdmUc+NZqjnVwbq2dGw1Tv1t58e3tFa+2FVnZnk8KpaMxwkzJOvzLSjqg5L8ox0AeVhSa4z1OSQWVznWt+X1tpVVXVuZrcIy49aa5um2T74Pb6k//cd0wXBX5ym/edncc1B35ghrDo5XVB6xyRvqaq90w3hPqu1Nt0iK1Pf/zvO4tqjvrZ35O3pplz4clW9O90UAV8YCutn1A+Tfco0u17eB2Rz7Q797ZeGgq0pn08y7byCM3hrurDoMelC+ClTQfcJA9s+m65343H9dBEfTReSz/SZOZ2p9/xnh3e01jZW1bfSBY6zMd37dupzas0Mn1MH9Le3TJKqul66HuznzvTHizk23XM2teDYD4ffV621rf3nw6EDm2+WLgT+UZK/70ZgX8vl6R/jLI36GXXzdFM+fKEPHod9Osnf55rv7Tul+yya7nPn5DFqBWAXIrgEgB37xQzbt2RxFrrbr7/9kx2022uabb+YZttU76mVA9v26W/PmkU9DxujnmFr+tuZejKdky4k3Cfj9cSarZ9Nt7G6RUe+ku4X9lPSzam4Md1ceuvShTyzWVTiFzNs35Jrfl925jwZOteaJBdO03su6YYDj2Om46aexzVDt9v7Pie/fB2O4hfTbJvucW9Xa+391S289VfpeoU+IUmq6uvpepB9Ygen2Cdd771hJ8xQ45SpxzybwDv55XM503M/q+9la+3MqvpUkvtW1S1ba/9X3UrmD0gXrn1noO2mqrp7ujk5H5RuCo0kuaCqXpWu5/RVO1n/tO/BHZjumKnPqfv2XzOZ+pzap78d5TNwLlzr86y1tqUPH2f6rNuSbl7aKVOP8Vcy/WtwyiifxcN+sZ0ahj9Xktm9t6c+i6Z7rYzz/QdgFyK4BIDFty3d0Lnp7DPNtqlfYm8/GCLMsV/0t6OEKFP1/G5r7UM7ed2pc90g3dxmw9YOtZtvbYbtT0sXEjxuePhkVT0iv+ydNsk2Jdm3qlZNE14eNOY5ZzruBv3txqHbG0zTNln47/M1tNbWJ1nf97q7W7rFW56Y5CNVdcfhKRqGjt2QboGa2fpaurn/Du0XXPnhiMdN9bCd6bkf53v5lnTh3mOSHJduaPGq/HLo8tX6nqh/3C+wcqt0w9b/LMk/pPvDzo4WqZr6Hu/otTMb071vp67z5Nbav45wjl/0t7MNkhfT1GP8z9baMYtcw2ze2xvTfRbtNk14Oc73H4BdiDkuAWDxXZTkoH412GF3mWbbf/e3R8xfSVdf47dm0XYu6pkaknnk8I6qumm6YZE/mYMht1NDMmfTm3HQTfvb902zb7bDWhfLN9P9X/DXptn3G2Oe8079MPBhRw5cM/2w19OSHFJVvzJN+6P622+MWceObM0I3/vW2qWttU+31p6WblX73TPae2LWWmuXJ/mP/u4/7Kh9VU316P1Wf3uPfo7DYeN8L9+fLhB9VH/Ox6TrWXet+W6ntM7/ttZemV/2aHzwCNea+h5f631TVWsyMIfjTprV51Rr7dIk/5Pus3mUKQt29jNlLnw/XeB69xl+nkxnruv+Qbq5dm/fT5swbLr39jfSfRZN91o9co7qAmCJElwCwOL7SrreTI8b3FhVj03y69O0f3O6X06fXVXXWnSkqlZU1ZE7WdOH0y0W8aC+B+HwNQbnVftguhDqz6rqt6c7WVXdo6quO8J139Tf/n1VTc05l6pameSf0v3f5Y0jPYLt+3l/e9iYx2/ob48c3FhV98/CLh60M97a376gX1AjydVh0Y56yc1kTYZCt6q6S7oeexuT/OfArjel65n40v77O9V+/4Hrvynz4+dJDqiq4XlJU1W/WVXTjUqa6hF42TzVlHRz/52Z5A+q6qUz1Ld/Vf1rkt9Pkn5BopPThelPGGr7gMxufsv057w8yXvS9TZ8apLbJ/lov3DS4PlvXVXT9ZSczXP1wXR/vHlk/1oZ9Jz8cujxTukX/jklyTFV9UfTtamq2/bD4qdM9cx8bf++GGy7oqrWDmza2c+Undb3nH5lul6N/zrD62ft0MJtF6XroTondfcLfb09yd4ZWlinqm6S5C/TrSD/HwO7phbR+seq2nOg/b7p3hMALGOGigPA4ntlutDy1VV173QLitwh3WISH0k3TPVqrbWfV9Wx6UKg/+7no/vfdL983rA/br90K+SOpbV2ZVU9LN3cje+oqiek67G0Z7qFHe6d/v8R/QINxyQ5Kd3w2i+m6wV2WV/Prya5cbpfprcbZLTWvlhVL0nyN0n+p6pOTHJpul5ut0m3eMNLx31cA77U1/KUqtovv5xH7ZUjLvrzqnTfs/f2NZ7d1/eAdIHP781BjfPtrenCrweke64/lG6+vIcm+Wq6RTamW+xlez6X5P9V1d3SLdKyNt1zsSLdysyDCwf9U7rv6+8m+XZVfTTdoh4PS3JgupWhx10kaEc+le51+fGq+ly6Idrfbq19OF1YdUhVfSFdQH1lkjunGwJ9epJ3TXvGOdBaO7f/DPhAkr9O8piq+kSSn6br7XnLdGH5Hrlmb8Y/S/d8v6r/48F30r3nHpouGPzdzP57+ZZ0IfyLBu4Pu2+64PlLSX6Y5Lx0vaKnrrfD92pr7ZKqenySdyc5pV8Q6Zx0ve9uk+419ZuzrH0mj0y3OMwbq+ovk3w53R+BDk1yu/569+gfR5K8IV0PzT9M8qOq+mCS85McnO718KZ04Wqy858pc+X56YLmP03ywKr6dLp5Og9MN/flryf5u/QLt/XP/5eTHFFVb0/3fdya5EM7MRXJcemetz+vql9Nt8DV/kkeni7Q/POhVd/fme5z4kHpPos+mO6z6Nh0n0U3GbMOAHYBgksAWGStte9V1X3SDUV9YLohmaek+wX6mAwFl/0xn6qq26ULN+6f7pfEK9MFaJ/O9EOYZ1vX16rqDul+Cf2tdEOKL05yaoZ61bXWvlNVt0839+PvpAv1tqULIL6ZbqGIC0a87jOq6ptJ/jzJo9P9Antaup43/9z36NnZx3ZRVT20r+uxSa7X73pbRphXsX+8RyV5QZKj0/2f6tvpvl+/yBIILltrraoekuRv0wUzf5Hu+/WWdMHsg/PL+RNH9ZN0gcnx/e0e6YaBPq+1dtLQ9a+sqvume808sr/+lnTP41Naa+8c75GN5AXp5o99YLogZ2W6x/3hdO/Dh6SbpuE+6V7HP+23v7y1dtE81pXW2g/7990fpgse75XuDxGb0wWpb0jy+tbadweO+V5V3aOv8V7913f6x3HLdEHirL6XrbXPV9Wp6XpyXpjujyjDTkrXU+83+2usTvca+kSSf2mtTbdi/XTXOrHvHfrsdOHW5nSB5T3Sff7MSXDZLzx053SvtYem6wm8Ml3I+L10f0QafF5bkkdX1UlJHt/Xtkf/GE9J8qGBtjv1mTJX+j8kPTjJo/o6fifdYjznp3t/Pitdj8hBf5jkZen+iPGIdD2hz0z3Ghqnhgv71+Mz030mPi3dauZfSfLS1tp/DbVv/R/Kjutr/vN0z/GbkzwvyRXj1AHArqG6n8cAANDpA8X/SnJ8a+2ZI7Rfly4UeUtr7bHzWx2z0feie2SSW7TWfrDY9QAAzIY5LgEAlqmqOniabful6zGZXHNOSiZUP9/itVZf7oed/16S7wktAYClyFBxAIDl61/6If5fTDeU9NB00wLsm+S1rbWvLGZxjGz3JGdU1WfSrSy9Jcmt081BeWW6OTABAJYcwSUAwPL1/nQrQD8w3ZyPV6Rb6OmNmZvV21kYVyV5Tbq5Le+WbpGjC5K8N91w/28uYm0AAGMzxyUAAAAAMHHMcQkAAAAATBzBJQAAAAAwccxxOUtVVUkOTnLxYtcCAAAAAEvU3knObtuZx1JwOXsHJzlzsYsAAAAAgCXu0CRnzbRTcDl7Uz0tD41elwAAAAAwW3un6xi43WxNcDm+i1trmxa7CAAAAABYSrqZGHfM4jwAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxVi12AQAAAABAZ91x61cmOSLJ2iTnJDllw/FHb13cqhaHHpcAAAAAMAHWHbf+mCQbknwmyTv62w399mVHcAkAAAAAi6wPJ09McsjQrkOSnLgcw0vBJQAAAAAson54+Cv6uzW0e+r+y/t2y4bgEgAAAAAW1xFJDs21Q8spleSGfbtlQ3AJAAAAAItr7Ry32yUILgEAAABgcZ0zx+12CasWuwAAAAAAWA76OSqPSNdz8pwkp2w4/uitSU5Jcma6hXimGy7e+v2nLFCpE0GPSwAAAACYZ/2q4BuSfCbJO/rbDeuOW39MH14+uW/ahg6duv+Uvt2yIbgEAAAAgHnUh5YnputROeiQJCf24eX7kxyb5KyhNmcmObbfv6xUa8MhLttTVauTbEyyprW2abHrAQAAAGBy9cPDN2THw8AP33D80Vu3M5x8lzFqvmaOSwAAAACYP0ckOXQ7+yvJDft2J/ch5ckLUNfEM1QcAAAAAObP2jlut2wILgEAAABg/pwzx+2WDcElAAAAAMyfU9LNYTnTQjMtyRl9OwYILgEAAABgnvRzVj65vzscXk7df8qutgDPXBBcAgAAAMA82nD80e9PcmySs4Z2nZnk2H4/Q6q1mXqpMp1Rl2sHAAAAgEHrjlu/Mt3q4WvTzWl5ynLsaTlqvia4nCXBJQAAAACMb9R8zVBxAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDirFrsAgAAAABYOOuOW78yyRFJ1iY5J8kpG44/euviVgXXpsclAAAAwDKx7rj1xyTZkOQzSd7R327ot8NEEVwCAAAALAN9OHlikkOGdh2S5EThJZNGcAkAAACwi+uHh7+iv1tDu6fuv7xvBxNBcAkAAACw6zsiyaG5dmg5pZLcsG8HE0FwCQAAALDrWzvH7WDeCS4BAAAAdn3nzHE7mHeCSwAAAIBd3ylJzkzSZtjfkpzRt4OJILgEAAAA2MVtOP7orUme3N8dDi+n7j+lbwcTQXAJAAAAsAxsOP7o9yc5NslZQ7vOTHJsvx8mRrU2Uw9hplNVq5NsTLKmtbZpsesBAAAAmI11x61fmW718LXp5rQ8RU9LFtKo+ZrgcpYElwAAAAAwvlHzNUPFAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDhLPrisqj+rqg1VdUVVfbmq7rqdto+tqjb0dcVC1gsAAAAA7NiSDi6r6veS/EuS5ya5U5JvJzmpqg7czmGbkqwd+LrRfNcJAAAAAMzOkg4ukzwtyetba29urX0vyZ8muSzJH23nmNZa+9nA17kLUikAAAAAMLIlG1xW1e5J7pzkk1PbWmvb+vv32M6he1XV6VV1RlV9sKpuvYPr7FFVq6e+kuw9F/UDAAAAADNbssFlkv2TrEwy3GPy3CQ3mOGYH6Trjfm7SR6V7vF/saoO3c51nplk48DXmTtRMwAAAAAwgqUcXM5aa+1LrbW3tta+1Vr7bJJjkpyf5AnbOexFSdYMfG0v5AQAAAAA5sCqxS5gJ1yQZGuSg4a2H5TkZ6OcoLV2VVV9M8lNt9Nmc5LNU/eravaVAgAAAACzsmR7XLbWrkzy9ST3ntpWVSv6+18a5RxVtTLJbZOcMx81AgAAAADjWco9LpPkX5K8paq+luQrSZ6S5HpJ3pwkVfXWJGe11p7Z3/+HJP+d5NQk+yR5epIbJXnDQhcOAAAAAMxsSQeXrbV3V9UBSZ6XbkGebyV5QGttasGew5JsGzjk+kle37e9KF2PzV9rrX1vwYoGAAAAAHaoWmuLXcOSUlWr060uvqa1tmmx6wEAAACApWTUfG3JznEJAAAAAOy6BJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMRZtdgFAAAAAPNj3XHrVyY5IsnaJOckOWXD8UdvXdyqAEajxyUAAADsgtYdt/6YJBuSfCbJO/rbDf12gIknuAQAAIBdTB9OnpjkkKFdhyQ5UXgJLAXVWlvsGpaUqlqdZGOSNa21TYtdDwAAAAzqh4dvSBdS1jRNWpIzkxw+zrBxw8+BnTVqvqbHJQAAAOxa7pnk0EwfWqbffsN04eOsGH4OLCTBJQAAAOwi+gDxvSM2XzvGuQ0/BxaM4BIAAAB2AQPB4vVHPOScWZx7ZZJX9HeHe3JO3X953w5gTgguAQAAYInbQbA4rCU5I8kps7jEEZmn4ecAM1m12AUAAAAAO20qWNyRqRV6nzLLBXVGHVY+q+HnANujxyUAAAAsfaMGhhcmOXbD8Ue/f5bnH3VY+cjDzwF2RHAJAAAAS9+ogeHDxwgtk25Y+Zn5ZY/NYeMMPwfYLsElAAAALH2jBoufHefk/bDyJw+ca/jcyeyHnwNsl+ASAAAAlrg+MHxqukVy5iVY7HtqHpvkrKFdZ2a84ecA21WtzfTHGKZTVauTbEyyprW2abHrAQAAYHT96ttHpJsT8pwkp+wKvQTXHbf+mHSrik+3QM8Z6ULLOQkWd9XnEFg4o+ZrgstZElwCAAAsTTOEe2cmefJS7i3YP64T+7s1sGvqF/6Hbzj+6BMDMCEEl/NEcAkAALD0jBDuLcmhzn3vxw1JDsk1H9eUli6cPVyvSGBSjJqvmeMSAACAXVof7r2ivzsc7k3df3nfbqk5Il0P0ulCy/Tbb9i3A1hSBJcAAADs6nblcG/tHLcDmBirFrsAAAAAmGdLMtwbcRGcc0Y83ajtACaG4BIAAIBd3cSEe6OuyD3TQkLrjls/vJDQKenmsNzRHJenzM0jAFg4hooDAACwq5sK92ZanbYlOSPzHO71YeSGJJ9J8o7+dkO/fbjdienCyEGHJDlxsH0fej65vzv8+KbuP8XCPMBSJLgEAABglzYJ4d6oYeQ4Cwn1PTCPTXLWUPszs0RXSwdIkmptpj84MZ1Rl2sHAABgssww/PqMdKHlvIV7fci4ITsezn14umHknxnhtEdtOP7ok6e5zg6HoQMstlHzNcHlLAkuAQAAlq75DvemO39mEUb2x71jhLaP3HD80e8ct06AxTRqvmZxHgAAAJaNPqQ8eT7OPdOCOkneO+IppsLOUVglHNjlCS4BAABY1uaiF+bAHJbDDknylBFPM9VD0yrhALE4DwAAAMvYqCt97+AcoyyoszUjrGo+CQsJAUwKwSUAAADL0qgrfY/giHTDw6frIZl++8r+dodhpFXCAToW55kli/MAAAAsPdMMB/9Ckh9nhJW+d9S7cd1x6x+R0RbUeVmSh2XEVc2tEg7sqizOAwAAAJlx0ZzzkxywncMqyQ3TBYcn7+ASoy6U86EkT8+IYeR8LiQEsBQILgEAANhlbWfRnP1HPMXaEdqMvKCOMBJgdIJLAAAAlqQdDaUecdGcHdlhb8oNxx+9dd1x65+cLiBtQ+e2oA7AmCzOAwAAwJIz4mrgO1o0Z3uuXul7lMYW1AGYexbnmSWL8wAAACyuoeHf0/VuPHbD8Ue/fxaL5szUS/LqwHHUhXIsqAOwY6Pma4LLWRJcAgAALJ4+GNyQEVYDTxcgfmaE0w4v1HONlb5nWNznzCRP1pMSYPZGzdcMFQcAAGAp2dHw78HVwKcWzZmpx87UcPBDkhyV5JH97eFDoeWJfZtBhyQ5cWhoOgBzSHAJAADAktD3trzXiM0P7odoP7m/PxxeDi6ac9WG448+Ocl7+m0PX3fc+iPXHbd+t+x4cZ+X93UBMMesKg4AAMDEGpgz8kFJHpVrDunenn9fd9z6Kzccf/SJ645bf2ymH+q9o+Hgw0PIhw327jx5xLoAGJEelwAAAEykoZXDn5rRQ8sk2SfJe9cdt/7FfTi5LrMfDr7/iNdaO4u6ABiRHpcAAABMnKGVw3fG36w7bv1XNxx/9ImZpldk36NzR8PBd+Sc8csDYCZ6XAIAADBRdhAmjuPftzMP5Y4W+9meqcV9Thm3MABmJrgEAABg0uxMmDidA/tzTmfUYd7bW9xn61hVAbBdgksAAAAmzXzMGTnTOUcd5n3B0P0zkxw7NU8mAHPPHJcAAABMmpvOwzlnCihPSRdCHpLpe3i2fv9Nkvx6ugD0nCSn6GkJML8ElwAAAMyJfh7JI7IT4V5/jsfPYVlTweO081BuOP7oreuOW//kdAsBtVwzvBwcDn5VplncB4D5Y6g4AAAAO61fBXxDks8keUd/u6HfPhtT81vOxjtn2D7SPJT9cO9jk5w1tMtwcIBFJLgEAABgp/Th5InphlsPOiTJieuOW/+s7azqPWyc+S0/nORhSc4f2j5y8Ni3WZfkqCSP7G8PF1oCLJ5qbXhhNLanqlYn2ZhkTWtt02LXAwAAsJj6QHJDZp4jcsqZSZ68oyBw3XHrj0zXW3M2jtpw/NEnz8VQdQDm36j5mjkuAQAA2Bn3zGhDuw9N8r51x63/hyQv3E6guKPFcgZdY/7K/pwnj1ALAEuAoeIAAACMpR8i/t5ZHva8bGfuyz58fHJ/d3tDBEeavxKApUtwCQAAwKwNzGu57xiHT819OVN4OdNiOYMsnAOwizPH5SyZ4xIAAFjuBua1nO3q34OmhnkfPlOPyaE5K8/tNx8U81cCLGnmuAQAAGC+/H12LrRMuvkrb5gumDx5ugbmrARY3gSXAAAAjGzdcetfnORv5vCUa+fwXADsQsxxCQAAwEjWHbf+2MxtaJl0w74B4Fr0uAQAAGC7+rkmj0xywhyedmqOy1Pm8JwA7EIElwAAACSZcTGc30nyx0lWz+GlplaJfYoFdgCYieASAABgmesDy79N8pQk+y7AJc9MF1q+fwGuBcASJbgEAABYxtYdt/6YJK9Lst88X2qql+Wzk7xQT0sAdkRwCQAAsEz1i+28d4Eup5clALMiuAQAAFiG+tDyXXN82ouT7D1w/7wkb0/yoSSn6GUJwGwILgEAAHYRQ4vrnJMZwsI+tHxPkpqjS1+U5PFJ/nOU6wPAKKq1tuNWXK2qVifZmGRNa23TYtcDAACQXD1X5SuSHDqw+fx0PR5PT9f78ewkByR5Z5KVc3Tp85IcuuH4o6+ao/MBsIsbNV/T4xIAAGCJ60PLE6fZdUC6lcLn0xOFlgDMhxWLXQAAAADj64eHv6K/O1dDv0exLcnDLLYDwHzR4xIAAGCJGZrL8qBcc3j4fJuab+z3Nhx/9HS9PAFgTgguAQAAloh1x63fLclrkjw8yV7zfLmpgPLSoWudmeQpeloCMN8ElwAAAEvAuuPWvzjJX2fhpvw6M938mB+MlcIBWASCSwAAgAnXh5Z/s4CXfH6S5w4ElCcv4LUBIIngEgAAYNENzVl5TpIvJPn1/v756XpaLqSr9KoEYLFVa23HrbhaVa1OsjHJmtbapsWuBwAAWNrWHbf+mHSrgg8usLMtCzckfDpnJDlceAnAfBg1X1vMH4QAAADLWh9anpjkkKFd8/m72sYR2twwXQ9QAFg0gksAAIBF0A8Pf0WS6r8Wwj8kedKIbdfOZyEAsCOCSwAAgAW07rj1K9cdt/5eSd6Saw4Pn09nJHnohuOPfn6Ss0c85px5rAcAdsjiPAAAAAukHxr+uiT7zfOlzkjy+iSnpgsgTxmYr/KUJGemG54+XU/P1u8/ZZ5rBIDtsjjPLFmcBwAAmI1+SPg9k/xpkofN46WemuTcXDuonK6mqbk1k2uGl1O/IB674fij3z8vVQKw7I2arwkuZ0lwCQAAjGrdceuPTfLGJKvn8TJTPSRntQr4DKuZn5HkKUJLAOaT4HKeCC4BAGD56ntPHpFu4ZoZezb27f4jySPmuISWOewhOerjAYC5JLicJ4JLAABYnmbooXhhkpcneeFU4DeP81h+IMldoockAEuc4HKeCC4BAGD52c6ckFN+nuTx/b9PnKHNuC5L8pgNxx99oh6SAOwKBJfzRHAJAADLSx8WbsjMq3BPael6YO67g3ajakneleQPhZMA7EpGzddWLVxJAAAAS9I9c83h2TOpzM3w8K1J3prkCRuOP/qqOTgfACxJgksAAIAZ9EPEX7+Al/x8kiP1sASAZMViF7CzqurPqmpDVV1RVV+uqrvuoP3Dqur7ffvvVtVvL1StAADA0jEwr+W+83iZU5N8Jcm/J9lzw/FHHyG0BIDOku5xWVW/l+Rfkvxpki8neUqSk6rq5q2186Zp/2tJ3pnkmUk+kuSRST5QVXdqrf3PghUOAABMtH5ey1dkvLkq2wjHbUryOKuBA8DMlnqPy6cleX1r7c2tte+lCzAvS/JHM7R/cpKPt9Ze2lr7v9bas5J8I8mfL0y5AADAEvG3GW1ey2Eb+9uZVkG9LMmzk+wrtASA7VuyPS6ravckd07yoqltrbVtVfXJJPeY4bB7pOuhOeikJA/eznX2SLLHwKa9x6kXAACYfH1Py79N8twxT/GTJM9P11tzMPj8eb/thYaCA8BolmxwmWT/JCuTnDu0/dwkt5jhmBvM0P4G27nOM9P9RRQAANgF9WHlEUkelORRSQ7YidNdueH4o9+/7rj1H+zPuTbJOUlOEVgCwOws5eByobwo1+yluXeSMxepFgAAYA71C/AM947cGV9Nkj6kPHmOzgkAy9JSDi4vSLI1yUFD2w9K8rMZjvnZLNuntbY5yeap+1XjzM0NAABMmoFVw+fSX83x+QBg2VqywWVr7cqq+nqSeyf5QJJU1Yr+/r/NcNiX+v0vH9h23347AACwi+qHg98zyZH9ps+m62mZjLdy+HQ+tOH4ozfvuBkAMIolG1z2/iXJW6rqa0m+kuQpSa6X5M1JUlVvTXJWa+2ZfftXJPlsVf1VkvVJfj/JXZI8foHrBgAA5tHAvJVrk9w0yZOT7DfQ5FlzfMkfbjj+6N+d43MCwLK2pIPL1tq7q+qAJM9Lt8DOt5I8oLU2tQDPYUm2DbT/YlU9MskLkrwwyY+SPLi19j8LWjgAADBv5mHeyu1pSV624fijDREHgDlWrbXFrmFJqarVSTYmWdNa27TY9QAAAL80NG/lfE1QvyXJJ5OclOTfNxx/9FXzdB0A2CWNmq8t6R6XAADA8jU0HPycJF/I3M9bOWhjkpcleUG/ajgAMI8ElwAAwJIzw3Dw85McMMeXOi/J25N8KMkpAksAWDiCSwAAYEkZGA4+3KtyrkJLYSUATICdCi6r6u5JjkpyYJJXtdZ+VFXXTXKLJD9srV0yBzUCAAAkuXp4+Osyt0PBz0jy2CQHpRtyLqwEgAkwVnBZVbsneVeS3033H4aW5MPpVuneluS/0s398o9zUyYAALArmmaeyh2FhvdMst8cl/GUDccf/ek5PicAsJNWjHnc85P8TpInJrl5Bv7a2Vq7Isl704WaAADAclG1X6r+OlXvS9WHUvWKVN1+pub9kO8NST6T5B397YZ++0yOHLGaNkKbC5I8dMPxR79/xHMCAAto3ODyEUle3Vp7XZILp9n/f0luPHZVAADA0lFVqXpmkjNb8o8/v87qdT9dc9Ahm1fu9sgk30rVx1O17+AhA/NUHjJ0tkOSnLiD8HIUFw/dPyPJs9N1wnh+knsnuYHQEgAm17hzXB6Y5Lvb2b81yXXHPDcAALC0PDfJs7659uYf+JOH/v1dL7je9e+UJKu2bsnvfP+Un7/4Y/96jz22XvWpVP1mWru4Hx7+iv7Y4bkqp6aievm649Z/cJph4ycnedYINR2T7veSUYegAwATZtzg8ox0C/DM5NeTnDrmuQEAgKWi6uZJnvXFw273rkc+4oW/N7hry8pV+cCtj9r3/w44PB894S9vtbJte2qS56Wb0/LQ7Z01yQ37dicP7ftskp9n+/NcXpDkZEElACxt4w4Vf0eSJ1TVPQa2tSSpqj9J8vAkb93J2gAAgMn3p5tX7rbpcQ97zsPSBY7X6kH5gwPX5QO3OvKqljw+VavS9YIcxbXa9WHk43dw3BOElgCw9I0bXP5jki8m+Vy6CbRbkpdV1U+TvDbJx9OtKg4AAOzCLt79ug97z+3uu3rzqt1XbqdZvev297teJYdcutuet0s3dHsU07br56V8aJIzh3adEYvtAMAuY6zgsrV2ZZIHJHlckh8n+X6SPZJ8J8ljkzywteYvnAAAsAtbd9z6lZftvucNLtpz7x22veg6q5Mkf/zQf/hYkv3ThY4zrfzd0oWQp8x0vj6cXJfkqCSP7G8PF1oCwK5j1nNcVtV10vW4/Exr7W1J3jbnVQEAAEvBET/ba7+VN77wrB02vMmFXefIc1bvf0CS9yR5aZKnpwspB4eXT4WZT9nRcO9+/8mzrhoAWBJm3eOytXZ5kickOWjuywEAAJaQtR+81ZG534/+O/tfetF2Gz7yWx/Pt9beLKdf/+CpkPIR6ebGH049z0xyrJ6TAMC4c1x+Pclt5rIQAABgyTnnxNveO1es2j0v+egrsvuWq6Zt9HvfPin3/Mk38uY7P3Bq09Sq4RfEcG8AYAazHiree0qSj1bV/yQ5obW2Ze5KAgAAlohTNu2515l/9uDjDnnD+55f73vbX+d1dz0mJ93s13LlylW5zbmn5bFf/3CO/Z9P5T/u+Nv54K2OHD5+reHeAMBMqrWZ5sPezkFV30k3ofZBSTanG95x+VCz1lq7/U5XOGGqanWSjUnWtNY2LXY9AACwmNYdt/6YJCfe8azv528+95a6x0+/myTZUiuyqm3LWXsfkNff9SE54c4PTKqGDz9qw/FHn7zAJQMAi2zUfG3c4PLkzLwC4NVaa0fN+uQTTnAJAADX1IeXr0hy6E0v+GnuePb3s/vWLdvOWHNQfX7dHWrbipXDh7R0c1kevqMFeACAXc+8BpfLmeASAACubd1x61cmOSLJ2iTnpBuh9Z5+93SrhluABwCWKcHlPBFcAgDAaAZ7Yg5sPiPJU4SWALB8zXtwWVUrkzwqydFJbtRvPj3JR5K8vbW2Sw75EFwCAMDopumJeYrh4QCwvM33HJdrkpyU5FeTXJzkx/2uw5OsTvKVJPffFYM9wSUAAAAAjG/UfG3FmOf/xyR3TvIXSQ5ord2ptXanJAcm+fMkd+nbAAAAAADM2rg9Ls9KcmJr7ckz7P/XJMe21g7eyfomjh6XAAAAADC++e5xuV+SH2xn//eT7DvmuQEAAACAZW7c4PLUJA/azv4HJTltzHMDAAAAAMvcuMHlq5Lcr6o+WlX3q6p1/df9q2p9kvsm+be5KxMAAAAAWE5WjXNQa+1VVXVgkuOS3H9o91VJntdae/XOFgcAAAAALE9jLc5z9cFV+ye5T5Ib9ZtOT/LJ1toFc1DbRLI4DwAAAACMb9R8bawel1P6gPJdO3MOAABg+9Ydt35lkiOSrE1yTpJTNhx/9NbFrQoAYH6NNcdlVd2nql64nf3/WFX3Gr8sAAAgSdYdt/6YJBuSfCbJO/rbDf12AIBd1riL8zwryQ23s/+QJH8/5rkBAIBcHVqemO7/14MOSXKi8BIA2JWNG1zeNsmXt7P/q0luN+a5AQBg2euHh7+iv1tDu6fuv7xvBwCwyxk3uNwjye472H/dMc8NAAB0c1oemmuHllMq3SioIxasIgCABTRucPk/SR4y3Y6qqiTHJPneuEUBAABZO8ftAACWlHGDy1cm+fWqem9V3baqVvVft0vy3iT36NsAAADjOWeO2wEALCmrxjmotfa2qrpJukV6jkmyrd+1IklL8oLW2lvmpkQAAFiWTklyZrqFeKYbLt76/acsZFEAAAulWmvjH9yFlw9JcuN+02lJPtBaO20OaptIVbU6ycYka1prmxa7HgAAdl0Dq4on1wwvp/4Tf+yG449+/8JWBQCwc0bN13YquFyOBJcAACykPrx8RbqFeqackeQpQksAYCla0OCyqm6R5GHpJgb/fpITdtVQT3AJAMBCW3fc+pXpVg9fm25Oy1M2HH/01sWtCgBgPHMeXFbVnyf5yyS/1lq7YGD7A9MtyLP7QPMfJ7n7YLtdheASAAAAAMY3ar42m1XFH5TktKHQclWSNyTZmuRxSW6b5LgkN0ryd2PUDQAAAAAwq+DyVkn+e2jbUUkOSPKy1tpbWmv/21p7SZL3JPntOaoRAAAAAFhmZhNc7pduEvBB9063ouF/Dm3/QpLDdqIuAAAAAGAZm01weW6SGwxtOyLJZUm+PbT9yv4LAAAAAGDWZhNcfi3JY6pq7ySpqlsnuWuSk1prW4ba3iLJmXNTIgAAAACw3KyaRdvnJvlqkh9V1f8muXO6YeIvmqbtQ5J8eufLAwAAAACWo5F7XLbWvpvkXkm+nuTgdAv1/HZr7euD7arqyHTDx987Z1UCAAAAAMtKtdYWu4YlpapWJ9mYZE1rbdNi1wMAAAAAS8mo+dps5rgEAAAAAFgQgksAAAAAYOIILgEAAACAiSO4BAAAAAAmjuASAAAAAJg4gksAAAAAYOKMHVxW1eqqOq6qTqqqb1bVXfvt+1bV06rqpnNXJgAAAACwnKwa56CqOjTJZ5PcMMmPktwiyV5J0lq7sKqekORGSZ48R3UCAAAAAMvIWMFlkpcm2TvJHZKc138N+kCS3xm7KgAAAABgWRt3qPj9kvxra+17Sdo0+3+crjcmAAAAAMCsjRtcXifJ+dvZv/eY5wUAAAAAGDu4/F6S39zO/gcn+eaY5wYAAAAAlrlxg8uXJ/n9qnpGkjVT56qqm1bVfyS5R5KXzUF9AAAAAMAyVK1NN0XlCAdW/V2S5ySpdAHotv7f25L8fWvtxXNU40SpqtVJNiZZ01rbtNj1AAAAAMBSMmq+NnZw2V/ksCQPTXLTdOHlaUne31r78dgnnXCCSwAAAAAY34IEl8uR4BIAAAAAxjdqvjbWHJdVdaeqetJ29j+pqu4wzrkBAAAAAMZdnOcfk9xnO/vvleQFY54bAAAAAFjmxg0u75zklO3sPyXJXcY8NwAAAACwzI0bXO6dZMt29m9LsmbMcwMAAAAAy9y4weWPktxvO/sfkGSXXVkcAAAAAJhf4waXb0xydFX9S1XtM7WxqvapqpelCy7fOAf1AQAAAADLULXWZn9QVSV5U5LHpBsWfna/6+B0Yeh/JHlsG+fkE27U5doBAAAAgGsbNV8bK7gcuMhRSR6a5Mb9ptOSvK+1dvLYJ51wgksAAAAAGN+CBJfLkeASAAAAAMY3ar427hyXAAAAAADzZqzgsjpPqKqvVNUFVbV1mq8tc10sAAAAALA8rBrzuJckeVqSbyV5W5KL5qogAAAAAIBxg8vHpFuE5+FzWQwAAAAAQDL+HJfXSfLJuSwEAAAAAGDKuMHlp5L86lwWAgAAAAAwZdzg8klJ7l5Vf1tV+81lQQAAAAAA1Vqb/UFVF6cLPffsN12RZOtQs9ZaW7Nz5U2eqlqdZGOSNa21TYtdDwAAAAAsJaPma+MuzvO+JLNPPAEAAAAARjBWcNlae+wc1wEAAAAAcLVx57gEAAAAAJg3YweXVXVYVb2mqn5QVRdV1W/22/evqn+tqjvOXZkAAAAAwHIy1lDxqrpVklPSBZ9fTnLTqXO11i6oqt9Icr0kfzxHdQIAAAAAy8i4i/O8JMkvktw93SI95w3tX5/k98YvCwAAAABYzsYdKv6bSV7dWjs/068u/tMkh4xdFQAAAACwrI0bXK5Ictl29h+QZPOY5wYAAAAAlrlxg8tvJDl6uh1VtSrJ7yf573GLAgAAAACWt3GDyxcleUBVvTrJbfptB1XVfZL8V5JbJjl+DuoDAAAAAJaham26KSpHOLDqD5O8IsmaJJVurstKsinJE1tr75yrIidJVa1OsjHJmtbapsWuBwAAAACWklHztbGDy/4i10tyvyQ3Tdd787QkJ7XWLh77pBNOcAkAAAAA4xs1X1u1MxdprV2a5D935hwAAAAAAMPGCi6r6rBR2rXWfjrO+QEAAACA5W3cHpcb0s1puSMrxzw/AAAAALCMjRtc/lGuHVyuTLIuyaOTnJfk38cvCwAAAABYzsYKLltrJ8y0r6penOTL6VYbBwAAAACYtRVzfcJ+wZ43J3nqXJ8bAAAAAFge5jy4HDjvDebp3EmSqtq3qt5eVZuq6hdV9caq2msHx5xcVW3o6zXzWScAAAAAMHvjznE5rapaneQ3kzw9yTfn8tzTeHuStUnum2S3dL08X5fkkTs47vVJ/mHg/mXzUh0AAAAAMLaxgsuq2paZVxWvJD9N8qRxixrh+rdM8oAkv9pa+1q/7S+SfLSq/rq1dvZ2Dr+stfaz+aoNAAAAANh54/a4fF6uHVy2JBclOS3Jf7XWtuxMYTtwjyS/mAote59Msi3J3ZL853aO/YOqelSSnyX5cJLnt9Zm7HVZVXsk2WNg095jVw0AAAAAjGTcVcWfM8d1zNYNkpw3uKG1tqWqLsz259Z8R5LTk5yd5HZJXpzk5kmO2c4xz0zy7J2qFgAAAACYlTmd43JnVdXxSZ6xg2a3HPf8rbXXDdz9blWdk+RTVXWT1tppMxz2oiT/MnB/7yRnjlsDAAAAALBjIwWXVfWmMc7dWmt/PMtj/jnJCTto8+N0w7wPHNxYVauS7NvvG9WX+9ubphvifi2ttc1JNg9cZxanBwAAAADGMWqPy3tl5sV4ZjLb9mmtnZ/k/B21q6ovJdmnqu7cWvt6v/leSVbkl2HkKO7Q354zmzoBAAAAgPlVrc06X5wIVfWxJAcl+dMkuyV5c5KvtdYe2e8/JMmnkjy6tfaVqrpJkkcm+WiSn6eb4/JlSc5srd1zFtddnWRjkjWttU1z+JAAAAAAYJc3ar62YsSTfaOqHjBw/9FVtW6nq9w5f5Dk++nCyY8m+XySxw/s3y3dwjvX7e9fmeQ+Sf6rP+6fk7wvyQMXqF4AAAAAYEQj9bisqi1JHttae1t/f2uSP2ytvWOe65s4elwCAAAAwPjmtMdlktOT3KeqVk6dP2PMYQkAAAAAMIpRe1w+PcmLk2xNcnmS66VbaXvLdg5rrbU1c1HkJNHjEgAAAADGN2q+NtKq4q21l1bVt5MclW5BnMck+WqSH89BrQAAAAAA1zDWquJVtS3Jo8xxqcclAAAAAMzGnPa4HNZaG3VuTAAAAACAWRsruJxSVXsnuVGS66dbsOcaWmuf25nzAwAAAADL01jBZVXtn+SVSR6aZOV0TdKtOj7dPgAAAACA7Rq3x+Xrkjwwyb8mOSXJRXNWEQAAAACw7I0bXN4vyctaa38zl8UAAAAAACTJuIvsXJZkwxzWAQAAAABwtXF7XL4tyUOSvGoOawEA5kvVPkl+K8l+SX6R5ONp7YLFLAkAAGB7xg0uT0xyz6r6eLr5Ls9IsnW4UWvtGztRGwCws7rA8sVJHpXkukmuTLJ7kitT9e4kf53Wzlu8AgEAAKZXrbXZH1S1beDudCeoJK21tsutKl5Vq5NsTLKmtbZpsesBgBlVXT/JZ5PcMMm/nHe967/prn/+H79y65+derPnfvK1d7zzWf93TCUXJzkirZ2zuMUCAADLxaj52rjB5WNGaddae8usTz7hBJcALBlVb0/ygCS/ue4ZH7l5klckOXRq97oLzz7nY2/+892vs+XKr6W1ByxWmQAAwPIyr8Hlcia4BGBJqDo4yelJnrbuGR85K900L0k3KmJKe/D/fiYv/8g/V5JbprXvL3SZAADA8jNqvjbuquIAwGR7WJKtp+576NvS9bRMrhlaJkl99Oa/kY17XG/b1lrxyIUtDwAAYPtGXpynqp42y3O31trLZnkMADA3Dkrys/v8yWtun4Hh4cOuXLVb/XSfG9ReV15+h8MXrjYAAIAdms2q4v80y3O3JIJLAFgclyTZ5zpXXnHI5bvvOXOr1nL9yy/OeXtdf4XgEgAAmCSzGSp++Cy/bjynlQIAs/HxJGteeNK/rdteo189839z6Kbz8r0Db/yZhSkLAABgNCP3uGytnT6fhQAAc6i1b6Tqyw/+3snH/t39/+ysy3a/zsEZmuNyt61X5a9PeVtO3+cGW7530I1fMcOZAAAAFoXFeQBg1/Wnldz0lNf+v0tvf/YPkm4alyTJLc/7cd783ufkDmd/P++/9b2e+8KP/9uWxSsTAADg2qq1tuNWXG3U5doBYCJU/WqSdyc5/Ef73fCqDddfu9sNLv55bnvuaTnvetff+u7b3e95f/HFdz1vscsEAACWj1HzNcHlLAkuAVhyqlYmecC21MMvuN4+v7Jxz722fv+AdZ+4ctVuL37odz+1ebHLAwAAlhfB5TwRXAIAAADA+EbN18xxCQAAAABMHMElAAAAADBxVo1zUFX9ww6atCRXJDkzyedaa2eNcx0AAAAAYHkaK7hM8px04WSS1NC+4e1bq+r1Sf68tbZtzOsBAAAAAMvIuEPFD03ynSRvSXLnJGv6r7skeWuSbyW5WZI7JXl7kick+dudrBUAAAAAWCbGWlW8qj6Q5PLW2iNm2P+uJKtaa8f29z+a5KattZvtRK0TwariAAAAADC++V5V/F5JPrud/Z9Nct+B+x9NctiY1wIAAAAAlplxg8vNSe62nf13T3LlwP1VSS4Z81oAAAAAwDIzbnD5ziSPrqp/qqqbVNWK/usmVfXPSR7Vt5lyVJLv7WyxAAAAAMDyMO6q4n+T5KAkT0vy1CRTq4WvSLea+Pv6NqmqPZN8PckXd6pSAAAAAGDZGGtxnqsPrrpjkgckuVG/6fQkJ7XWvjEHtU0ki/MAAAAAwPhGzdfG7XGZJGmtfTPJN3fmHAAAAAAAw0ae47Kq9qyq11TVX+yg3V9W1auraredLw8AAAAAWI5mszjP45M8Nsn6HbRbn+RxSf7fmDUBAAAAAMvcbILLhyd5X2vtx9tr1Fo7Lcl7kzxiZwoDAAAAAJav2QSXt03y+RHbfjHJ7WZfDgAAAADA7ILL3ZNcOWLbK5PsMftyAAAAAABmF1yeneQ2I7a9Td8eAAAAAGDWZhNcfjLJo6vqwO016vc/OskndqYwAAAAAGD5mk1w+eIkeyb5dFXdbboG/fZP9e1euvPlAQAAAADL0apRG7bWflxVD0/yziRfrKofJ/lukouT7J1uePhNklyW5Pf71cUBAAAAAGatWmuzO6BqXZJnJPmdJIcM7Do7yUeSvKS19uO5KnDSVNXqJBuTrGmtbVrsegAAAABgKRk1X5t1cDl0kb2TrE6yqbV28dgnWkIElwAAAAAwvlHztZGHik+nDyuXRWAJAAAAACyc2SzOAwAAAACwIASXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcVYtdgEAwDyoukmSGyW5Msm309rFi1wRAADArOhxCQC7kqoHpuqzSU5N8qkkpyQ5O1WvTtUNF7c4AACA0elxCQC7iqrjkrwoySlXrVj1iL+735NWtKpDH/2Nj9zqNueedkwlD07VvdLa/y12qQAAADtSrbXFrmFJqarVSTYmWdNa27TY9QBAkq6nZfKhJM+78dM/+O1tK1a+IsmhU7sPvOTnZ3/iDU/asmbzpVuT3DKtbV6sUgEAgOVt1HzNUHEA2DX8dZJT+tDyxCSHDO48b6/91j70US+9YZLDkxyzGAUCAADMhuASAJa6biGe37xqxapX9T0tk6SGW526/2H52iG33NySP1rgCgEAAGZNcAkAS9+NkuTv7vekFemGhw+HllPq64fcco/LV+1xiwWrDAAAYEyCSwBY+q5MklR2uGr4da7anCtX7TbvBQEAAOwswSUALH3fTnLJH35j/S2312jV1i2534++lIuus/qbC1QXAADA2ASXALDUtXZxkv+4zbmn/daBl/z87CRtumYP/84n2g0uuTAHXHLhsxe2QAAAgNkTXALAruFFlWz75BuetPVm529IBsLLVVu35JHf/Fh77idfUz/Y/0af3OvKy/W4BAAAJl61Nm2nDGZQVauTbEyyprW2abHrAYCrVd0iyfokN/7aIbfc/I2Db7HHdbZszv1+9N856JIL84P9b/TJm19w+m+ntasWu1QAAGD5GjVfE1zOkuASgIlWtXuSY1ryR5fvtsctrly5Wy68zupvHnjpRf+w1+bLvr3Y5QEAAAgu54ngEgAAAADGN2q+Zo5LAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgIkjuAQAAAAAJo7gEgAAAACYOIJLAAAAAGDiCC4BAAAAgImzarELAGDCVa1MclC6nxnnprXNi1wRAAAAy4AelwBMr2rfVP1dkp8kOSvJ6UkuSNW/p+pmi1scAAAAuzo9LgG4tqobJ/lkkhtsS73zA7c+8rSf7bXf3r/5k28ccuvzfnxMJY9N1bFp7WOLXSoAAAC7pmqtLXYNS0pVrU6yMcma1tqmxa4HYM5VXSfJt5PUHz/0Wf/0qZve7e+THDq1e8+rrjjrk2940tmHbjrvNkl+Na3972KVCgAAwNIzar5mqDgAwx6e5Fee/ltPfuWnbnq3Vyc5ZHDnFbvtefC9/9+r73LZbntckuRpi1IhAAAAuzzBJQDDHr8tOem9t7vv0/v7NbS/Nu+2R17/qw/ZvSWPTNXeC10gAAAAuz7BJQDDbvHttTf/abrh4cOh5ZT67OF3XlPJnkkOW7jSAAAAWC4ElwAM23bJHtdZs6NGK9vWq9vPbzkAAAAsR4JLAIZ987Y/O/VWO2p01I+/li214tIkG+a/JAAAAJYbwSUAw169zxWX3Obup3/nvCRtugZrLr+4PeJbJ22rtDektcsXuD4AAACWAcElAMM+nOS/3/LeZ+95h7N/kAyFlwdecmF7y3ufXde96opLVrb2L4tSIQAAALu8am3azjTMoKpWJ9mYZE1rbdNi1wMwL6r2T/KRJHf76qG32vzRm//6Hlet3C13PvN7Ofr7n0+r2rjH1qvunda+vtilAgAAsLSMmq8JLmdJcAksG1W7J3loS57UUndL2sordtvjrN23XPWvq9q2N6W1Cxe7RAAAAJYeweU8EVwCy1ZVxQ8NAAAAdtKo+Zo5LgEYjdASAACABSS4BAAAAAAmzpINLqvq76rqi1V1WVX9YsRjqqqeV1XnVNXlVfXJqvqVeS4VAAAAAJilJRtcJtk9yXuTvHoWx/xNkr9M8qdJ7pbk0iQnVdWec18eAAAAADCuVYtdwLhaa89Okqp67Cjtq6qSPCXJC1prH+y3PTrJuUkenORd81EnAAAAADB7S7nH5WwdnuQGST45taG1tjHJl5PcY6aDqmqPqlo99ZVk73mvFAAAAACWueUUXN6gvz13aPu5A/um88x0y7NPfZ0596UBAAAAAIMmKrisquOrqu3g6xYLXNaLkqwZ+Dp0ga8PAAAAAMvOpM1x+c9JTthBmx+Pee6f9bcHJTlnYPtBSb4100Gttc1JNk/d76bKBAAAAADm00QFl62185OcP0+n/0m68PLe6YPKfs7Ku2V2K5MDAAAAAPNsooaKz0ZVHVZVd0hyWJKVVXWH/muvgTbfr6qHJElrrSV5eZK/r6oHVdVtk7w1ydlJPrDQ9QOLrOrGqXpJqk5L1YWp+nGq/jlVN13s0gAAAIAJ63E5S89L8piB+9/sb49KcnL/75unm5dyykuSXC/J65Lsk+TzSR7QWrtiPgsFJkzVk5K8MsnGbal3ffPgm6/cbduWG93yvJ/8v1Xbtj61qp6a1l6x2GUCAADAclZdR0RG1Q8v35hkTWtt02LXA8xS1R8keVuSf73HE9/8pXNWH/DS9Itu7XHV5vztyW+++DHf+MjeSR6X1k5YxEoBAABglzRqvia4nCXBJSxhVauSbEjyhRs//YPv3rZi5YlTe65u01p72Uf+uX7rB1/8xZ5brzwwrV21CJUCAADALmvUfG3JznEJMIbfSXLIpbvt+eJtK1ZODQWva7Soqtfc7di259Yr97lyxapjFrxCAAAAIIngElhe7prkp7d+2omr0w0Pr+ka/eDAdbVhn7X5yb6HPGRBqwMAAACuJrgElpNVSa5MsnZHDa9auSqX77bH6vkvCQAAAJiO4BJYTk5Ncvh9f/ilK7fXaP9LL8qNLjonl+22x48WqC4AAABgiOASWE7enWTzaz7wwjsmOTPJtKuTPeJbH2/bVqxoB2+64HkLWh0AAABwNcElsHy0tjHJv69s7RnP/69XvX1q62CTe536lfYXX3x3/XD/w9avu+jsny98kQAAAECSVGvTdjhiBqMu1w5MqKpVSd6R5Niz997/my868nE3Ov36a/dbe/EFefh3PpGjTvtazlxz4JcP23juEWntqsUuFwAAAHY1o+ZrgstZElzCLqBqRZLHJPmzJHee2nzx7tf90Z5bNr90t21b35TWti5afQAAALALE1zOE8El7GKq1iW5fpKLkpweH4oAAAAwr0bN11YtXEkAE6i1DUk2LHIVAAAAwBCL8wAAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXALzp2rVYpcAAAAALE2CS2BuVf1qqk5I1cVJrkrVJan6j1TdbbFLAwAAAJYOwSUwN6oqVc9O8pUk99xaK/750ze+y4s/f6M7fPTyVbvfO8l/p+oFqapFrhQAAABYAgzjBObKE5M8J8nf3/xp7/vB5t32eFmSQ5Ok2rb8+RffvfGvPv/2v0vy8yQvW7wyAQAAgKWgWmuLXcOSUlWrk2xMsqa1tmmx64GJULVHkp8m+fC6Z3zko0lOnNoz0Kq94KR/r4d/5xOX7L5ty4Fp7fIFrxMAAABYdKPma4aKA3PhIUkOvHj3674sySv6bcNDwuv1d31I233blr2uWrHy4QtbHgAAALDUCC6BuXD7JBtu+9T3HJBuePi081iefv2D67R9D8lP9j3ktxe0OgAAAGDJEVwCc2FFkm1J1u6o4bZakUt3u85e818SAAAAsJQJLoG58H9JDn/odz+13Ulzb7Dpgtz4wrOyaY/rfm+B6gIAAACWKMElMBfek2TTSz728rsnOTPJtAHmY7/x4Vyxavd24KUXvWBBqwMAAACWHMElsPNauyzJS1e29uR//8CLPjS1dbDJQ7/7yfb4L78//3vQTd57y/N+snHhiwQAAACWkmptuyM7GTLqcu2w7FRVkn9P8sQLr7P3qa/8td/fb8P1D77+2osvyMO/84nc4Zwf5gf73+iTN7/g9PuntW2LXS4AAACwOEbN1wSXsyS4hO3owsvfTvJnLXlA9auLX3DdNV9dvfnSF+6+dcsH40MHAAAAljXB5TwRXMKIqvZKsibJxrR2yWKXAwAAAEyGUfO1VQtXErCsdGGlwBIAAAAYi8V5AAAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7gEAAAAACaO4BIAAAAAmDiCSwAAAABg4gguAQAAAICJI7hk11F13VT9Uaq+lKqfp+r8VH0sVQ9K1crFLg8AAACA0Qku2TVU3SzJ/yR5w7bkwm8cfPP3nbLuDp/euMf1bpTkg0lOTtW+i1skAAAAAKOq1tpi17CkVNXqJBuTrGmtbVrsekhSdUCSrye55C8f+Nf//qFbHXlckkOndh/xk2+c/8b3PW/P3bdu+XaSo9LalsUqFQAAAGC5GzVf0+OSXcFfJLn+ox7+/H/50K2OfGWSQwZ3nnL4nfZ/1O+9YK8kv5HkQYtRIAAAAACzI7hkaavaLcmfbEv9x+cPv+Ozp7YOt/rKDW+Tb9/gV65syRMXuEIAAAAAxiC4ZKk7NMkN3n27+/2k//dwaDmlPnqLX999a624x8KVBgAAAMC4BJcsdauS5OzV+++9o4ZXrdjt6vYAAAAATDbBJUvd2Ukuv/dpXzlkRw3vfNb3cvlue5y5ADUBAAAAsJMElyxtrV2a5B23P+dH9919y5VnJmnTNTt443nt/j/8Uq5z1eZXLGyBAAAAAIxDcMmu4GWVHPiJNz7pjN23XJUMhZf7Xraxve4/X1BXrVx14aq27a2LUyIAAAAAs1GtTdtBjRlU1eokG5Osaa1tWux66FX9bpJ3X7rbnhtfe7dj9vj8ujuuWbVta+596lfy+98+adseW67ctOfWq+6Z1r6z2KUCAAAALGej5muCy1kSXE6wqtsleVpLfr+SPZLkqhUrN61o296wsrWXpTXzWwIAAAAsMsHlPBFcLgHd9+iwJFuT/CStXbHIFQEAAADQGzVfW7VwJcEC6V7w/7PYZQAAAAAwPovzAAAAAAATR49Llo+q3ZM8NMkjkhyQ5JIk65O8Ja1dtJilAQAAAHBNelyyPFTdPclPkryjJdc/fc0NLvrpmoP23Zb8U0vOTNVjFrtEAAAAAH5Jj0t2fVW3T/KJJN9++m89+cXvvd19n57k0CQ54JKL8nefeUN78Pc+e0KqtqS1ty9qrQAAAAAksar4rFlVfAmq+lSSA3/9T9/4wrPWHDQVTNbV+1trL//IP9Vvf/8Ll+y+bcuBae3yRakTAAAAYBkYNV8zVJxdW9Utk9zrqhUrjz9rzUEvmdo61KZe9ht/0HbftmWvq1as/L0FrxEAAACAaxFcsqs7KsmWX//TN52Xbnh4Tdfo9OsfXN9ae7P8bO/9HrGg1QEAAAAwLcElu7o9k1x+3t777b+jhhfvft1ctWLVmgWoCQAAAIAdEFyyqzsryd4P/e6ntjuZ68ptW3PTn5+RrStWnrFAdQEAAACwHYJLdnUfTrLxJR97+Z2SnJlk2gDzPqd+ua295Oc5eNP5xy9odQAAAABMS3DJrq21y5K8ZmVrT37GyW9+49TWwSY3/MU57TmfeG2dd73r/+/1rrz86wtfJAAAAADDqrXtjqBlyKjLtTNBqnZP8oEk9/n+ATf6zDPv/xe3/9H+hx2072Ub89D/+VQe+/UPb1vZtp2715WX3zmtnbPI1QIAAADs0kbN1wSXsyS4XKKqdkvyN0melOTgqc1ba8Xl1dpbVqQ9K61dsGj1AQAAACwTgst5Irhc4roA84gkBya5OMnn0trFi1sUAAAAwPIxar62auFKggnQ2lVJPr3YZQAAAACwfRbnAQAAAAAmjuASAAAAAJg4gksAAAAAYOIILgEAAACAiWNxHhZG1eFJ1iW5Msl3rOQNAAAAwPboccn8qvqtVH06yY/Treb9+SRnp+pVqbrh4hYHAAAAwKTS45L5U/VXSf4pyRevWrHyUc++759my4qVh/7hN9bf8jbnnvbQSh6Sqnunte8tdqkAAAAATJZqrS12DUtKVa1OsjHJmtbapsWuZ2JVPSDJx5K86MZP/+DXtq1Y+Yokh07tPuCSC8/+5BueeNWazZe2JLdIa5sXq1QAAAAAFs6o+Zqh4syXpyf5Uh9anpjkkMGd5++179qHPuqlh6Wb9/LYRagPAAAAgAkmuGTuVd0oyb2uWrHyVX1PyySp4Van7n9YvnLorTa35I8WuEIAAAAAJpzgkvmwLkmed+8/qXTDw4dDyyn19UNutccVq3a/xUIVBgAAAMDSILhkPlyZJFtr5SE7arjnls25auVu818RAAAAAEuK4JL58N0kF//Btz526+01Wrlta+7/wy/louvs/a2FKQsAAACApUJwydxr7ZIkb7nVeT++/0EX//zsJNMuXX/sdz/VDr74gux/6S+evbAFAgAAADDpBJfMl+MrueoTb3zi1l85//RkILxcuW1rfu/bJ7Xn/9er6kf73fAz17vy8q8tXpkAAAAATKJqbdrOcMygqlYn2ZhkTWtt02LXM9GqbpFkfZIbf+2QW27+2iG33OM6Wzbnfj/876y95Of50X43/Myv/PyM+6e1qxa7VAAAAAAWxqj5muBylgSXs1S1e5JjWvLHl6/a45ZXrVzVLrrO6m8fcOlFz77elZd/fbHLAwAAAGBh7fLBZVX9XZKjk9whyZWttX1GOOaEJI8Z2nxSa+0Bs7iu4BIAAAAAxjRqvrZq4Uqac7sneW+SLyX541kc9/Ekjxu4v3kuiwIAAAAAdt6SDS5ba89Okqp67CwP3dxa+9mojatqjyR7DGzae5bXAwAAAABmaTmuKn5kVZ1XVT+oqldX1X47aP/MdF1Xp77OnPcKAQAAAGCZW27B5ceTPDrJvZM8I8k9k3ysqlZu55gXJVkz8HXofBcJAAAAAMvdRA0Vr6rj0wWK23PL1tr3xzl/a+1dA3e/W1XfSXJakiOTfGqGYzZnYB7Mqhrn0gAAAADALExUcJnkn5OcsIM2P56ri7XWflxVFyS5aWYILgEAAACAhTdRwWVr7fwk5y/U9arq0CT7JTlnoa4JAAAAAOzYkp3jsqoOq6o7JDksycqqukP/tddAm+9X1UP6f+9VVS+tqrtX1bqquneSDyY5NclJi/EYAAAAAIDpTVSPy1l6XpLHDNz/Zn97VJKT+3/fPN2COkmyNcnt+mP2SXJ2kv9K8qx+HksAAAAAYEJUa22xa1hSqmp1ko1J1rTWNi12PQAAAACwlIyary3ZoeIAAAAAwK5LcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNn1WIXwASp2j/JryXZM8kZSb6c1rYtblEAAAAALEeCS5KqdUmen+ThSXYf2POjVL0syWvSWluM0gAAAABYngSXy13VrZJ8JsmVW2rFs578wKf/5P8OPHzvP/jWx/Z63Nc+9Osr0l6V5I6peoLwEgAAAICFUrKo2amq1Uk2JlnTWtu02PXslKpVSb6f5LIHPfpf/vk7a2/2giSHDrQ4818/9JIPPOj/PvfnSf40rb12UeoEAAAAYJcxar5mcZ7l7YFJbvLs+zzhLd9Ze7M3JzlkaP8hf/mgv/mzDfus/WKSp6aqFr5EAAAAAJYjweXy9uiWfPktd37gU/r7w8FkJcmz7vfEmya5eZK7LmBtAAAAACxjgsvl7ZDT91l7frrh4TP1pqzv3uCmB/b/PnSGNgAAAAAwpwSXy9sVW1es2H9HjfbafNnUPy+f33IAAAAAoCO4XN4+ve6ic+4wEExO60H/97lsrboyyX8vTFkAAAAALHeCy+Xt9Svatt2e/IV3Xpxk2uXl97/0ova4r31oa7W8I61duMD1AQAAALBMCS6Xs9bOquTZf/LV/9z76Z99S62+4pJrhJe3P/sH7Z3v/Nva+8rLNq1I+4fFKhMAAACA5adam7ajHTOoqtVJNiZZ01rbtNj17LSqSvLMbannbV6124rP3Pgudcke180tz/tJbnvuabl49+uetfeVl90rrf1wsUsFAAAAYOkbNV8TXM7SLhdcTqlau7VWPH7jnns9eGutuN6WlSvP3veyjf+8x9YtH01rWxe7PAAAAAB2DYLLebLLBpcAAAAAsABGzdfMcQkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNHcAkAAAAATBzBJQAAAAAwcQSXAAAAAMDEEVwCAAAAABNn1WIXsITtXVWLXQMAAAAALDV7j9JIcDl7U0/smYtaBQAAAAAsbXsn2TTTzmqtLWAtS1913SwPTnLxYtcygfZOF+geGs8PTDLvVZh83qewNHivwuTzPoXJtXeSs9t2wkk9LmepfzLPWuw6JtHA0PmLW2szpuXA4vJehcnnfQpLg/cqTD7vU5hoO3xPWpwHAAAAAJg4gksAAAAAYOIILplLm5M8t78FJpf3Kkw+71NYGrxXYfJ5n8ISZnEeAAAAAGDi6HEJAAAAAEwcwSUAAAAAMHEElwAAAADAxBFcAgAAAAATR3DJ2KpqbVUdX1WfqaqLq6pV1ZGzOP45/THDX1fMX9Ww/Ozse7U/xyFV9Z6q+kVVbaqqD1bVjeenYlieqmqfqnpdVZ1fVZf279k7jXjsCTP8TP3+fNcNu6Kq2qOqXlxVZ1fV5VX15aq674jH+pkJC2Dc96nfQ2FpWbXYBbCk3TzJM5L8KMl3k9xjzPM8McklA/e37mRdwDXt1Hu1qvZK8pkka5K8MMlVSZ6a5LNVdYfW2s/ntlxYfqpqRZL1SW6f5KVJLkjypCQnV9WdW2s/GuE0m5P8v6FtG+e0UFg+TkhybJKXp/v5+dgkH62qo1prn5/pID8zYUGdkDHepwP8HgpLgOCSnfH1JPu11i6sqmOTvHfM85zYWrtgDusCrmln36tPSvIrSe7aWvtqklTVx5L8T5K/SvK3c1ksLFPHJvm1JA9rrZ2YJFX1niQ/TPLcJI8c4RxbWmtvm78SYXmoqrsm+f0kT2+t/VO/7a3pfu69JN17dSZ+ZsIC2Mn36RS/h8ISYKg4Y2utXdxau3AOTlVVtbqqag7OBQyZg/fqsUm+OvULWH/O7yf5VJKH72x9QJLufXZukvdPbWitnZ/kPUl+t6r2GOUkVbWyqlbPT4mwbBybrufV66Y2tNauSPLGJPeoqhvu4Fg/M2H+7cz7dIrfQ2EJEFwyCX6cbijbxVX1tqo6aLELAjr98NXbJfnaNLu/kuQmVbX3wlYFu6Q7JvlGa23b0PavJLlukpuNcI7rJtmUZGNVXVhV/94PWwVm545Jftha2zS0/Sv97R2mO8jPTFhQY71Ph/g9FJYAQ8VZTBcl+bckX0o3L9cRSf4syV2r6i7T/BACFt6+SfZIcs40+6a2HZzkBwtWEeya1ib53DTbB99n393O8eekGxr3jXR/mH5AuiGrt6+qI1trW+awVtjVrc2Of+5Nx89MWDjjvk8Tv4fCkiK4JMnVfyHefcTmm1trbWev2Vp7xdCm91XVV5K8Pd0vW8fv7DVgV7MI79XrTJ1rmn1XDLUBMvb79DrZifdZa+2ZQ5veVVU/TPKP6YbTvWvEeoDx349+ZsLCGfvnpt9DYWkxVJwpv5nk8hG/bj5fRbTW3pHkZ0nuM1/XgCVuod+rl/e3082vt+dQG6Azzvv08sz9++xlSbbFz1SYrXHfj35mwsKZ05+bfg+FyaXHJVO+n+RxI7adrkv+XDoj3VAb4NoW+r16Ybq/Zq+dZt/UtrPn4DqwKxnnfXpO5vh91lq7vKp+Hj9TYbbOSXLINNt39H70MxMWzrjv0+3xeyhMIMElSZLW2s+SnLDYdfQruq1L8s1FLgUm0kK/V1tr26rqu0nuMs3uuyX5cWvt4oWqB5aCMd+n30pyRFWtGFqg525JLkvyw9nW0S8Csn+S82d7LCxz30pyVFWtHprr7m4D+6/Fz0xYUN/KGO/Tmfg9FCaXoeIsiKo6rKpuMbTtgGmaPjHJAUk+viCFAdcw3Xs1yYlJfrWq7jLQ7uZJ7pXkvQtZH+zCTkxyUJJjpjZU1f5JHpbkw621zQPbb1JVNxm4v+cMKxU/K0nFz1SYrROTrEzy+KkNVbVHup7UX26tndFv8zMTFs/Y71O/h8LSUnOwxgrLWFX9ff/PWyf5/SRvSvKTJGmtvWCg3clJ7tlaq4FtlyV5d7pVUq9I8hv9Ob6d5Ndba5ctwEOAZWEn36t7p/vr895J/inJVUmelu4/i3dorenNBTupqlYm+XyS2yR5aZIL0i0QcFiSX22t/WCg7YYkaa2t6++vS/cefWe6YepJcv8kv53uF7Cjh3pxAjtQVe9J8pB0c8WemuQxSe6a5N6ttc/1bU6On5mwaHbifer3UFhCBJfslKqa8QU09MPh5Fz7B8brk/xakhumm0T59CTvS/KPhtHA3NqZ92q//dB0/ym8X7re+icneWpr7dT5qBeWo6q6frrQ8sHpVkP9apK/bq19bajdhuQaweU+SV6Z5O5JDk4XkJyabnXUf2qtXbUQ9cOupKr2TPL8JI9Kcv0k30nyrNbaSQNtTo6fmbBoxn2f+j0UlhbBJQAAAAAwccxxCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAAAAADBxBJcAAAAAwMQRXAIAAAAAE0dwCQAAAABMHMElAMAEq6rnVFVb7DrmW1WtqqqXVNUZVbWtqj7Qb29V9ZzFrQ4AgMUguAQAWCBV9dg+iJv6uqKqzq6qk6rqL6tq78WucUpVXbcPTY8csf2R/WM6dsxL/lGSpyc5McljkrxszPMseVX1G1X1sao6q3+N/LSqPlxVj1zs2gAAFtKqxS4AAGAZ+ockP0myW5IbJDkyycuTPK2qHtRa+85A2xckOX6hC0xy3STP7v998gJc715JzmqtPXUBrjWxquphSd6d5FtJXpHkoiSHJ/nNJH+S5B2LVhwAwAITXAIALLyPtda+NnD/RVV1ryQfSfKhqrpla+3yJGmtbUmyZXsnq6oVSXZvrV0xbxXPvwOT/GKxi5gAz0nyvSR3b61dObijqg5cqCKqqpLsOfU6BABYDIaKAwBMgNbap5M8P8mNkjxqavt0c1z2Q7L/rar+oKr+N8nmJA/o9x1SVW+qqnOranNV/W9V/dHw9apqz/7cP+yHI59TVe+vqptU1bok5/dNnz0wtP05s3lMU7VX1U2r6oSq+kVVbayqN1fVdfs26/rHd1SSWw9c68gZznlCVW2Y6VrTbH9UVX29qi6vqgur6l1VdcOhNidX1f9U1a2q6jNVdVk/TPtvZvO8DbRZUVVP6Z/7K/rvxWur6vojPG03SfLV4dAySVpr5w3VsqKqnlxV3+2vc35Vfbyq7jLQZlVVPauqTutfDxuq6oVVtcfQuTZU1Ueq6v5V9bUklyd5Qr9vn6p6eT//6OaqOrWqntEH5gAA88Z/NgAAJsd/9Lf3G6HtvdLNA/nuJE9OsqGqDkry30nuk+Tf+u2nJnljVT1l6sCqWpmud+ezk3w9yV+lG5a8Jslt0oWWT+yb/2eSP+y/3j/m43pPkr2TPLP/92Pzy2Ho5/fn/n6SMweu9X9jXutqVfV3Sd6a5EdJnpZuOP69k3yuqvYZan79JB9P8u10z8f3k7y4qn5r4Hw7et6mvDbJS5N8Id334M1J/iDJSVW12w7KPj3Jvavq0BEe4hv7x3RGkmekm1LgiiR3H2jzhiTPS/KNJE9N8tl034d3TXO+myd5Z5JP9HV/qw+YP5suTH9rkr/sH9eLkvzLCDUCAIzNUHEAgAnRWjuzqjam63W3IzdPctvW2vemNlTVG5Ks7Lf/vN/8mqp6Z5LnVNVr+6G/j04X4D2ttTa4CM7xVVWttVZVJyZ5dZLvtNbetpMP7ZuttT8eqHO/JH+c/P/27jbUsqoM4Pj/GcsPKjmZEvpFywb8oJYKRmHFWGFjZNg0CUmSOJmJZEVhlhYFTWlNgYVk774EMqRpr0wK0zCOYCVRmuDIdSpDJnOcJqcpsvHpw7POzGbPOfeeez13PND/B5d999lrr7X2vufD5eFZ6+GKzPwncEtErAb2TGCswRjHAp8BrsrMNZ3Pbwd+C1wKrOnccgxwQWbe3Np9mwoiXgT8vLWZ9b21+84AVgPnZ+be/SgjYgMVGF3F7PtUXkMFJGciYjNwD/AL4N7MfLbT33IqAHxdZl7euX9tZy6vpAodfSsz39euXx8RTwAfjYjlmbmhc+8rgLdk5vrOOFdR38dTMvOR9vENEfE48LGIWJuZj83yPJIkSQtmxqUkSdJ02UVlJ85lYy9oGcBK4Mft9MjBD7Ceygo8tTVfCTwJfLXfaWbut9x6Ar7eO98EvCQiXrQIYw28g/pfd13vXWyjMjCX99rvAvYGTdtS7V8BL++0Gee9rQJ2Anf1xr2/jdEft9/Pd6hl/78EzgCupt7XIxHx2t5ckgrOjprL2e3Yz4xc245v7X2+tRu07DzPJmBH73nupoLkr5/teSRJkp4LMy4lSZKmy2HAE3O2qqrkXUcBS4GL288wg+IuxwMPt8I/B8Kfe+c72vHFwD8WacxlQFBBymGe6Z3/ZUjQdgdwcud8nPe2jAoSj/obzllgpwUP17dl2qcB5wGXAD+JiBPaXpfHA49n5lOzdHUs8Cy1XUC3/20R8fd2vav/nYJ6npPZt+dp3wErGCRJkv7/GLiUJEmaEm1fw8PpBZpG6Fd7HqykuQW4ccQ9v1/g1J6rPSM+jwX0NSoj9KDe+ZLWdsWI8Xf1zic1xyVU0PL8EddHBQD3k5m7qWzHTRHxJLW35gpG/31HdjVmu2EVxJdQe15eO+KeLfOciyRJ0tgMXEqSJE2P97Rjf7nuOP4GPA0clJl3z9F2Bnh1RLwwM/uZhwOLsWR8EnZQmaV9/ezBGSrouDUzJxVcG+e9zVDFkTa3/UQn5TfteHRnnLMi4ohZsi7/RAUel9EpdtSKOC1t1+cyAxw2xndKkiRp4tzjUpIkaQpExJnUfoZbge/P9/7M3APcBqyMiBP71yPiqM7pbcCRwGVD2g0yDHe349L5zmWRzQCHR8TeJdwRcTRwbq/d7VQW5ac7zzRoH61A0HyN897WUdmfVw9p84Ih1cz7bd444tJgv8qHO3MJ9lVnHzaXn7Xjh3pNPtKOP51tLs064DURcdaQcZZGhIkQkiRp0fiPhiRJ0oG3IiJOoP4XeylwJvBmKgPunMz89wL7/ThV/OW+iPgm8BBwBFWU503td4CbqArZX46I06nlyIe2NtcDd2bmvyLiIeC8iNgCPAU8mJkPLnBuk3IrVXn7hxFxHXAI8AFqyfKg+BCZOdMqYn8eOC4i7qAyUl9GBTm/AXxpnmOP8942RsQNwJUR8SqqIvgzVNbjKuBy4AezjHFnRGyliizNdPp/G/Dr9jmZuSEibgY+GBHLqIrlS4DXARuAr2Xm7yLiRuDiFjDdCJxOVRq/o1dRfJQvAudQ+2t+jyoydChwEvBO4DiqYJEkSdLEGbiUJEk68D7bjv+hAoIPUFlx383MpxfaaWb+tQXUPkVV1b4U2A78Abii025PRJwNfBJ4N1WhejtwT5vLwGqqgvZXgIOpCtbPa+AyM7dHxLlUpexrqQzVK6nA4Km9tl9oQdcPsy8z8TEqmPijBYw91nvLzEsi4n7g/cAa4L/AH6n9RzfPMcxq4O3Au4BjqKzKR4HPAdf0CgNdSO1behEVYNxJLSm/t9ffo8B7qYDtNiqYu1818hHPvDsi3gB8ggq8XkAVVNpCvdOd4/QjSZK0ELF/8URJkiRJkiRJen65x6UkSZIkSZKkqWPgUpIkSZIkSdLUMXApSZIkSZIkaeoYuJQkSZIkSZI0dQxcSpIkSZIkSZo6Bi4lSZIkSZIkTR0Dl5IkSZIkSZKmjoFLSZIkSZIkSVPHwKUkSZIkSZKkqWPgUpIkSZIkSdLUMXApSZIkSZIkaeoYuJQkSZIkSZI0df4HoE0+zde4ucoAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -862,8 +862,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation Cg vs direct 0.9978821390094573\n", - "Spearman Correlation Cg vs direct 0.9946595460614153\n" + "Pearson Correlation Cg vs direct 0.9975120139679169\n", + "Spearman Correlation Cg vs direct 0.9968058039650353\n" ] } ], @@ -918,9 +918,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Lissa iteration: 100%|██████████| 1000/1000 [00:04<00:00, 206.59it/s]\n", + "Lissa iteration: 100%|██████████| 1000/1000 [00:02<00:00, 414.49it/s]\n", "Reached max number of iterations 1000 without achieving the desired tolerance 0.0001.\n", - " Achieved max residual 9149.03 % and 8.06354 % mean residual\n" + " Achieved max residual 125303.41 % and 17.06509 % mean residual\n" ] } ], @@ -956,7 +956,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Percentage error of Lissa over direct method:119.32581663131714 %\n" + "Percentage error of Lissa over direct method:43.32989752292633 %\n" ] } ], @@ -982,7 +982,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1026,8 +1026,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation Lissa vs direct 0.9875324674899437\n", - "Spearman Correlation Lissa vs direct 0.9758067360253924\n" + "Pearson Correlation Lissa vs direct 0.9940533986762107\n", + "Spearman Correlation Lissa vs direct 0.9942061112930448\n" ] } ], @@ -1080,8 +1080,8 @@ "arnoldi_influence_model = ArnoldiInfluence(\n", " nn_model,\n", " F.cross_entropy,\n", - " rank_estimate=30,\n", - " hessian_regularization=0.1,\n", + " rank=30,\n", + " regularization=0.1,\n", ")\n", "arnoldi_influence_model = arnoldi_influence_model.fit(training_data_loader)\n", "arnoldi_train_influences = arnoldi_influence_model.influences(\n", @@ -1108,7 +1108,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Percentage error of Arnoldi over direct method:40.1591956615448 %\n" + "Percentage error of Arnoldi over direct method:44.838228821754456 %\n" ] } ], @@ -1134,7 +1134,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABS4AAALGCAYAAABLZg+zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2G0lEQVR4nOzdd5ilZ1038O9vd1MgZQMJJCEJLEUDilIFEQKhKRK6ATSCVAH1fQ1SNPiKFFGjFBNRUECq9BAJuDSFBIJAaKETajZmQwgJSXbTy+79/vE8EyYnU86caWdmPp/rOtfZ8zz385zfnDJnz3fuUq21AAAAAACMk3XLXQAAAAAAwCDBJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQBrVlXtXVX/WFVbquraqmpVdeeqOrz/94uXu8bVpqp+vao+U1UX94/x+5e7pkFVdUpVtQU4z4v7n/Hw+Ve1clTVpv7nfvNy18IN9c/NKXNoP+Xvw4V6nyyXqR6HtfqeXQ5V9eb+sd60TPf/5P7+n7wc9w/A8ASXAKwo/ReNhfqy/PdJ/m+Sryf52yQvSfLjBTo3A/ovqCcluXWSN6Z7vN81yzG+XK5h/R8Vtix3HTOpqhtPCuLfsdz1sLqs1IBYCAzAQtmw3AUAwDJ6WJLvttYePnljVd1+mepZ7R6UZPckz22tjXPA83tJbrwA5/mndMHs/y7AuVaSc5LcIcm25S5kiTw+ycYkLcljqmrf1tpPl7mmpbBQ75NxslbfswAwtvS4BGAtu0WSc5e7iDXkFv31j5a1ilm01v63tXbGApzngtbaGa21yxeirpWitXZN/3OvlffWM5LsTPLyJLsledLylrM0Fup9Mk7W6nsWAMaZ4BKAFW/ynHr9v99VVRdU1ZVV9cWqethA+4mhd5XkfhPDz2eb922mYaszDYurqtv3tZ1dVVdX1XlV9Y6qOnSKttfN+1VVz6yqr/c/x3lV9bqq2jjN/R/cz9f5vaq6oqourKrPV9ULp2n7T1X1w6q6qqp+WlUfqKpfmennn+Z+H1dVn6qqbf39fr2qXlBVu01qc3j/eL+k33TypMf8Bo/XpONOSfKm/uabJh1z3bxokx/3qjqqqk6rqksnP0/9cPP39T/vFVW1var+p6qeMN39Dg7NnDzPX3XzoG7uhwdfXlWfrKpfm+I8U74mJl5rVbVf/5ye2z8P36yqp0xT0279+SaeszOr6mX99qHnLBx4r9y+qt7fv1Yuq6pPV9Wvz3D/x/TP7+X9Y3hqVT1upvsY2D70a3vSa+ZWSW418Ny/eVK7w6rqg1W1tX9cflxVn6uqFw3zeMxXVd0xya8m+XiSv0tydZKnz9B+4rk/oKreUFXnVNWO6qdCGPG1sa6qnlVVX+hf+5f1//6Dqhr6//pVtX9V/Vv/fFxRVV+pqmlD2KneJ9O0271/r/ykqqYc7VVVr+1/9odN2jbv57aqdq2qF1bVDwbfN9O0n+09O+Xz1re5Z1Wd0Nd5dXW/7/+1qm6RKVTVTavqr6vqG/17altVfbWqjq2qPSbeR0nuN6mGoT6rBn+WqvqdqvpSfz8/qqpXTTwGVfWA/mfbXlUXVdXbqmrfac451GdHdb9/J56nyb/vp3y91Nw+6+5W3e/zn/Q1nFVVr6mqA6dpf7uqem//s11W3RzLR8z2+AEwPgwVB2A1uVWSzyf5YZK3JblpumGcJ1XVg1prJ/ft3pzklHRfrM7qbyfJloUuqKoekuTEJLsk+WCS7yc5OMljkhxRVfdvrX15ikP/Pslv9Md8LMn9k/x+ktslecDAfdw9yUfT/byf6u/vxkl+IcmLk/zVpLZ37c930/6YE5Psl+RRST5dVY9urX1oyJ/tb5K8IMkFSd6R5NIkv5nkb5L8RlX9emvt6nSP60uSHJ7uS/hb8rPHekum9+YkFyd5ZLq5Mb8yad/FA22fm+TB6R6vk9MN3Z3w2iTfTPfYnJtk3yQPTfK2qjq0tXaDcHcGd0/yp0k+m+QNSW6Z5LeSfLyq7txa+86Q59knyf+kC7pOSNdT77FJ3lhVO1trb5loWFWV5H1JjkjyvXTDWXdJ8uQkvziH2ie7df8zfD3JvyY5MN175cNVdVRr7d2T7n/XdK+V+yU5I8k/p3t9HZnk3f3P/edzuO9hXttb0r1mnt3fPm7S8V/p63pIks1Jtif5QLoh6jdNN0z9D/OzoHwxPaO/fnNr7cKq+mCS36qqw1prp05zzE2TfC7d++XEdL01z5u0f58M+drovS3JUUnOTveabEkeneQ1Se6T5Hdn+yGqar8kn0lymySf7i8HJvmXdM/RyFprV1bVu9M9Vr+Z7nmffN+7pXvtnZfkI/22eT+3/fvmPel+f/wg3ftm1yRPTfJLI/wo0z5vVfXUJK9LclVf79lJfi5diP3wqvrV1tp1w8+r6tbpfk/dKsmX0v2OWpfk55P8SbrH/eL+53xy327yz7xlDnX/33SP+/vTfe79en8fN62qk9INjd/c1/9rSZ6Q7jPhNyefZI6fHcf12wd/309lLp91D0v3u7DSvTfOSnK3JH+Q5JFVdZ/W2pmT2v9cut9z+yb5cLrfHbfrH4sPz1ATAOOktebi4uLi4rJiLum+lLeBbZsmtid50cC+3+i3f2iac50yxfbD+30vHti+JcmWaep6cX/M4ZO23STJRemCvV8YaH/HdF+Avzyw/c39ef43yS0nbd+QLnhrSe4xafuuSc7stx81RV0HD5zj+0muTHK/gXa3SBcOnJtktyGeh3tNqvOAgfv4YL/vz2d7jIa4nyf3xzx5lsf9siR3mabNbafYtmu6XnLXJDloYN8pU7zGJl4TN6glyTP77a8Z5ueddJ43JFk/afsvJLk2ybcG2j+xb/+pJLtO2r5PuiBxytfxNI/Fpkn3//KBfXfvH4+Lkuw9afsL+vYfSrJh0vabp3tPtCS/NsV9vHk+r+0h3nPv64+50xT79hv2NTbqJd18rRemC5hu1G97WF/T26Y5ZuKxf+vkx3Ier43f6dt/Ocmek7bvkeSLmeL3wlSvl3ShVUvyD9O8Jqb6fXhKBt4nMzxWE78vTphi32P7fa9cyOc2XZjb0gVXu0/aftN0QeZUj8OLM/N79gbPW7qw8ep0v1sHf5c8MMmOJP8xsP0z/fleMNXPN1Dv0I/zND/LtiR3mLR9t3R/yNmR5KeZ9FmQLjz9r/64O0/aPufPjukey0n735y5fdbt2de7I8lhA+f6s779xwa2f6zffvTA9kdOek6fPNfH1sXFxcVlaS+GigOwmpyV5GWTN7TWPprui9E9lqGe30sXLr2otfatgbq+keT1Se5SVb8wxbEvbZN66LTWrs3Phk1P/lkeni4o+kCbYsGb1trWSTePSHLbJK9urX1yoN2P0vV8OSDdl+3ZPLW/fllr7bqV2Ps6n5uuN9K0Q2YXwetaa6dPtaO19oMptl2drufghgz38074n9bamwe2vTFdqDSX19jlSZ7TWtsxqaZvpetpd4eq2nNS2yf113/R1z3R/uJM6k07R9uSvHTyhtbaF5O8Pd1r9tGTdj013Rf85/TP70T7n0y6/7k818O+tod1xeCG1toFI5xnrh6b7o8T726tTdTwkSQ/TnJkVd1kmuOuTvK8yY/lgLm8Nibeh8e01i6d1P6ydGFOMstzU1W7pOuVeUm6sOk6k14T89Ja+2yS76brfXjTgd0Tr+/BnqTJ/J7biaH1f95au3LS8RdmtPfNdM/bH6TrAX10a+2cgVo/nq4H5sOraq+kG+qcLsj9SrrpBTJwzAWT610A/9ha+/ak81+V5N3pQsrNkz8LWms7k/x7f/NOk86xkJ8dg4b9ffDIdKHzu9sNezO/Mt0fOR5cVbdMumHt6Xrhn5mut+3kmk9K8skAsCIYKg7AavKVyV/2Jzk73RfFpTZxn3eqqhdPsf/n++s7JPnWwL4vTtH+7P56ciDyq/31MMPeJuq51TT1/NykemYbLn7X/voTgztaa9+tqq1Jbl1VG1trS7G69Oen29F/kf2zdF+qb5nkRgNNDprD/dzgeWmtXVNV5+X6z8tsvtda2z7F9snP8UQQdZd0QfBnpmj/6Tnc52Rfbq1dMsX2U9IFSXdJ8pY+bLldknPa1AuxTDz/d5nDfQ/72p7N29NNuXBaPxT55HTB8taZD+tU1T752VD0yY7rQ+HZTAwTnwhZ0lq7tqreni68f2KSf5ziuC196Dudubw27prutXHKFO0/ma532mzPze3TDf0/dZr36ilZmAWH3pLkr5P8drph7Kmq/dP1ij+9tfa1SW3n9dz2Jh6bqd4jp8y5+umft4nfq/cbnOuxd/Mk69P9vv9SfvY7+6N9ULjYpnq/TSyQ9qUp9k2ErwdP2raQnx3D1DfV74OZPnOurapPpfsj3l3S/bFy4nX/6Wn+X3BK+vlDARhvgksAVpOLp9l+bZZnQbqJBQ5+f5Z2e06x7eIptk309Fk/ads+/fU5md1EPY8doZ5BG/vr6VaOPjddSLhPut59i+3HU22sqtukCzVvkuTUdEMHt6ULdDalC2SmXKhjGhdPs/3aXP95mc95MnCujUkunKaH3nlTbBvGdMdNPI4bB65nep6Tn70Oh3HxFNum+rln1Fo7sZ/z7rnpeh4+M0mq6kvphuD+1yyn2Cc/W0BksjdPU+N1quoO6eaPPKO19rkpjn9uuvf9VMHllK/VSaa775leG1cPNu7DnAvSBWczmXiOZ3tNzNdb0/V0fFL64DJdT88NGehtuQDPbfKzx+aaKfaN8jNNd8zE79Xnz3L8xO/VffrrYX5nL4Spfv9eO8S+XSZtW8jPjkEXz1DD4Gs9Gf530VK9rgFYZIJLABjeznRzI05lnym2TXwpvNNAb6KFdHF/PUyvwYl6Htla+8A873fiXAekmy9u0IED7RZbm2b7c9J96X7K4BDvqvqdLExPssW2Pd1CGhumCC/3H/Gc0x13QH+9beD6gCnaJkv/PF9Pa21zks1VtUeSe6abY/IPkvxnVd1lcIqGgWO3pFvkYxQTvS1vP91KyUnuWFW/1lob7Ck7XftRbEv32thlMKCrbgXv/dK9fmY7RzL7a2JeWmtbq+oTSR5UVbfve/A+Kd0cmlNNczHyc9ub9rHJaD/TdM/bxOO3cZqesoMu7q/n0tN7uS3kZ8d8axj2d9GSvK4BWHzmuASA4V2UZP9+TrhBd59i20RPrMMWr6Tr7uM3Z2x1/bYLUc/EfJKHD+6oqtulG2Z45pBDbmcyMcRvLr0ZJ7tdf/2+KfatlGGCp6f7P9uvTbHvPiOe864Tc+4NOHzSfaYfTv6DJAf1K/QOun9//eUR65jNjgzx3LfWLmutfaK19px0q9rvmuHeE3PWr4L9xHR/yHhjkn+b4vLRvvlsva3na+K1cd8p9t033WM323NzRrp5Ne9cVRun2H/4fAoc8Ob++klVdeckv5zkw62186c7YB7P7ZfTPTZTvUcOn0PNs5nr79WJ9r9RVcN8F9uRJFU16u/AhTDKZ8d8f3cPmukzZ0N+VtuXB9rfZ5rH7gbnAWA8CS4BYHifTzda4SmTN1bVk5Pce4r2b0rXu+ZFVXWDRUeqal1VHT7Pmj6YblGCR/Q9CAfvY/I8ZSelC6H+qKoeOtXJqupeVXXjIe73jf31X1TVzSYdvz7JK9L9H+PfhvoJZvbT/vqWIx6/pb8+fPLGqvqNLO3iQfPx1v76ZVV1XY/fPmR64Yjn3JjkLydvqKq7pxu6uy3Jf0za9cZ0PRNfPjkAqKr9Jt3/G7M4fprkZlU1OC9pquq+fWAxaKKH1eWLVNNvpevF+9HW2tNaa08fvCR5XLqV7h83TRi4UCYe97+d/L7t/31sf3PG92HfG/HtSfbKwOI8k14TC+XEdD1An5Dkyf22Nw82WqDndmLu0b+uqt0nnfumSf5iyHqH8U/peo3+Q1X9/ODOqtq1qq4L/FprX0o3X+2d87MFlCa333dyvZn/78CFMMpnx0LX/f4kFyb5nar61YF9z05y6yT/PbHQTz8f6n/12//PQK2PzMr5wxXAmmeoOAAM79XpQsvXVtUD0y0gcOd0Cxf8Z7qhjNdprf20qo5MFwJ9rqo+nuSb6YYcHtIft2+SyV9S56S1dnVVPTbd3I3vqKpnpusds3u6hRIemP7zvl9E5jHpeoNtrqrPpFvZ9vK+nl9Jcpt0Q+5mDAZaa5+pqr9P8qdJvlFVJ6QLan4zyR3TLYjx8lF/rkk+29fy7KraNz+bl+zVQy7685p0z9l7+xp/1Nf3kCTvSfL4Bahxsb013YImD0n3WH8g3fxzv5XkC0kOTdf7by4+leTpVXXPdKtVH5jusViX5JkDQ15fke55fWSSr1bVh9It5vLYdPMn/n1rbdRFgmbz8XSvy4/0i29cleSrrbUPpps/8qCq+p90AfXVSe6W5AFJzkryrkWqaWKY+Buma9Ba215V700Xzj0h3Qr2C6619o4+hHlckm9W1fvT/X55VLrA5t2ttWFWBf/zdL8rnt2HlZ/Oz14TH0ryiAWq94r+cXlakj9MF25tnqLpQjy370xX/yPSvW9OSve+OTLd++a28/pheq21M6rqqelC5G9W1UfSraC+S7rQ7rAk56dbBGnCE9ItDvM3VfVb/b8r3SI3v9633dK3/Xi699qJ/XvviiRntdbethD1D2PEz46T0/1e+tuqumO6EQtprb1sxBou7R/n9yb5ZP86+t90r4tfT/fZ8MyBw/4o3WfIcVX160m+mq4X/qPT/dHv4aPUAsDSElwCwJBaa9+qqgelG6748HQLCJyaLoB8TAaCy/6Yj1fVLyd5XrrVcw9L9yX8R+lWR51qCPNc6/piP+zymHQB068luSTJ9zPQq6619rWqulO6uR8fli7U25luYYPT0y1WcsGQ9/tnVXV6ut4sv5fui/oP0vVmeuVUC4aM8LNd1H+xf1G6EGiPfte/Z4h5Ffuf9/5JXpbkiHT/9/lquufr4qyA4LK11qrq0enCpScm+b/pnq+3pAtmH5XZ5zEcdGaSZ6XrlfesdAsUfTnJS1trH53csA/HH5zuNXNUf//Xpnscn91ae+doP9lQXpZu/tiHp+vVvD7dz/3BdO/DR6ebpuFB6V7H/9tvP661dtFCF9P3qLtfugU/PjhL89ene83+fhYpuOz9TroVxK9bxCbJt5O8MslrhzlBa+2Cqrp3fva77e5JvpNuTsktWaDgsvfmdMHlLkneOc3viXk/t/375rHpfi8+Od3vqXPT9cR8aZIr5/uDTLqvf6+qr6ZbTOj+6YK0y9L9nj8hybsH2p9ZVXdN94efR/W1XZnusX5lksmrl78hya3S/fHiT9P9DvtkkiULLpO5f3a01r5dVU9K99n3h/nZH+hGCi77c57Uv07/PN3n6cZ0geW/JPmr1tqPBtp/r++deWy619HhSb6W7jG/WQSXACtCtbaQ84MDALBU+kDxY0mOba29YIj2m9KFlm9prT15casDAID5McclAMCYq6pbTLFt3/xsHsP/GNwPAAArnaHiAADj71X9MM3PpJsv7+B00wLcNMm/ttY+v5zFAQDAYhBcAgCMvxPTraj88HRzPl6ZbqGnf8vCrN4OAABjxxyXAAAAAMDYMcclAAAAADB2VnRwWVX3raoPVtWPqqpV1aOGOObwqvpyVV1VVd+vqicvfqUAAAAAwFys9Dku90jy1SRvTDf304yq6tZJNif5lyS/m+SBSd5QVee21j46zB1WVSW5RZJLRi0aAAAAANa4vZL8qM0wj+WqmeOyqlqSR7fW3j9Dm79LckRr7Y6Ttr0ryT6ttYcMeT8HJdk6z3IBAAAAYK07uLV2znQ7V3qPy7m6V5L/Htj20STHTXdAVe2WZLcpdh0cvS4BAAAAYK72StcxcMZsba0FlwckOW9g23lJ9q6qG7XWrpjimBckedEU2y9prW1f6AIBAAAAYDXrZmKc3YpenGeJ/G2SjZMuBy9vOQAAAACw+q21Hpc/TrL/wLb9k2yfprdlWmtXJblq4vawiTAAAAAAMLq11uPys+lWEp/swf12AAAAAGBMrOjgsqr2rKo7V9Wd+0237m/fst//t1X11kmH/EuS21TV31fV7avqD5M8Lsk/LG3lAAAAAMBMVnRwmeTuSU7vL0nyqv7fL+1vH5jklhONW2tnJjkiXS/LryZ5bpKnt9Y+ulQFAwAAAACzq9bactewolTV3km2JdloVXEAAAAAmJth87WV3uMSAAAAAFiFBJcAAAAAwNgRXAIAAAAAY0dwCQAAAACMHcElAAAAADB2BJcAAAAAwNgRXAIAAAAAY0dwCQAAAACMHcElAAAAADB2BJcAAAAAwNgRXAIAAAAAY0dwCQAAAACMHcElAAAAADB2BJcAAAAAwNjZsNwFAAAAAACdTcdsXp/ksCQHJjk3yalbjj1ix/JWtTz0uAQAAACAMbDpmM2PSbIlyclJ3tFfb+m3rzmCSwAAAABYZn04eUKSgwZ2HZTkhLUYXgouAQAAAGAZ9cPDj+9v1sDuidvH9e3WDMElAAAAACyvw5IcnBuGlhMqySF9uzVDcAkAAAAAy+vABW63KgguAQAAAGB5nbvA7VYFwSUAAAAALK9Tk2xN0qbZ35Kc3bdbMwSXAAAAALCMthx7xI4kR/c3B8PLidvP7tutGYJLAAAAAFhmW4494sQkRyY5Z2DX1iRH9vvXlGptuh6oTKWq9k6yLcnG1tr25a4HAAAAgNVj0zGb16dbPfzAdHNanrraeloOm68JLudIcAkAAAAAoxs2XzNUHAAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYOxuWuwAAAAAAWEk2HbN5fZLDkhyY5Nwkp2459ogdy1vV6qPHJQAAAAAMadMxmx+TZEuSk5O8o7/e0m9nAQkuAQAAAGAIfTh5QpKDBnYdlOQE4eXCElwCAAAAwCz64eHH9zdrYPfE7eP6diwAwSUAAAAAzO6wJAfnhqHlhEpySN+OBSC4BAAAAIDZHbjA7ZiF4BIAAAAAZnfuArdjFoJLAAAAAJjdqUm2JmnT7G9Jzu7bsQAElwAAAAAwiy3HHrEjydH9zcHwcuL2s/t2LADBJQAAAAAMYcuxR5yY5Mgk5wzs2prkyH4/C6Ram653K1Opqr2TbEuysbW2fbnrAQAAAGBpbTpm8/p0q4cfmG5Oy1P1tBzesPma4HKOBJcAAAAAMLph8zVDxQEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOwILgEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOwILgEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOwILgEAAACAsSO4BAAAAADGzoblLgAAAACAxbXpmM3rkxyW5MAk5yY5dcuxR+xY3qpgZnpcAgAAAKxim47Z/JgkW5KcnOQd/fWWfjuMLcElAAAAwCrVh5MnJDloYNdBSU4QXjLOBJcAAAAAq1A/PPz4/mYN7J64fVzfDsaO4BIAAABgdTosycG5YWg5oZIc0reDsSO4BAAAAFidDlzgdrCkBJcAAAAAq9O5C9wOlpTgEgAAAGB1OjXJ1iRtmv0tydl9Oxg7gksAAACAVWjLsUfsSHJ0f3MwvJy4/ey+HYwdwSUAAADAKrXl2CNOTHJkknMGdm1NcmS/H8ZStTZdb2GmUlV7J9mWZGNrbfty1wMAAAAwm03HbF6fbvXwA9PNaXmqnpYsl2HzNcHlHAkuAQAAAGB0w+ZrhooDAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNnxQeXVfVHVbWlqq6sqtOq6h4ztH1yVbWBy5VLWS8AAAAAMLsVHVxW1eOTvCrJS5LcNclXk3y0qm4+w2Hbkxw46XKrxa4TAAAAAJibFR1cJnlOkte31t7UWvtWkmcluTzJU2c4prXWfjzpct6SVAoAAAAADG3FBpdVtWuSuyX574ltrbWd/e17zXDonlV1VlWdXVUnVdUvznI/u1XV3hOXJHstRP0AAAAAwPRWbHCZZL8k65MM9pg8L8kB0xzznXS9MR+Z5Anpfv7PVNXBM9zPC5Jsm3TZOo+aAQAAAIAhrOTgcs5aa59trb21tfaV1tonkzwmyflJnjnDYX+bZOOky0whJwAAAACwADYsdwHzcEGSHUn2H9i+f5IfD3OC1to1VXV6ktvN0OaqJFdN3K6quVcKAAAAAMzJiu1x2Vq7OsmXkjxwYltVretvf3aYc1TV+iS/lOTcxagRAAAAABjNSu5xmSSvSvKWqvpiks8neXaSPZK8KUmq6q1JzmmtvaC//ZdJPpfk+0n2SfL8JLdK8oalLhwAAAAAmN6KDi5ba++uqpsleWm6BXm+kuQhrbWJBXtumWTnpENukuT1fduL0vXY/LXW2reWrGgAAAAAYFbVWlvuGlaUqto73eriG1tr25e7HgAAAABYSYbN11bsHJcAAAAAwOoluAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLGzYbkLAAAAAJbHpmM2r09yWJIDk5yb5NQtxx6xY3mrAujocQkAAABr0KZjNj8myZYkJyd5R3+9pd8OsOyqtbbcNawoVbV3km1JNrbWti93PQAAADBXfTh5Qn+zJu2aCAmO3HLsESdOc6xemsC8DJuv6XEJAAAAa0gfPB7f36yB3RO3j+vbDR6rlyawZASXAAAAsLYcluTg3DC0nFBJDunbXWdSL82DBtoflOQE4SWw0ASXAAAAsLYcONd28+mlCTAqwSUAAACsLeeO0G6kXpoA8yG4BAAAgLXl1CRb87OFeAa1JGf37SbMuZcmwHwJLgEAAGAN6VcAP7q/ORheTtx+9sBK4aP00gSYF8ElAAAArDFbjj3ixCRHJjlnYNfWJEf2+ycbpZcmwLxUa9P9zmEqVbV3km1JNrbWti93PQAAADCqfjGdw9IN8T43yakDPS0nt51YVTy5/lyXE8HCVIEnwA0Mm68JLudIcAkAAMBa1YeXx6dbqGfC2emGlgstgaEMm69tWLqSAAAAgJVoUs/M3ZI8qd+8f2bppQkwH4JLAAAAYFrT9LLcmuToLcceccqyFAWsCYaKz5Gh4gAAAGvbXOaFXOnMawkshmHzNauKAwAAwJD6IG9LkpOTvKO/3tJvX1X6gPb4/mYN7J64fVzfDmDBCS4BAABgCJN6Hx40sOugJCeswvDysHTDwwdDywmV5JC+HcCCE1wCAADALNZo78MDF7gdwJwILgEAAGB2a7H34bkL3A5gTgSXAAAAMLu12Pvw1HSrh0+3qm9LcnbfDmDBCS4BAABgdmuu92G/UvrR/c3B8HLi9rNX64rqwPITXAIAAMDs1mTvwy3HHnFikiOTnDOwa2uSI/v9AIuiWpvudy5Tqaq9k2xLsrG1tn256wEAAGBpTFpVPLn+XJcTX6xXbZDXLzp0WLqh8OcmOVVPS2BUw+Zrgss5ElwCAACsXX14eXy6hXomnJ1uyPSSh5YCRWAlElwuEsElAADA2jYuYeE0IerWJEev1p6fwOoguFwkgksAAACW21oetg6sfMPmaxbnAQAAgBWk7/F5fH+zBnZP3H7tpmM277J0VQEsPMElAAAArCyHpRsePhhaTqgkN09yTt8zE2BFElwCAADAynLgkO32S3KC8BJYqQSXAAAAsLKcO2S7iR6Zx/XDywFWlA3LXQAAAAAsh3FZHXwEp6ZbPfygTD9cfEIlOSTdz3nK4pYFsLD0uAQAAGDN6YdPb0lycpJ39NdbVsKw6j5cPbq/2WZqO8mww8sBxobgEgAAgDWlDydPSNdjcbKDskLmhNxy7BEnJjkyyQVDHjLs8HKAsSG4BAAAYM3oh4cf398cHGa9ouaE7MPLg5Kcn+l7XrYkZ6cbXg6wogguAQAAWEsOS3Jwpp8bcvKckGNvy7FHXJPkWf3NwfBy4vazV8jcnQDXI7gEAABgLRl2rscVMyfkpGHj5wzs2prkyH4/wIpjVXEAAADWkmHnelxRc0JuOfaIEzcds/mkLMEq6St4NXZghanWhl2AjCSpqr2TbEuysbW2fbnrAQAAGIawqdM/Ducl2XeGZhckOWAtPj6z6RcuOj7dcPsJW5McrWcnMKxh8zU9LgEAAFa56cKmTcdsFjaNoXENmSetxj5oYjV2w9KBBWWOSwAAgFVsUth00MCuibDpMUtf1bI6LDP3tkyS/bJMi/P0z8eWJCcneUd/vWW5n6fVtBo7sHIILgEAAFYpYdOUxnZxnjEPmVfVauzAyiC4BAAAWL2ETTc0lovzrICQeWwDX2D1ElwCAACsXsKmGzo13WIy061U25Kc3bdbSuMeMo9l4AusboJLAACA1UvYdEOPTHKjTB0QToSZz16GxXDGPWQe18AXWMWsKg4AALB6TYRNB2X6oG5rVmnYNMXq3PsleU+m79X40yTPXKaVscc6ZN5y7BE7Nh2z+eh0c3C2XP8xXM7AF1jF9LgEAABYpfoQ6ej+5mBPuVUdNk2zOve7M31o2ZJckeSkpahvCmPfo7EPdI9Mcs7Arq1JjlymwBdYxQSXAAAAq9haDJtmWJ17pu/AyzqH5EoJmfvXy6Yk909yVH9969X4OgKWX7U23R9zmEpV7Z1kW5KNrbXty10PAADAMKYYNn3qcodggxaixv4cWzL98PjZHLXl2CPeOcJxC6IPXY9Pt1DPhLPThZbCQWBVGDZfE1zOkeASAABg4U0T2G1NcvRcArtNx2w+PN2w8FE9cMuxR3xiHsfP20oImQHmY9h8zeI8AAAALKtJQ7sHHZTkhE3HbJ7LkPblWnV7wfQh5SnLXQfAcjPHJQAAAMum7114fH9zcGj3xO3j+nbDmO+q2/vP83gAFogelwAAACyYEYY5H5brDw8fNHnRnFOGKOF/kuxIMmzQOWi+wScAC0RwCQAAwIKYbp7KTcdsnmmeymGHdk/bbiAs3T+jhZYt3Zyap45wLACLwFBxAAAA5m3SPJUHDeyamKfyMdMcOmwPxynb9efdkm5Bnnck+YchzzeVZ1sEB2B86HEJAADAvAwxT2VL8tpNx2zePcmPcv3h46em6+l40BTHJjP0hJxhUZ9RvGguq5cDsPgElwAAAMzXMPNU3jzJ2/vb1w0f33LsETs2HbP56HQBZMv1w8vWXz+7bzd5SPh5mT4sHcX3F+AcACwgQ8UBAACYr2HnqZxwveHjfU/HI5OcM9Bua5Ijtxx7xIlTDAn/eLqwdCFCy8SiPABjR3AJAADAfM019JsIG4/re1FOhJebktw/yVH99a0nhZZTzZ+5EFqSs2NRHoCxY6g4AAAA8zXbPJVTqSSHpBv6fUqS9PNenjK50SzzZw7jT5Lcsr+ecSj6COcGYBEJLgEAABjawDyT56ZfaGeGeSpnM9sw89nmz5zOxKI+r+7r+3S6AHTyubamCy0tygMwhgwVBwAAYChTzDN5cpItE3NVJrkwc+8VOdsw87nOn5lM0ZNypqHoI5wfgCWgxyUAAADXmaFH5cQ8k4MOSvK+/CwsnIsLMvvckqMsmjNlT8qphqIDML4ElwAAAGvQVAFlkkdmiuHUm47Z/CdJ/qG/PdijsqbZvlBmmz9zYkj4k5Psn0lh6yLVA8ASqdZG+aPY2lVVeyfZlmRja237ctcDAAAwV33vycGAcnuSvadoPvGlcbGCyftvOfaIU2ZqMNDbc6rFdY405Btg5Rg2XzPHJQAAwBoyKQQ8aGDXVKFlsniB5YRZ57DsQ8kjk5wzsGtrhJYAq5ah4gAAACvcdPNSTrH/FkmO6zfPJZBczPByqDkstxx7xImbjtl8Umb4OQFYXQwVnyNDxQEAgHEyzbDvrUmO7sO+qfaPqmXhQsyJuSlvLXwEWFuGzdcEl3MkuAQAAMbFEHM/vjzJ86fYPx8LEV6amxJgDTPHJQAAwCrWD/8+vr853Urfz51m/yjOTvLiJBctwLnMTQnArMxxCQAAsMRmm5NySIdl5uHflWT9aBVOaWO64HLChemC099Pt9DPsOHonyR5teHhAMxGj0sAAIAl1A/v3pLk5CTv6K+39Nvn4hYLXNps9hq4fZN0QeY7+tuzzUPW0vXaFFoCMBTBJQAAwBKZNCflQQO7DkpywrDhZd/uuIWtblbTDUf/nSSPS3LODMdOhJrPFloCMCyL88yRxXkAAIBR9MPDt2T6YdXTrrI9MLT8dkle0u+aaXh2S7IzXYeVhVqYZzr3T3JquhofkeQJSW42af/Z6UJLc1oCMHS+Zo5LAACApTHMnJSH9O1OmdjY9648fpZjB030UHlVkudlYVYCn8mBfdh6SpJTNh2z+fmZ/xyeAKxxgksAAIAFMMSCOwcOearr2k0aWj5X5yd5c5Kjsvi9LZPu573OpBATAEZmjksAAIB5GnLBnXOnOHQq5/bnXJ+up2Uy9/DxHUmenxvOpbnQJhbcOXWR7weANUhwCQAAMA9zWHDn1HRzWE630MBgCDgxtHyUHpNP7q8Xs7elBXcAWFSCSwAAgBHN0ity4vZxm47ZvL4P947utw2Gl1OFgMMOLZ/KPlPUs9C2JjnSgjsALBbBJQAAwOhm6xU5ecGd9CHfkUnOGWg3VQg47NDypdSS/DTJA9Otfi60BGDRWJwHAABgdHNecGfLsUecuOmYzSflZwv5nNfv2n/TMZsPz88W9Tk1yYVJbrpw5c7J4ErkE71Cn7Hl2CM+sQz1ALDGCC4BAABGN6cFdyZMrLq96ZjNj03yvnRDuydcuOmYzccl+XaW9jvbRDD54iTfSvIP6XqTTtiabii7XpYALIlqbbp5oZlKVe2dZFuSja217ctdDwAAsHz6OS63pFuIZ6rh4i1d4PfkJPunCzBP3XLsETs2HbP5P5I8apa7GOz1ONf9c3F2JgWT/c820Sv0uroX6L4AWMOGzdcEl3MkuAQAACabtKp4csOh1ZVuTsh9J23fmuSLmT20XExnJ3lOkgsimARgiS16cFlVt0zy50nun+RmSR7VWvtUVe2X5C+TvKm1dvpIJx9jgksAAGBQH14en+sPrb4gPwsspwo0F8IF/fluNkTbf07ymSQ/ipASgGW0qMFlVf1Cuomi1yU5LcmDkzy4tfaJfv+Xk5zeWnvaCLWPNcElAAAwlYGh1ecleUumH0K+kN6b5LFDtDtqy7FHvHORawGAWQ2br4060fPfJ7k4ya+m++veTwb2b07y+BHPDQAAsOJMLLiTJP3q4AfP1H4BnTFku2EXEgKAsbBuxOPum+S1rbXz87OV5yb733R/WQQAAFiLDlzC+zol3byZ0w2na+nmtDx1qQoCgIUwanC5LsnlM+y/WZKrRjw3AADASvdzS3AfE4HkJ5McPWnbYJukWy3cnJYArCijBpdfTnLEVDuqakOS307yuVGLAgAAWKk2HbP5yHQLli6m6wWSW4494sQkRyY5Z6Dd1iRH9vsBYEUZNbj82yQPqarXJrljv23/qnpQko8luUOSYxegPgAAgBWjX138PUnWD9H82/O4qxsEkv2/NyW5f5Kj+utbCy0BWKlGWlU8SarqiUmOT7Ix3Sp5rb/enuQPWmurcrU6q4oDAABT6VcV35LhVxJvSV6Z5Ln97dmOuTzJ7yf5UZJTDf0GYKVa7FXF01p7W1WdmOTB6eZvWZfkB0k+2lq7ZNTzAgAArFCHZe4riT8+yeOS/MMMx070Nnmi3pMArCVzDi6r6sbpJoA+trX28iTvX+iiAAAAlkvfc/KwdCuDn5vktHQ9Ix+WZM9034femuQftxx7xDWTDp3rSuKV5JAkF6Qb4n1YkkckeUK6BU8nbE03l6XQEoA1Zc5zXLbWLk9ybZLLFr6cuauqP6qqLVV1ZVWdVlX3mKX9Y6vqjL7916vqoUtVKwAAMN76OSq3JDk5yTv668uT/EG6kPEmSX45ySuSXLnpmM1/N+nwc0e82wP7BXZO2XLsEc9JF4CapxKANW/UoeLvS3JkVb22jTpJ5gKoqscneVWSZ6X7K+izk3y0qg5trf1kiva/luSdSV6Q5D/T/Ufg/VV119baN5ascAAAYOz0q4G/dw6HrEvyp5uO2Zwtxx7xZ0lOTdc7ctg5LidcL/Ds5648ZQ7HA8CqNNLiPFV13ySvSTek4fXp/iJ5xWC71tqX51nfbHWcluQLrbX/099el27YxqtbazdY1byq3p1kj9bawyZt+1ySr7TWnjXkfVqcBwAAVpk+tHxXhlsNfNCOJDfacuwR1/Q9Nk/ot88WXrZ0QeetLbQDwFoybL4256HivVOS/EKS+6ab2+VTSb4w6fLF/nrRVNWuSe6W5L8ntrXWdva37zXNYfea3L730Rnap6p2q6q9Jy5J9ppX4QAAwFjpw8b3ZLTQMv1xf5Qk/ZDuI5OcM8sxEz1Ini20BICpjTpU/CkLWsVo9kv3H4TzBrafl+T20xxzwDTtD5jhfl6Q5EWjFAgAAIyvfhGe+6UbRTaXod1Tue3EP7Yce8SJm47ZfFJ+tsDP7ZI8I9dfNdyCOwAwi5GCy9baWxa6kDH2t+nm0ZywV7r/ZAAAACtU38vy+Fw/TJyP6wWfg/NUbjpm89/k+iuVn6qnJQDMbNQel9epqj3Tra6XJGe31i6d7zmHdEG6uWT2H9i+f5IfT3PMj+fYPq21q5JcNXG7ar5/iAUAAJbKpF6Vh/ebTkly03RDwxfSZ2faacEdAJi7Uee4TFX9SlWdnOSiJN/oLxdV1Seq6u4LVeB0WmtXJ/lSkgdOqmldf3u6/zR8dnL73oNnaA8AAKxQfa/K85J8PMkL+8vHk7y7b7KQvRJmm9MSAJijkXpcVtU90/218Ookb0jy7X7XHZL8TpJPVdXhrbXPL0SRM3hVkrdU1ReTfD7Js5PskeRNfZ1vTXJOa+0Fffvjk3yyqp6bZHOS305y93TzzQAAACtA34tyxmHXk1b3niqcHLkDxxQmVgY/dQHPCQAkqdba7K0GD6r67ySbktyntfbjgX37J/mfJGe21h68EEXOUsv/SfL8dAvsfCXJH7fWTuv3nZJkS2vtyZPaPzbJy/r6v5fkT1trH5rD/Q21XDsAALDwppmbcmuSoycWuumDzS1ZuPkrpzPxZepIi+wAwPCGzddGDS4vSfLS1trLp9n/p0le2Frba84nH3OCSwAAWDjD9J6c1HaiF2Vy/Z6U1wsQNx2z+fAkJy9CuW3gfs+OlcEBYM6GzddGHSKxMzMPM1/ftwEAAJhSH0RuSRcyvqO/3tJvH2y7Pl1Py+SGw78nbh/XtztwgUv9aZK/TLJbkvsnOaq/vrXQEgAWz6g9Lj+c5JeS3Lu1dtbAvlumGyr+9dbaQxekyjGixyUAANzQXHpO9u2H6j05qf3hGa4X5f376/n2uJzoXfmXSf5mpp8FAJibxR4qfpckn0rX6/I/kny333VokkcmuTbJYa21r8755GNOcAkAANc3zLyTA+0n5qA8KFMvnjOx4M2tJwLDTcds/p10vTJnc1S6QPScJDebpe2OdKPFpmIYOAAskmHztZFWFW+tnd6vLP7XSR6R5Mb9rsuTfCTJX7TWvjXKuQEAgJVjoOfkZAclOWHTMZunWrjmsMy8cE4lOaRvd0q/7dwhS7pdkh9m9tAySX47yQXpeome12/bP0P0GAUAFt9IwWWS9MHko6tqXX72n4LzW2vmtgQAgDVgiHknW7p5J08aCAGHnYNycrtT0/XCnKmX5k+TvGSI816Q5Jl6UwLAeBt1cZ7rtNZ2ttbO6y9CSwAAWDsmek5OFSQm1+85OdmwvSeva9cHn0f3N6ea76rSLZ4z8e9BLd2QtAclOUBoCQDjb6TgsqpeVlVfmWH/6VX1opGrAgAAVoKhek7ufeWlB6fqMak6JlV/esYrHr1LWtuaqQPI9NvPTtfL8jp92PjyTB+U7jXDvkqyMckOQ8ABYGUYtcflkUk+PMP+DyV5/IjnBgAAVoaZe062lqd94f354j894R+TvO+q9Rv+37W17i9333HNx75+3OPWPeQ7n0luGF5O3H72YMDYD00/aopj5mLYYeoAwDIbNbi8ZZIfzLD/zCS3GvHcAADAyjAx7+QNg8TW8qKPvy4v/MQb8oE73G/XBz/1n3Po896/5+2ef9IeR/32X1+8bfc9z/2X9/9NPfHL/3nxwJFbk0y1oE8y+9D0YQw7TB0AWGajBpeXZuZg8tZJrhzx3AAAwAow07yTD/r+ae0pX/pg/uLBf9Ced8Sf7PG9m/VfH6rymVvdaZ/7POuNd/vUpruc/tL/+pe9f/srH3lCup6U909y6xnmn5xPb8kph58DAOOrWpv7KIuqek+Sw5PcpbV2zsC+Q5J8OcknW2tHLkSR46Sq9k43qffG1tr25a4HAACW26ZjNj8m3eriB09se9c7jrly92uv3vCo33vVhumO2+2aq/L14x9/6a47rv3XtPa8Ie7n8CQnD1FSy/V7ZU586ZmuJycAsISGzddG7XH5wnQr9n2zql5ZVU/tL69K8vUku/ZtAACAVa4PAzel6zF51D3O/sYjf/Xsb+z+9jv/5rShZZJctctuefcv//q6Nvz8+NMPTe+0JBckOWdg+0zDzwGAMTXjfySm01r7TlUdluTVSf5kYPenkvxxa+3b8y0OAABYGfph46ckSephP58kZ91k9pHd39/3kBu31C7DTFq55dgjdmw6ZvPRSU7I9L0qn5nkpHTzYR6Ybk7LU60kDgArz0jBZZK01r6W5H5VtV+S2/Sbf9hau2BBKgMAAFaqS5Jkv8sunrXhzS67KFev33DF7kOeeMuxR5y46ZjNR2ZgaHq6XpXPntSr8pShqwUAxtKoQ8Wv01q7oLX2+f4itAQAAH7cki/99lc/evVMjdbt3JFHf+PknLfXvp+ey8kHh6Zn9kV9AIAVaOgel1V1QJKfT/Ll1tqlk7bvkm4+y99NNxTjjCQvbq19YIFrBQAAVoLWWlW95rAtp7/hvj/8Uj51m7tN2expXzypHXTJ+XXZLru/eK53cb2h6QDAqjSXHpfHJHlvksG/mr4yyV8kuUmSbyY5NMn7quq+C1IhAACwEr21ks1vfN9LdzztC+/PXldddt2Om1/y0/z5J/4t/+/kN9aXb3Hof+xx9RVfWMY6AYAxVa1NtyDfQMOq05N8qbX29EnbbpZususzktyntXZxVd0qyWeTfKG19shFqHlZDbtcOwAArHlVuyV59c7kaVfuslt98+a3rV12Xps7/vj7uXr9Lu30Wxz6jnv/79eemGG/lAAAq8Kw+dpcFuc5JMlbB7Y9LF2vzVe01i5OktbaWVX1piRPm1PFAADA6tLaVUmesa7qJbtde81TDrj0p/e5bNfdd//8IXf8ys0vvfAl9z7rqxctd4lJsumYzetjFXIAGDtzCS53T3LpwLbDkrQkHx/Y/oN0Q8cBAIC1rrVz1icvO2S565jCpmM2PyZTrFC+6ZjNR1vsBwCW11zmuDwzyZ0Htt0/yVmttbMHtu+Z5MJ51AUAALCo+tDyhCQHDew6KMkJ/X4AYJnMJbg8McmTqurxVXVIVf2/JLdK8p4p2v5qkh8uRIEAAAALrR8efnx/swZ2T9w+rm8HACyDuQwV//skD0/yznTDwyvJd5L89eRGVbVvkkckefkC1QgAALDQDsv1h4cPqnTz/B+W5JSlKAgAuL6hg8vW2mVVdY8kj05ymyRnJXl/a+3KgaYHJXlRuiEXAAAA4+jABW4HACywufS4TGvt2iTvnaXN15J8bT5FAQAALLJzF7gdALDA5jLHJQAAwGpxapKt6abBmkpLcnbfDgBYBoJLAABgzdly7BE7khzd3xwMLyduP7tvBwAsg2ptuj8wMpWq2jvJtiQbW2vbl7seAADWjn6F68PSzbt4bpJTBWvzs+mYzY9Jt7r45IV6zk4XWp64PFUBwOo2bL4muJwjwSUAAMthmoBta5KjBWzzIxAGgKUluFwkgksAAJZaH1qe0N+sSbsm/jN/pPASAFgphs3X5j3HZVUdWFV3qqo95nsuAADg+vregMf3N2tg98Tt4/p2AACrxsjBZVU9sqrOSDc85ctJ7tlv36+qTq+qRy1MiQAAsKYdlm54+GBoOaGSHNK3AwBYNUYKLqvq4UlOTHJBkpdk0n+iWmsXJDknyVMWokAAAFjjDlzgdgAAK8KoPS7/MsmnWmv3SfLPU+z/bJK7jFwVAAAw4dwFbgcAsCKMGlzeMcl7Zth/XpKbj3huAADgZ05NNz3TdKtqtiRn9+0AAFaNUYPLy5PMtBjPbZL8dMRzAwAAvS3HHrEjydH9zcHwcuL2s/t2AACrxqjB5clJnlRVGwZ3VNUBSX4/ycfmUxgAANDZcuwRJyY5Mt1c8pNtTXJkvx8AYFWp1qYbcTLDQVWHJvlcki1J3pvkr5K8Isk1SZ6ZbrGeu7fWtixUoeOiqvZOsi3Jxtba9uWuBwCAtWPTMZvXp1s9/MB0c1qeqqclALDSDJuvjRRc9nfwi0mOT3L/TFpVPMkpSf6otfbtkU485gSXAAAAADC6YfO1Gwz1HlZr7ZtJHlRVN0lyu3TDzn/YWjt/1HMCAAAAACTzCC4ntNYuSvKFBagFAAAAACDJiIvzVNUfV9VHZ9j/4ar6g9HLAgAAAADWslFXFX9akm/NsP9bSZ4x4rkBAAAAgDVu1ODytklmWnznjL4NAAAAAMCcjRpcXp3kgBn2H5hk54jnBgAAAADWuFGDy88leXJV7TW4o6o2JnlK3wYAAAAAYM5GXVX8JUk+meQrVXVckm/22++Y5NnpelweNd/iAAAAAIC1aaTgsrV2WlU9PMm/Jjk+Set3VZIzkzyitfbZhSkRAAAAAFhrRu1xmdbaf1XV7ZLcJT9biOcHSb7cWmvTHwkAAAAAMLORg8skaa3tTPKl/gIAAAAAsCDmFVxW1S8kuU2Sm6QbJn49rbW3zuf8AAAAAMDaNFJwWVW3TfLvSe6RKQLLXksiuAQAAAAA5mzUHpf/muSX0q0gfmqSixaqIAAAAACAUYPLeyf5m9baqxeyGAAAAACAJFk34nEXJNm2kIUAAAAAAEwYNbj8lyRPqKr1C1kMAAAAAEAy+lDx7yZZn+SrVfXGJGcn2THYqLV24jxqAwAAAADWqGqtzf2gqp1DNGuttVXXI7Oq9k43TH5ja237ctcDAAAAACvJsPnaqD0u7z/icQAAAAAAsxopuGytfXKhCwEAAAAAmDBqj8skSVXtluSuSW6e5H9aaxcsSFUAAAAAwJo26qriqao/TnJukk8nOTHJL/fb96uqC6rqqQtTIgAAAACw1owUXFbVU5Icl+QjSZ6WpCb29b0uP5HktxegPgAAAABgDRq1x+Vzk5zUWjsqyQen2P+lJL84clUAAAAAwJo2anB5uyQfnmH/hUn2HfHcAAAAAMAaN2pweXGS/WbY/wtJfjziuQEAAACANW7U4PJDSZ5RVfsM7qiqX0zy+0k+MI+6AAAAAIA1rFprcz+o6hZJTku3KM8Hkzwjyb8nWZ/kt9KtNn6PfqGeVaWq9k6yLcnG1tr25a4HAAAAAFaSYfO1kXpcttZ+lORu6VYVf3y6APOJSR6e5J1JfnU1hpYAAAAAwNIYqcflDU5SdbN0Iej5rbWd8z7hGNPjEgAAAABGN2y+tmEh7qy1dv5CnAcAAAAAIBkxuKyqvxyiWWut/dUo5wcAAAAA1rZRF+eZaTh4SzfnZWutrR+1sHFlqDgAAAAAjG6xF+dZN3hJ13vztkn+IckXk9x8lHMDAAAAAIwUXE6ltbaztXZma+15Sb6X5NULdW4AAAAAYG1ZsOBywKeSPHSRzg0AAAAArHKLFVzePclM82ACAAAAAExr1FXFf2+aXfskuW+SxyR5w4g1AQAAAABr3EjBZZI3z7DvgiTHJnnpiOcGAAAAANa4UYPLW0+xrSW5qLV2yTzqAQAAAAAYLbhsrZ210IUAAAAAAExYrMV5AAAAAABGNlSPy6ramW4o+Fy01tqoQ9EBAAAAgDVs2GDxpZl7cAkAAAAAMJJhg8sTk5zVWtu2mMUAAAAAACTDz3F5epIjJm5U1Seq6oGLUxIAAAAAsNYNG1xekeTGk24fnmT/Ba8GAAAAACDDDxX/apLnVNWOJBPDxX+lqq6c6aDW2onzKQ4AAAAAWJuqtdnX3Kmquyc5Ickt+00tSc1yWGutrZ9feeOnqvZOF95ubK1tX+56AAAAAGAlGTZfG6rHZWvti1V1uyS3TTdE/JQkf53kv+dfKgAAAADA9Q07VDyttWuTfCfJd6rqLUn+s7V22qJVBgAAAACsWUMHl5O11p6y0IUAAAAAAEwYKbhMkqq6SZLfSXKbJDfJDee8bK21p82jNgAAAABgjRopuKyq30i3WM8eSbYnuWiKZrOv+gMAAAAAMIVRe1y+MsmPkzymtfb1BawHAAAAACDrRjzudkn+UWgJAAAAACyGUYPL7yXZayELAQAAAACYMGpw+RdJ/rCqNi1gLQAAAAAASUaf4/KBSc5P8u2q+q8kZyfZMdCmtdaOnk9xAAAAAMDaVK3NffHvqto5RLPWWls/95LGW1XtnWRbko2tte3LXQ8AAAAArCTD5msj9bhsrY06xBwAAAAAYFYCSAAAAABg7KzY4LKqblpVb6+q7VV1cVX9W1XtOcsxp1RVG7j8y1LVDAAAAAAMZ+ih4lX1tTmeu7XW7jTHY+bi7UkOTPLgJLskeVOS1yU5apbjXp/kLyfdvnxRqgMAAAAARjaXOS4vTDL3lXwWQVXdIclDkvxKa+2L/bb/m+RDVfW81tqPZjj88tbaj5eiTgAAAABgNEMHl621wxexjrm6V5KLJ0LL3n8n2Znknkn+Y4Zjf7eqnpDkx0k+mOSvWmvT9rqsqt2S7DZp014jVw0AAAAADGWkVcXHwAFJfjJ5Q2vt2qq6sN83nXckOSvJj5L8cpK/S3JoksfMcMwLkrxoXtUCAAAAAHMyVsFlVR2b5M9maXaHUc/fWnvdpJtfr6pzk3y8qm7bWvvBNIf9bZJXTbq9V5Kto9YAAAAAAMxurILLJK9M8uZZ2vww3TDvm0/eWFUbkty03zes0/rr2yWZMrhsrV2V5KpJ9zOH0wMAAAAAoxir4LK1dn6S82drV1WfTbJPVd2ttfalfvMDkqzLz8LIYdy5vz53LnUCAAAAAItr3XIXMIrW2reTfCTJ66vqHlV17yT/lORdEyuKV9VBVXVGVd2jv33bqnphVd2tqjZV1SOSvDXJp1prX1uunwUAAAAAuKEVGVz2fjfJGUk+nuRDST6d5BmT9u+SbuGdG/e3r07yoCQf6497ZZL3JXn4EtULAAAAAAypWmvLXcOKUlV7J9mWZGNrbfty1wMAAAAAK8mw+dpQc1xW1ZlJdia5fWvtmv72bIlna63ddtiCAQAAAAAmDLs4zyfTBZU7B24DAAAAACw4Q8XnyFBxAAAAABjdsPnaSl6cBwAAAABYpYad4/K+o5y8tfapUY4DAAAAANa2Yee4PCXXn9OyMtwcl+vnWhAAAAAAwLDB5f0Hbu+W5O+T3DjJ65J8p99++yS/n+SyJH+6EAUCAAAAAGvPSIvzVNWrktwnyX1ba1cO7LtxulXHP9Vae+6CVDlGLM4DAAAAAKNb7MV5fjfJ2wZDyyRprV2e5G1JnjDiuQEAAACANW7U4HKPJAfOsP/AdMPIAQAAAADmbNTg8r+THF1VjxncUVW/leTovg0AAAAAwJyNOsflQUk+keR2Sc5N8v1+122T3CLJD5I8oLW2dYHqHBvmuAQAAACA0S3qHJettXOS3CnJc5J8I8n+/eWbSf4kyZ1WY2gJAAAAACyNkXpcrmV6XAIAAADA6BZ7VXEAAAAAgEWzYZhGVfWJEc7dWmsPHOE4AAAAAGCNGyq4TNczc65jymuO7QEAAAAAkgwZXLbWDl/kOgAAAAAArmOOSwAAAABg7Aw7VHxKVXW/JEckuVW/6awkm1trn5xvYQAAAADA2jVScFlVuyZ5Z5JHpZvL8uJ+1z5JnltV/5Hkd1pr18y/RAAAAABgrRl1qPiLkjw6ySuTHNhau2lr7aZJDkjyiiSPSfKXC1MiAAAAALDWVGtzXSw8qaozk5zSWnvKNPvfnOTw1tqmeVU3hqpq7yTbkmxsrW1f7noAAAAAYCUZNl8btcflgUlOm2H/ael6XwIAAAAAzNmoweXWJIfPsP9+fRsAAAAAgDkbNbh8S5LHVdW/VNWhVbW+qtb1/35tkscmefOCVQkAAAAArCmjznG5Psm/Jfm9JC3Jzn7XunSrjL8lydNaazunPsPKZY5LAAAAABjdsPnaSMHlpDv55SQPTXKrftNZST7UWvvayCcdc4JLAAAAABjdsPnahvncSR9QrtqQEgAAAABYHvMKLpOkqvZMcpN0Q8Svp7X2v/M9PwAAAACw9owUXFbV7klelORpSfadoen6Uc4PAAAAAKxto/a4fE2SJyV5f5JTk1y0UAUBAAAAAIwaXD4myRtaa89cyGIAAAAAAJJk3YjHtSRfXshCAAAAAAAmjBpcnpTkQQtZCAAAAADAhFGDy79Kcpuqel1V3a2qblZVNx28LGShAAAAAMDaUa21uR9UtXPSzWlP0FpbdauKV9XeSbYl2dha277c9QAAAADASjJsvjbq4jwvzQyBJQAAAADAfMw5uKyqXZKcmOTC1trWhS8JAAAAAFjrRpnjcmeSLyV5zALXAgAAAACQZIQel621HVV1VpLdFqEeAFaDqhsleXyS301yQJLLknwkyevT2jnLWRoAAAArw6irir86yTOsHA7ADVT9WpIzk7yxJe37Nz34e9/b9+Brd9S6P2vJWal67nKXCAAAwPgbdXGe9UmuSvKDqjohyZYkVwy0aa21f5hHbQCsNFV3SvLRJKf/4SOPecmHbn+fP09ycJLsedXlee6pb7vkKV/64CtSdU1a+8dlrRUAAICxVq3NfXHwqto5RLPWWls/95LG27DLtQOsSVUfSXLwvZ/1b399zsb93z6xdVKL9pKPvbZ+9ysfvmpD27l/Wtu2DFUCAACwjIbN10YdKn7rIS63GfHcAKxEVbdL8hvXrFv/inM27v/3E1sHW/3zrz2+pWq3a2vdk5e2QAAAAFaSkYaKt9bOmq1NVd1klHMDsGIdliQPevprz00/PHwqP9nzpnXaIb+Y2/70nMcckBy/ZNUBAACwooza43JKVbVbVT22qt6f5NyFPDcAY2+3JDvO3rj/rAu3Xb7LjXLN+g17LUFNAAAArFDzDi6r86CqelOS85K8O8m9krxjvucGYEU5O8n6Z3z+xBvN1Gjdzh25w0/OzDXr1p+9RHUBAACwAo0cXFbV3arqVUnOSfKxJL+XZHOSeyc5oLX21IUpEYAV4mNJzv3TT771V5NsTTLl6m/3/+EX28Hbf5IDLvnp3y5pdQAAAKwocwouq+o2VfXCqjojyeeTHJnk7Uken24Bhve11j7bRlmqHICVrbVrkhy/Lu3pf/fh4989sXVyk1tedG572UdfU+ffeJ9v3/jaq05b+iIBAABYKWrYjLGqPpvkHkkuSHJCkne21j7d77ttku8lObK1duIi1ToWhl2uHWBNqlqX5K1Jjtqyz4GfedGDn/Xz377Zppvd5MpL8uhvnpyjvvLhVq2du9fVV9w9rZkLGQAAYA0aNl+bS3C5M8mZSZ6TZHNr7dpJ+wSXAHS68PJZSf44yaETm69dt/7SajvfsL61l6W1ny5bfQAAACyrYfO1DXM45/9JclSS/0hyYVW9L8m7kpwyjzoBWG1a25nkNal6bZK7Jdk/yWUbdu74fFq7fHmLAwAAYKUYOrhsrb0myWuq6tZJfjddiPn7SX6c5OR085iZ2xKATtel/4vLXQYAAAAr09BDxac8uOpu6ULMxyc5MMl5ST6Y5ANJ/ru1duVCFDlODBUHAAAAgNEt+ByXs9zZuiQPSPKEJI9OsleSy1tre8775GNGcAkAAAAAoxs2X1u3EHfWWtvZWvvv1tqT081l9jtJPr4Q5wYAAAAA1p4FCS4na61d2Vp7d2vtkQt9bgBgkVQdkKoXpuoHqbo6VdtT9cFU/Wa/UjwAAMCSmsuq4gDAalT14CTvS7J+Z+qdn7vlHS+7ev0uN7/bOWfcfa+rL/9Qkven6neyCueuBgAAxpfgEgDWsqq7JDkpySkPecqr33nGzW/9N0kOTpK0lod+59MXvPoDL3/o+rbzTemmggEAAFgShn4BwNr2l0nOuscfvuWtZ9z81m9JctB1e6ryodsftu/zH3r0Lkl+O1V3Wq4iAQCAtUdwCQBrVdVBSR5xba179U/22vflE1sHW530C4fnJ3vss2Nn6g+XuEIAAGANE1wCwNp1pyTrjn74889PNzx8MLRMkuxYt75Ovs2vrL9ktxvfd0mrAwAA1jTBJQCsXeuS5Pw99tlvtoY71q3PjnXrd1v8kgAAADqCSwBYu76bJE/+0gcPmLFVa7nH2d/IFbvs9sMlqQoAACCCSwBYu1r7bpJP/uZ3P/Pg2rlza5I2VbPDtpzebnfh1ux7+ba/WdoCAQCAtUxwCQBr219X8qub33L0N9bt3JEMhJeH/mRL+4f/fFWdf+N9ztj92qtPXp4SAQCAtaham7JzBdOoqr2TbEuysbW2fbnrAYB5q3pGktdeuuuNznvtPY+88em3OHTjnldfkSPO+HQe+p1P59Jdb3TmTa689J5p7fzlLhUAAFj5hs3XBJdzJLgEYFWqukeSZ7fkyEp2SZLLN+x2zq47rnnVhrbzdWnt0mWuEAAAWCUEl4tEcAnAqla1V5KbJ7kyyblpbecyVwQAAKwyw+ZrG5auJABg7LV2SZJLlrsMAAAAi/MAAAAAAGNHj0sAGBdVleQ+Se6R7jP6+0k+mNauXta6AAAAloHgEgDGQdVDk/xdkjsmuSzJNUn2SfKTVL0iyStiYmoAAGANEVwCwHKr+t0kb0vy8SvX7/KcX3zOCdfsWLf+wKd+4f27/sUn/u1e69L+PsltU/UHwksAAGCtsKr4HFlVHIAFVXVIkh8kecfPPe8/PnjN+l2OS3LwpBZbX33S35308DNO/aMkj09r71mOMgEAABbKsPmaxXkAYHk9I8mVh//+6z52zfpd3pvkoIH9B/3fR/7ZH563x02/keT/LH15AAAAy0NwCQDL6/E7U+/actNb/F1/uwb2V5K8/H6/d2CSw1I1GGwCAACsSoJLAFheNzv9FofuTDc8fDC0nFDf2/eQfft/77c0ZQEAACwvwSUALK9LKu3g2Rrte/m269ovbjkAAADjQXAJAMvrP+943g/utWHHtTM2+q1vfDyX7bL7liRnLklVAAAAy0xwCQDL6zW77rj2pn/8mXdtS9KmanDP//16+43vfja7XXv1K9PalG0AAABWG8ElACyn1r6R5KV//Jl3bfzL/35dHbD9guuCyRtffUWe+OXN7U0nvLgu2GOfr21oO1+3jJUCAAAsqdJxY26qau8k25JsbK1tX+56AFgFqirJ865dt/6laW33rx/wc7l6/Ybc4SdnZo9rrsiZNznok7e7cOtD09rly10qAADAfA2brwku50hwCcCiqdr72lr3xB/tfbOHXblh1z2v3GW37x+07Scv2ffybVuWuzQAAICFIrhcJIJLAAAAABjdsPmaOS4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYOys2uKyq/1dVn6mqy6vq4iGPqap6aVWdW1VXVNV/V9XPLXKpAAAAAMAcrdjgMsmuSd6b5LVzOOZPk/xxkmcluWeSy5J8tKp2X/jyAAAAAIBRbVjuAkbVWntRklTVk4dpX1WV5NlJXtZaO6nf9ntJzkvyqCTvWow6AQAAAIC5W8k9Lufq1kkOSPLfExtaa9uSnJbkXtMdVFW7VdXeE5ckey16pQAAAACwxq2l4PKA/vq8ge3nTdo3lRck2TbpsnXhSwMAAAAAJhur4LKqjq2qNsvl9ktc1t8m2TjpcvAS3z8AAAAArDnjNsflK5O8eZY2Pxzx3D/ur/dPcu6k7fsn+cp0B7XWrkpy1cTtbqpMAAAAAGAxjVVw2Vo7P8n5i3T6M9OFlw9MH1T2c1beM3NbmRwAAAAAWGRjNVR8LqrqllV15yS3TLK+qu7cX/ac1OaMqnp0krTWWpLjkvxFVT2iqn4pyVuT/CjJ+5e6fgAAAABgemPV43KOXprkSZNun95f3z/JKf2/D003L+WEv0+yR5LXJdknyaeTPKS1duViFgoAAAAAzE11HREZVj+8fFuSja217ctdDwAAAACsJMPmayt2qDgAAAAAsHoJLgEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOxsWO4CYE2o2pDk15NsSnJ1kv9Ja99e1poAAAAAxpjgEhZTVSX5kyTPSXJQkh1J1vf7TknyZ2nt88tVHgAAAMC4MlQcFksXWr4uySuTfOSyXXa/222ff9KD7vzH7/i9D//8r/1VSzYm+WSqHrC8hQIAAACMn2qtLXcNK0pV7Z1kW5KNrbXty10PY6zqyUnelORJm/7sPy9NcnySgyd273bNVVtPef0zLjjwkp9uSnKreD0BAAAAa8Cw+Zoel7AYut6WRyf5zz60PCHdUPHrXLXLbgc96omvvNPOZO8kT1yGKgEAAADGluASFscdktz5qvUbXp+up2WS1ECbOm+v/fLJ29z9qpb87tKWBwAAADDeBJewOPZPkpc86Jl7pRsePhhaTqgzbrbpRldu2PVWS1YZAAAAwAoguITFcVmS3OjqK28zW8N9rtieqzbseu3ilwQAAACwcgguYXF8Jcl5R331I3edqdHu11yZh37nf/LjPff9zNKUBQAAALAyCC5hMbR2dZLX3+bCcx58h/N++OMkbapmf/C5E7LXVZfngEt/+sKlLRAAAABgvAkuYfG8vJIfvP9tz73xb37nf7J+547rwsv9Lrso/+8Tb8jRn3lXTrvlHd+5zxWXfH85CwUAAAAYN9XalB3BmEZV7Z1kW5KNrbXty10PY65qvyTvTPKgn+xxkx1fP+B263e/9qr8ytnfys5169rnD/7Ft913y+lPjjciAAAAsEYMm68JLudIcMlIqu6ys+qp5994n7tevuuNNpy/x02+cPNLL3zRpot+9NPlLg0AAABgKQkuF4ngEgAAAABGN2y+Zo5LAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxs6G5S4AYN6qDknyjCS/mWTPJOcleUeSt6e1S5ezNAAAAGA0elwCK1vV85Kc2ZKjz964/wWnH3joD36yx012bclrkpyVqsOXt0AAAABgFHpcAitX1R8nefnpBx76/ic+/qW/culue/zGxK5DLv7xue99+5+df8ClP/1Qqu6X1r6wjJUCAAAAc1StteWuYUWpqr2TbEuysbW2fbnrgTWramOSc75589t86oin/ONDJrZOatF2u+aqnPaaJ23Z58pLt6S1ByxDlQAAAMCAYfM1Q8WBleqJLdn96b/1wjv1t2tgf121y2552QOevneS+6fqDktcHwAAADAPgktgpTrsohvt9Y1z977ZLXLD0HJCfeAO99u3JS3JYUtYGwAAADBPgktgpdrtsl1uNOtcF1ev35CdVTuS7LoENQEAAAALRHAJrFRn3fyyiw5ev3PHjI1+/oKzsr61DUnOWpqyAAAAgIUguARWqjfttuOa/Y4449SfphsKPpX2tC+cdGlLfpzkI0tYGwAAADBPgktgZWrtK0k+/vIPHb/htj89O7lheNke9u1P5XFf/689Kjk+rV2z5DUCAAAAI6vWZp0ijkmGXa4dWAJVN0ty8jXr1t/m3b/86zvedaff2POiG+2dW194Tp78pQ9e8cAffH73St6e5ElpbedylwsAAAAMn68JLudIcAljpmpjkmNa8vRK9pvY3JJvVfKPSV4vtAQAAIDxIbhcJIJLGFNVuyW5e5I9k5yX5KvxCw4AAADGzrD52oalKwlgEbV2VZL/We4yAAAAgIVhcR4AAAAAYOwILgEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOwILgEAAACAsSO4BAAAAADGjuASAAAAABg7gksAAAAAYOwILgEAAACAsbNhuQtgBavaL8khSa5O8r20dvUyVwQAAADAKqHHJXNXde9UnZjkvCRfTvKNJFtT9TeputnyFgcAAADAaqDHJXNT9ftJ/jXJt66tdX/8z/d6/LVXb9hwi0d/8+RfvO1Pt/5RJUel6gFp7YfLXSoAAAAAK1e11pa7hhWlqvZOsi3Jxtba9uWuZ0lVHZ7kE0le83PP+4+Tr1m/y3FJDp7YffC28879yBv/T/a8+ortSX4prV2zLHUCAAAAMLaGzdcMFWcu/jTJ6X1o+d4kB03euXXj/gc87qi/OyDJoUketQz1AQAAALBKCC4ZTtXBSR5yzbr1/9z3tEySGmz1rf1vk9MP/PmrdiZPX9oCAQAAAFhNBJcM6zZJ6mUPePq16YaHD4aWE+q0W/7Sbldt2PWOS1caAAAAAKuN4JJhXZMkV+yy24GzNdxlx7W5Zr11nwAAAAAYneCSYX0zyeVPOP1Dh87UqNrOPPh7n8u23ff8xhLVBQAAAMAqJLhkON0KT2//pR9//yE3uXzbj5JMuRz9w759arvltvOy32XbXrK0BQIAAACwmggumYu/rWTXj7/hWZcftO0nyeTwsrX85hmfbn//4ePrrH0O+OyNrr3qs8tWJQAAAAArXrU2Zcc5plFVeyfZlmRj63ohri1Vd0myuSX7n3Kbu131xYN+4Ua7X3t1fvM7/5PbXbg1/7tx/9Nuue28+6e1K5a7VAAAAADGz7D5muByjtZ8cJkkVXsl+d2WPPXadet//tp1G3ZedKO9vnrTK7b/1e7XXn1yvKgAAAAAmIbgcpEILgEAAABgdMPmaxuWriTWhKp7JHlSkoOTXJnklCT/ntYuWc6yAAAAAFhZLM7Dwqi6RapOTXJaSx72kz1ucvPz9rzpL7Xkn1ryo1Q9Y7lLBAAAAGDl0OOS+avaL8knk+z+5rs+7G9f+sDff+LOdet/NUkO3H5+nnfq2+q3vvGJf03Vrmntn5a3WAAAAABWAnNczpE5LqdQ9eokRz3rUS/4i48ceu9/ntg6qUV78X/9S/3elzdfuy7toLT2k2WoEgAAAIAxMGy+Zqg481O1Z5In7aj6l48ceu8/n9g62Oof7vO77Zr1G9bvqHVPX+IKAQAAAFiBBJfM172S7PXSBz7jO+kW5BkMLZMk2260V338tr9S23fb4/FLWh0AAAAAK5LgkvnaM0m+fIvb7z5bw4tuvHda1xUYAAAAAGYkuGS+zkuSh5/xqV1na3ibC8/JjlpnfksAAAAAZiW4ZL5OS3Lm0z///nsm2ZpkytWebvPTre1e//v17HPlJVYVBwAAAGBWgkvmp7UdSV69Lu2ol37stf8+sXVykxtffUU79iP/WFeu3/XiXXbueO+C11D1C6k6LlUf7y/Hp+oXF/x+AAAAAFgy1dqUHeSYxrDLta8pVeuTvCvJo79589t89DlHPOeu37nZrQ7Y7dqr87AzPp0/+tx7rrnlxT/esWHnjvuntc8t4P3eKMkbk/x2S35y7l77ffOKXXa70SHbzjt01x3X3iTJu5M8Na1dvmD3CQAAAMC8DJuvCS7nSHA5jS68/PMk/zfJzXamrl2XtiFJdiYfW5c8P619bYHv76QkD/jA7Q/7t+ce8ZxHXbNhl4OTZJcd1+SxX/vvC1/6X6/dY0PbeXKSh6e1axfsvgEAAAAYmeBykQguZ1G1W5KHJDkkyRVJPpXWvrcI9/O4JO9+/a88+q/++gFP+4uJrZNatPuceXr+/T0vrCS/k9beteA1AAAAADBngstFIrgcE1WntKRu/Wf/eZskB+X6oeWEdsK/P//qu53z7S9Ua4ctcYUAAAAATGHYfM3iPKw8Vbskud//3OrOX0pycKYOLZOkTrjjA3er5D59T1AAAAAAVgjBJSvRjZLkf/fZf8NsDS/ZbY/rHQMAAADAyiC4ZCW6NMmlv3zu9/aZreHP/fR/s6PWXZnkkkWvCgAAAIAFI7hk5WltZ5J//8Wf/PBBG3ZcuzXJlBO1bthxbTvqKx/eUa29La3tWNoiAQAAAJgPwSUr1T9VcrPNbz76jGo7k4HwstrO9tL/+pfa77KLa13aq5enRAAAAABGZVXxObKq+BipelKSN55/432+81cPfPrNP37be+zbqnLYltPzjNNOvOquPzpj10qentbeuNylAgAAANAZNl8TXM6R4HLMVP16kr9Mcu/Jm1vymUpemtY+ujyFAQAAADAVweUiEVyOqapfSnLH/tY30trXl7McAAAAAKY2bL62YelKgkXUBZXCSgAAAIBVwuI8AAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjJ0VG1xW1f+rqs9U1eVVdfGQx7y5qtrA5SOLXCoAAAAAMEcblruAedg1yXuTfDbJ0+Zw3EeSPGXS7asWsigAAAAAYP5WbHDZWntRklTVk+d46FWttR8vfEUAAAAAwEJZsUPF5+HwqvpJVX2nql5bVfvO1LiqdquqvScuSfZaojoBAAAAYM1aa8HlR5L8XpIHJvmzJPdL8uGqWj/DMS9Ism3SZetiFwkAAAAAa91YBZdVdewUi+cMXm4/6vlba+9qrX2gtfb11tr7kzwsya8kOXyGw/42ycZJl4NHvX8AAAAAYDjjNsflK5O8eZY2P1yoO2ut/bCqLkhyuyQfn6bNVZm0gE9VLdTdAwAAAADTGKvgsrV2fpLzl+r+qurgJPsmOXep7hMAAAAAmN1YDRWfi6q6ZVXdOcktk6yvqjv3lz0ntTmjqh7d/3vPqnp5Vf1qVW2qqgcmOSnJ95N8dDl+BgAAAABgamPV43KOXprkSZNun95f3z/JKf2/D003L2WS7Ejyy/0x+yT5UZKPJXlhPxwcAAAAABgT1Vpb7hpWlKraO93q4htba9uXux4AAAAAWEmGzddW7FBxAAAAAGD1ElwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdgSXAAAAAMDYEVwCAAAAAGNHcAkAAAAAjB3BJQAAAAAwdjYsdwGMkar9kvxakt2TnJ3ktLS2c3mLAgAAAGAtElySVG1K8ldJHpdk10l7vpeqf0jyL2mtLUdpAAAAAKxNgsu1ruoXkpyc5Opra90Lj37488/89s1vvdfvfuXDez7lix+497q01yS5S6qeKbwEAAAAYKmULGpuqmrvJNuSbGytbV/ueualakOSM5Jc/ojfe9Urv3bgz78sycGTWmz9xw/8/fsf8e1P/Z8kz0pr/7osdQIAAACwagybr1mcZ217eJLbvuhBz3zL1w78+TclOWhg/0F//Ig//aMt+xz4mSR/kqpa+hIBAAAAWIsEl2vb77XktLfc7eHP7m8PBpOVJC/89T+4XZJDk9xjCWsDAAAAYA0TXK5tB521z4HnpxsePl1vyvr6Abe7ef/vg6dpAwAAAAALSnC5tl25Y926/WZrtOdVl0/884rFLQcAAAAAOoLLte0Tmy46986TgskpPeLbn8qOqquTfG5pygIAAABgrRNcrm2vX9d27nL0/7zzkiRTLi+/32UXtad88QM7quUdae3CJa4PAAAAgDVKcLmWtXZOJS/6/S/8x17P/+Rbau8rL71eeHmnH32nvfOdf157XX359nVpf7lcZQIAAACw9lRrU3a0YxpVtXeSbUk2tta2L3c981ZVSV6wM/XSqzbssu7k29y9Lt3txrnDT87ML533g1yy643P2evqyx+Q1r673KUCAAAAsPINm68JLudo1QWXE6oO3FHrnrFt9z0ftaPW7XHt+vU/uunl2165245rP5TWdix3eQAAAACsDoLLRbJqg0sAAAAAWALD5mvmuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLEjuAQAAAAAxo7gEgAAAAAYO4JLAAAAAGDsCC4BAAAAgLGzYbkLWMH2qqrlrgEAAAAAVpq9hmkkuJy7iQd267JWAQAAAAAr215Jtk+3s1prS1jLylddN8tbJLlkuWtZYnulC2sPztr72WEl8p6FlcP7FVYW71lYWbxnYXztleRHbYZwUo/LOeofzHOWu46lNmlY/CWttWmTcGA8eM/CyuH9CiuL9yysLN6zMNZmfU9anAcAAAAAGDuCSwAAAABg7AguGdZVSV7SXwPjz3sWVg7vV1hZvGdhZfGehRXM4jwAAAAAwNjR4xIAAAAAGDuCSwAAAABg7AguAQAAAICxI7gEAAAAAMaO4JIbqKoDq+rYqjq5qi6pqlZVh8/h+Bf3xwxerly8qmHtmu97tj/HQVX1nqq6uKq2V9VJVXWbxakYqKp9qup1VXV+VV3Wv3/vOuSxb57mc/aMxa4bVrOq2q2q/q6qflRVV1TVaVX14CGP9TkKS2zU96zvq7CybFjuAhhLhyb5syTfS/L1JPca8Tx/kOTSSbd3zLMuYGrzes9W1Z5JTk6yMcnfJLkmyZ8k+WRV3bm19tOFLRfWtqpal2RzkjsleXmSC5L8YZJTqupurbXvDXGaq5I8fWDbtgUtFNaeNyc5Mslx6T5Tn5zkQ1V1/9bap6c7yOcoLJs3Z4T37CS+r8IKILhkKl9Ksm9r7cKqOjLJe0c8zwmttQsWsC5gavN9z/5hkp9Lco/W2heSpKo+nOQbSZ6b5M8XslggRyb5tSSPba2dkCRV9Z4k303ykiRHDXGOa1tr/754JcLaUlX3SPLbSZ7fWntFv+2t6T4L/z7de3Y6Pkdhic3zPTvB91VYAQwV5wZaa5e01i5cgFNVVe1dVbUA5wKmsQDv2SOTfGHiy1Z/zjOSfDzJ4+ZbH3ADRyY5L8mJExtaa+cneU+SR1bVbsOcpKrWV9Xei1MirDlHputt9bqJDa21K5P8W5J7VdUhsxzrcxSW1nzesxN8X4UVQHDJYvphumFrl1TVv1fV/stdEHB9/ZDVX07yxSl2fz7Jbatqr6WtCla9uyT5cmtt58D2zye5cZKfH+IcN06yPcm2qrqwqv65H64KjOYuSb7bWts+sP3z/fWdpzrI5ygsm5HeswN8X4UVwFBxFsNFSf4pyWfTzcF1WJI/SnKPqrr7FB8uwPK5aZLdkpw7xb6JbbdI8p0lqwhWvwOTfGqK7ZPfc1+f4fhz0w2D+3K6P0I/JN1Q1TtV1eGttWsXsFZYKw7M7J+FU/E5Cstj1Pds4vsqrCiCy1Wu/yvwrkM2v6q11uZ7n6214wc2va+qPp/k7em+WB073/uA1WoZ3rM3mjjXFPuuHGgDDBjxPXujzOM911p7wcCmd1XVd5P8dbqhc+8ash7gZ0Z9X/ocheUx8mep76uwshgqvvrdN8kVQ14OXawiWmvvSPLjJA9arPuAVWKp37NX9NdTzam3+0Ab4IZGec9ekYV/z/1Dkp3xOQujGvV96XMUlseCfpb6vgrjS4/L1e+MJE8Zsu1UXe0X0tnphtMA01vq9+yF6f5afeAU+ya2/WgB7gdWq1Hes+dmgd9zrbUrquqn8TkLozo3yUFTbJ/tfelzFJbHqO/Zmfi+CmNIcLnKtdZ+nOTNy11Hv1LbpiSnL3MpMNaW+j3bWttZVV9Pcvcpdt8zyQ9ba5csVT2w0oz4nv1KksOqat3AAj33THJ5ku/OtY5+8Y/9kpw/12OBJN378v5VtffA/Hb3nLT/BnyOwrL5SkZ4z07H91UYX4aKMy9Vdcuquv3AtptN0fQPktwsyUeWpDBgSlO9Z5OckORXquruk9odmuQBSd67lPXBGnFCkv2TPGZiQ1Xtl+SxST7YWrtq0vbbVtVtJ93efZoVil+YpOJzFkZ1QpL1SZ4xsaGqdkvXo/q01trZ/TafozAeRn7P+r4KK0stwFosrEJV9Rf9P38xyW8neWOSM5OktfaySe1OSXK/1lpN2nZ5knenWxH1yiT36c/x1ST3bq1dvgQ/Aqwp83zP7pXur8t7JXlFkmuSPCfdfwbv3FrTgwsWUFWtT/LpJHdM8vIkF6RbDOCWSX6ltfadSW23JElrbVN/e1O69+s70w1TT5LfSPLQdF+2jhjoxQkMqarek+TR6eaM/X6SJyW5R5IHttY+1bc5JT5HYSzM4z3r+yqsIIJLplRV074wBn7pn5IbfhC8PsmvJTkk3eTIZyV5X5K/NlQGFsd83rP99oPT/afv19P1xj8lyZ+01r6/GPXCWldVN0kXWj4q3cqnX0jyvNbaFwfabUmuF1zuk+TVSX41yS3SBSPfT7cS6itaa9csRf38//buPfjyuq7j+PO1gDGosXhNqwFFRi21ssasIYPFRGyiMURnBIkEr9NwKyNSZNPipnhlzC0QBEpkEFEp3HRaNsSphFQEUnDdVZAQhGWTYFHg3R+fz4EzX363/fG7HPP5mDnz/X2/38/5XL7n7HB4z+fzeev/oyQ7Au8EDgZ2Aa4Gjq+qtWNlLsP/jkoTYb7/Zv3/VenHi4FLSZIkSZIkSRPHPS4lSZIkSZIkTRwDl5IkSZIkSZImjoFLSZIkSZIkSRPHwKUkSZIkSZKkiWPgUpIkSZIkSdLEMXApSZIkSZIkaeIYuJQkSZIkSZI0cQxcSpIkSZIkSZo4Bi4lSZIkSZIkTRwDl5IkSRMsyeoktdz9WGxJtk9yapIbkzyQ5OJ+vZKsXt7eSZIkaTkYuJQkSVoiSQ7tgbjRa2uSm5OsTXJEkscudx9HkuzUg6Z7zbH8Xn1Mr5hnk68F3gJcCPwh8N551vNjL8meSS5N8t3+HflOks8kefVy902SJGkpbb/cHZAkSfoJ9HZgI7AD8DPAXsD7gGOS7F9VV4+V/Svg5KXuILATcEL/+7IlaG8V8N2qOnoJ2ppYSQ4EPg58BXg/sBl4GvAi4HXAPyxb5yRJkpaYgUtJkqSld2lVXTl2flKSVcAlwKeTPLuq7gGoqvuA+2aqLMkK4FFVtXXRerz4ngTcudydmACrgeuAF1bVD8dvJHnSUnUiSYAdR99DSZKk5eBScUmSpAlQVf8CvBPYFTh4dH2qPS77kuzTkxyU5FrgXuCl/d7PJvlIku8luTfJtUleO2wvyY697uv7cuT/TnJRkt2T7Abc1oueMLa0ffW2jGnU9yTPSHJ2kjuTbElyVpKdepnd+vj2Bn5xrK29pqnz7CSbpmtriusHJ7kqyT1J7khyfpKfH5S5LMk1SX4hybokd/dl2n+2Lc9trMyKJEf1Z7+1fxZrkuwyh8e2O/ClYdASoKpuHfRlRZIjk3ytt3Nbks8m+bWxMtsnOT7Jhv592JTkxCQ/NahrU5JLkuyb5ErgHuAN/d7KJO/r+4/em+SbSY7tAXNJkqRF448NSZKkyXFuP75kDmVX0faB/DhwJLApyZOBfwNeDJzer38TODPJUaM3JtmONrvzBOAq4E9oy5J3Bp5DC1q+qRf/JPCa/rponuO6AHgscFz/+1AeWoZ+W6/768BNY2391zzbelCStwLnADcAx9CW4+8D/GuSlYPiuwCfBb5Kex5fB05Jst9YfbM9t5E1wLuAK2ifwVnAQcDaJDvM0u1vA/sk+bk5DPHMPqYbgWNpWwpsBV44VuYM4B3AfwJHA+tpn8P5U9T3TOBjwOd6v7/SA8zracH0c4Aj+rhOAt4zhz5KkiTNm0vFJUmSJkRV3ZRkC23W3WyeCTy3qq4bXUhyBrBdv357v/zhJB8DVidZ05f+HkIL4B1TVeNJcE5OkqqqJBcCfwNcXVXnPcKhfbmqDhvr5+OBw4Bjq+p/gfOSHA7cvwBtjdrYFfhL4G1VdeLY9YuALwNvBk4ce8tTgUOq6txe7kxaEPEw4NJeZsbn1t+3J3A4cFBVPbgfZZJ1tMDogcy8T+UptIDkhiRXAF8A/hn4YlU9MFbf3rQA8Aeq6six95821pdfoiU6OqOqXtfvfyjJrcCfJtm7qtaNvfcZwEurau1YO2+jfR9/papu6JfXJLkZeEuS06rqxhnGI0mSNG/OuJQkSZosd9FmJ85m/SBoGeAA4DP99AmjF7CWNivw+b34AcD3gQ8OK62qhy23XgAfHpxfDjw+yU8vQlsjf0D7rXvB4FncQpuBufeg/F3Ag0HTvlT7P4Cnj5WZy3M7ENgCfG7Q7lW9jWG7w3o+Qlv2fxmwJ3A87XndkOQ3B30pWnB2ur68rB+HMyNP68ffHVzfOB60HBvP5cDmwXg+TwuSv2im8UiSJD0SzriUJEmaLI8Bbp21VMtKPu6JwErg9f01lVFyl92Bb/TEP0vhO4Pzzf24C/A/i9TmHkBoQcqp/GhwftMUQdvNwPPGzufy3PagBYmn+wxnTbDTg4dr+zLtXwVeBbwRuCTJs/pel7sDN1fVHTNUtSvwAG27gPH6b0lyZ78/bvidgjae5/HQnqdDS5YwSJIk/eQxcClJkjQh+r6GOzMINE1jmO15tJLmPOCj07zn6nl27ZG6f5rrmUdd080I3W5wvqKX3W+a9u8anC9UH1fQgpYHTXN/ugDgw1TV3bTZjpcn+T5tb839mP7znbaqOZabKoP4Ctqel6dO857rt7EvkiRJc2bgUpIkaXK8ph+Hy3Xn4jbgB8B2VfX5WcpuAH49yQ5VNZx5OLIYS8YXwmbazNKh4ezBDbSg48aqWqjg2lye2wZacqQr+n6iC+XKfnzKWDv7JnncDLMuv00LPO7BWLKjnsRpZb8/mw3AY+bwnZIkSVpw7nEpSZI0AZKsou1nuBH4+219f1XdD3wCOCDJc4b3kzxx7PQTwBOAP56i3GiG4d39uHJb+7LINgA7J3lwCXeSpwAvH5S7iDaL8oSxMY3KpycI2lZzeW4X0GZ/Hj9Fme2nyGY+LLPPNLdG+1V+Y6wv4aHs7FP15Z/68ahBkWP68R9n6kt3AfAbSfadop2VSZwIIUmSFo0/NCRJkpbefkmeRfst9mRgFfA7tBlw+1fV1nnW++e05C//nuTvgOuAx9GS8ry4/w1wDi1D9nuSvIC2HPnRvcyHgE9V1T1JrgNeleR64A7gmqq6Zp59Wyjn0zJvfzLJB4CdgDfRliyPkg9RVRt6RuyTgN2SXEybkfo0WpDzb4F3b2Pbc3lu65OsAY5L8su0jOA/os16PBA4ErhwhjY+lWQjLcnShrH6fw/4Ur9OVa1Lci5wRJI9aBnLVwC/BawDTq+qryb5KPD6HjBdD7yAlmn84kFG8em8C9iftr/m2bQkQ48Gngu8AtiNlrBIkiRpwRm4lCRJWnrv6Mcf0gKCX6PNijurqn4w30qr6ns9oPZ2WlbtNwO3A9cCx46Vuz/Jy4C3Aq+mZai+HfhC78vI4bQM2u8FHkXLYL2sgcuquj3Jy2mZsk+lzVA9jhYYfP6g7Mk96Ho0D81MvJEWTPz0PNqe03OrqjcmuQp4A3AicB+wibb/6BWzNHM48PvAK4Gn0mZVfgv4a+CUQWKgP6LtW3oYLcC4hbak/IuD+r4FHEoL2N5CC+Y+LBv5NGO+O8lvA39BC7weQkuodD3tmW6ZSz2SJEnzkYcnT5QkSZIkSZKk5eUel5IkSZIkSZImjoFLSZIkSZIkSRPHwKUkSZIkSZKkiWPgUpIkSZIkSdLEMXApSZIkSZIkaeIYuJQkSZIkSZI0cQxcSpIkSZIkSZo4Bi4lSZIkSZIkTRwDl5IkSZIkSZImjoFLSZIkSZIkSRPHwKUkSZIkSZKkiWPgUpIkSZIkSdLE+T+1SjjclW2oegAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -1178,8 +1178,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation Arnoldi vs direct 0.9913769850406638\n", - "Spearman Correlation Arnoldi vs direct 0.9818122276242538\n" + "Pearson Correlation Arnoldi vs direct 0.9876463041029632\n", + "Spearman Correlation Arnoldi vs direct 0.9772879562687357\n" ] } ], @@ -1234,7 +1234,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Encountered error in cholesky decomposition: linalg.cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 16 is not positive-definite)..\n", + "Encountered error in cholesky decomposition: linalg.cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 17 is not positive-definite)..\n", " Increasing shift by smallest eigenvalue and re-compute\n" ] } @@ -1271,7 +1271,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Percentage error of Nyström over direct method:106.66680335998535 %\n" + "Percentage error of Nyström over direct method:52.49669551849365 %\n" ] } ], @@ -1297,7 +1297,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1341,8 +1341,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation Nyström vs direct 0.9951186619181842\n", - "Spearman Correlation Nyström vs direct 0.9858830642114014\n" + "Pearson Correlation Nyström vs direct 0.9949392350441726\n", + "Spearman Correlation Nyström vs direct 0.9883719172733456\n" ] } ], @@ -1422,7 +1422,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Percentage error of EK-FAC over direct method:1995.9354400634766 %\n" + "Percentage error of EK-FAC over direct method:1528.7002563476562 %\n" ] } ], @@ -1456,7 +1456,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1508,8 +1508,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation EK-FAC vs direct 0.9595030844711058\n", - "Spearman Correlation EK-FAC vs direct 0.8974028264100562\n" + "Pearson Correlation EK-FAC vs direct 0.9579606771217988\n", + "Spearman Correlation EK-FAC vs direct 0.895971987807643\n" ] } ], @@ -1550,8 +1550,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pearson Correlation EK-FAC vs direct - top-20 influences 0.9891339337484283\n", - "Spearman Correlation EK-FAC vs direct - top-20 influences 0.9593984962406013\n" + "Pearson Correlation EK-FAC vs direct - top-20 influences 0.9729110282877721\n", + "Spearman Correlation EK-FAC vs direct - top-20 influences 0.9759398496240601\n" ] } ], diff --git a/notebooks/support/common.py b/notebooks/support/common.py index eecf7c170..6a5242047 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -21,16 +21,16 @@ def plot_gaussian_blobs( - train_ds: Tuple[NDArray[np.float_], NDArray[np.int_]], - test_ds: Tuple[NDArray[np.float_], NDArray[np.int_]], - x_min: Optional[NDArray[np.float_]] = None, - x_max: Optional[NDArray[np.float_]] = None, + train_ds: Tuple[NDArray[np.float64], NDArray[np.int_]], + test_ds: Tuple[NDArray[np.float64], NDArray[np.int_]], + x_min: Optional[NDArray[np.float64]] = None, + x_max: Optional[NDArray[np.float64]] = None, *, xlabel: Optional[str] = None, ylabel: Optional[str] = None, legend_title: Optional[str] = None, vline: Optional[float] = None, - line: Optional[NDArray[np.float_]] = None, + line: Optional[NDArray[np.float64]] = None, suptitle: Optional[str] = None, s: Optional[float] = None, figsize: Tuple[int, int] = (20, 10), @@ -104,15 +104,15 @@ def plot_gaussian_blobs( def plot_influences( - x: NDArray[np.float_], - influences: NDArray[np.float_], + x: NDArray[np.float64], + influences: NDArray[np.float64], corrupted_indices: Optional[List[int]] = None, *, ax: Optional[plt.Axes] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None, legend_title: Optional[str] = None, - line: Optional[NDArray[np.float_]] = None, + line: Optional[NDArray[np.float64]] = None, suptitle: Optional[str] = None, colorbar_limits: Optional[Tuple] = None, ) -> plt.Axes: @@ -403,7 +403,7 @@ def plot_sample_images(dataset: pd.DataFrame, n_images_per_class: int = 3): def plot_lowest_highest_influence_images( - subset_influences: NDArray[np.float_], + subset_influences: NDArray[np.float64], subset_images: List[JpegImageFile], num_to_plot: int, ): @@ -454,7 +454,7 @@ def plot_losses(losses: Losses): def corrupt_imagenet( dataset: pd.DataFrame, fraction_to_corrupt: float, - avg_influences: NDArray[np.float_], + avg_influences: NDArray[np.float64], ) -> Tuple[pd.DataFrame, Dict[Any, List[int]]]: """Given the preprocessed tiny imagenet dataset (or a subset of it), it takes a fraction of the images with the highest influence and (randomly) @@ -494,7 +494,7 @@ def corrupt_imagenet( def compute_mean_corrupted_influences( corrupted_dataset: pd.DataFrame, corrupted_indices: Dict[Any, List[int]], - avg_corrupted_influences: NDArray[np.float_], + avg_corrupted_influences: NDArray[np.float64], ) -> pd.DataFrame: """Given a corrupted dataset, it returns a dataframe with average influence for each class, separating corrupted and original points. @@ -534,7 +534,7 @@ def compute_mean_corrupted_influences( def plot_corrupted_influences_distribution( corrupted_dataset: pd.DataFrame, corrupted_indices: Dict[Any, List[int]], - avg_corrupted_influences: NDArray[np.float_], + avg_corrupted_influences: NDArray[np.float64], figsize: Tuple[int, int] = (16, 8), ): """Given a corrupted dataset, plots the histogram with the distribution of diff --git a/notebooks/support/types.py b/notebooks/support/types.py index 5f3988745..7210fcedc 100644 --- a/notebooks/support/types.py +++ b/notebooks/support/types.py @@ -5,5 +5,5 @@ class Losses(NamedTuple): - training: NDArray[np.float_] - validation: NDArray[np.float_] + training: NDArray[np.float64] + validation: NDArray[np.float64] diff --git a/requirements.txt b/requirements.txt index 109d85ca0..0a08a506a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ pyDeprecate>=0.3.2 -numpy>=1.20 +numpy>=1.20,<2 pandas>=1.3 scikit-learn scipy>=1.7.0 diff --git a/src/pydvl/influence/torch/__init__.py b/src/pydvl/influence/torch/__init__.py index 9dc3a175b..417910de0 100644 --- a/src/pydvl/influence/torch/__init__.py +++ b/src/pydvl/influence/torch/__init__.py @@ -7,5 +7,5 @@ LissaInfluence, NystroemSketchInfluence, ) -from .pre_conditioner import JacobiPreConditioner, NystroemPreConditioner +from .preconditioner import JacobiPreconditioner, NystroemPreconditioner from .util import BlockMode, SecondOrderMode diff --git a/src/pydvl/influence/torch/base.py b/src/pydvl/influence/torch/base.py index 40aa57140..7ae0c7006 100644 --- a/src/pydvl/influence/torch/base.py +++ b/src/pydvl/influence/torch/base.py @@ -3,7 +3,18 @@ from abc import ABC, abstractmethod from collections import OrderedDict from dataclasses import dataclass -from typing import Dict, Iterable, List, Optional, Tuple, TypeVar, Union, cast +from typing import ( + TYPE_CHECKING, + Dict, + Generic, + Iterable, + List, + Optional, + Tuple, + TypeVar, + Union, + cast, +) import torch from torch.func import functional_call @@ -12,11 +23,14 @@ from ..base_influence_function_model import ComposableInfluence from ..types import ( Batch, + BatchType, BilinearForm, BlockMapper, GradientProvider, + GradientProviderType, Operator, OperatorGradientComposition, + TensorType, ) from .util import ( BlockMode, @@ -27,6 +41,9 @@ flatten_dimensions, ) +if TYPE_CHECKING: + from .operator import LowRankOperator + @dataclass(frozen=True) class TorchBatch(Batch): @@ -244,7 +261,7 @@ def flat_mixed_grads(self, batch: TorchBatch) -> torch.Tensor: class OperatorBilinearForm( - BilinearForm[torch.Tensor, TorchBatch, TorchGradientProvider] + BilinearForm[torch.Tensor, TorchBatch, TorchGradientProvider], ): r""" Base class for bilinear forms based on an instance of @@ -257,7 +274,7 @@ class OperatorBilinearForm( def __init__( self, - operator: "TensorOperator", + operator: TorchOperatorType, ): self.operator = operator @@ -406,6 +423,75 @@ def _aggregate_grads(left: torch.Tensor, right: torch.Tensor): return torch.einsum("i..., j... -> ij", left, right) +class LowRankBilinearForm(OperatorBilinearForm): + r""" + Specialized bilinear form for operators of the type + + $$ \operatorname{Op}(b) = V D^{-1}V^Tb.$$ + + It computes the expressions + + $$ \langle \operatorname{Op}(\nabla_{\theta} \ell(z, \theta)), + \nabla_{\theta} \ell(z^{\prime}, \theta) \rangle = + \langle V\nabla_{\theta} \ell(z, \theta), + D^{-1}V\nabla_{\theta} \ell(z^{\prime}, \theta) \rangle$$ + + in an efficient way using [torch.autograd][torch.autograd] functionality. + """ + + def __init__(self, operator: "LowRankOperator"): + super().__init__(operator) + + def grads_inner_prod( + self, + left: TorchBatch, + right: Optional[TorchBatch], + gradient_provider: TorchGradientProvider, + ) -> torch.Tensor: + r""" + Computes the gradient inner product of two batches of data, i.e. + + $$ \langle \nabla_{\omega}\ell(\omega, \text{left.x}, \text{left.y}), + \nabla_{\omega}\ell(\omega, \text{right.x}, \text{right.y}) \rangle_{B}$$ + + where $\nabla_{\omega}\ell(\omega, \cdot, \cdot)$ is represented by the + `gradient_provider` and the expression must be understood sample-wise. + + Args: + left: The first batch for gradient and inner product computation + right: The second batch for gradient and inner product computation, + optional; if not provided, the inner product will use the gradient + computed for `left` for both arguments. + gradient_provider: The gradient provider to compute the gradients. + + Returns: + A tensor representing the inner products of the per-sample gradients + """ + op = cast("LowRankOperator", self.operator) + + if op.exact: + return super().grads_inner_prod(left, right, gradient_provider) + + V = op.low_rank_representation.projections + D = op.low_rank_representation.eigen_vals.clone() + regularization = op.regularization + + if regularization is not None: + D += regularization + + V_left = gradient_provider.jacobian_prod(left, V.t()) + D_inv = 1.0 / D + + if right is None: + V_right = V_left + else: + V_right = gradient_provider.jacobian_prod(right, V.t()) + + V_right = V_right * D_inv.unsqueeze(-1) + + return torch.einsum("ij, ik -> jk", V_left, V_right) + + OperatorBilinearFormType = TypeVar( "OperatorBilinearFormType", bound=OperatorBilinearForm ) @@ -653,7 +739,11 @@ def block_names(self) -> List[str]: @property def n_parameters(self): - return sum(block.op.input_size for _, block in self.block_mapper.items()) + return sum( + param.numel() + for block in self.parameter_dict.values() + for param in block.values() + ) @abstractmethod def with_regularization( diff --git a/src/pydvl/influence/torch/functional.py b/src/pydvl/influence/torch/functional.py index bb9859995..f07cc3983 100644 --- a/src/pydvl/influence/torch/functional.py +++ b/src/pydvl/influence/torch/functional.py @@ -26,6 +26,7 @@ from __future__ import annotations import logging +import warnings from dataclasses import dataclass from functools import partial from typing import TYPE_CHECKING, Callable, Dict, Optional, Tuple, Union @@ -55,12 +56,13 @@ "create_per_sample_gradient_function", "create_matrix_jacobian_product_function", "create_per_sample_mixed_derivative_function", - "model_hessian_low_rank", "LowRankProductRepresentation", "randomized_nystroem_approximation", "model_hessian_nystroem_approximation", "create_batch_loss_function", "hvp", + "operator_spectral_approximation", + "operator_nystroem_approximation", ] @@ -708,204 +710,6 @@ def __post_init__(self): raise ValueError("eigen_vals and projections must be on the same device.") -def lanzcos_low_rank_hessian_approx( - hessian_vp: Callable[[torch.Tensor], torch.Tensor], - matrix_shape: Tuple[int, int], - hessian_perturbation: float = 0.0, - rank_estimate: int = 10, - krylov_dimension: Optional[int] = None, - tol: float = 1e-6, - max_iter: Optional[int] = None, - device: Optional[torch.device] = None, - eigen_computation_on_gpu: bool = False, - torch_dtype: Optional[torch.dtype] = None, -) -> LowRankProductRepresentation: - r""" - Calculates a low-rank approximation of the Hessian matrix of a scalar-valued - function using the implicitly restarted Lanczos algorithm, i.e.: - - \[ H_{\text{approx}} = V D V^T\] - - where \(D\) is a diagonal matrix with the top (in absolute value) `rank_estimate` - eigenvalues of the Hessian and \(V\) contains the corresponding eigenvectors. - - Args: - hessian_vp: A function that takes a vector and returns the product of - the Hessian of the loss function. - matrix_shape: The shape of the matrix, represented by the hessian vector - product. - hessian_perturbation: Regularization parameter added to the - Hessian-vector product for numerical stability. - rank_estimate: The number of eigenvalues and corresponding eigenvectors - to compute. Represents the desired rank of the Hessian approximation. - krylov_dimension: The number of Krylov vectors to use for the Lanczos - method. If not provided, it defaults to - \( \min(\text{model.n_parameters}, - \max(2 \times \text{rank_estimate} + 1, 20)) \). - tol: The stopping criteria for the Lanczos algorithm, which stops when - the difference in the approximated eigenvalue is less than `tol`. - Defaults to 1e-6. - max_iter: The maximum number of iterations for the Lanczos method. If - not provided, it defaults to \( 10 \cdot \text{model.n_parameters}\). - device: The device to use for executing the hessian vector product. - eigen_computation_on_gpu: If True, tries to execute the eigen pair - approximation on the provided device via [cupy](https://cupy.dev/) - implementation. Ensure that either your model is small enough, or you - use a small rank_estimate to fit your device's memory. If False, the - eigen pair approximation is executed on the CPU with scipy's wrapper to - ARPACK. - torch_dtype: If not provided, the current torch default dtype is used for - conversion to torch. - - Returns: - [LowRankProductRepresentation] - [pydvl.influence.torch.functional.LowRankProductRepresentation] - instance that contains the top (up until rank_estimate) eigenvalues - and corresponding eigenvectors of the Hessian. - """ - - torch_dtype = torch.get_default_dtype() if torch_dtype is None else torch_dtype - - if eigen_computation_on_gpu: - try: - import cupy as cp - from cupyx.scipy.sparse.linalg import LinearOperator, eigsh - from torch.utils.dlpack import from_dlpack, to_dlpack - except ImportError as e: - raise ImportError( - f"Try to install missing dependencies or set eigen_computation_on_gpu " - f"to False: {e}" - ) - - if device is None: - raise ValueError( - "Without setting an explicit device, cupy is not supported" - ) - - def to_torch_conversion_function(x: cp.NDArray) -> torch.Tensor: - return from_dlpack(x.toDlpack()).to(torch_dtype) - - def mv(x): - x = to_torch_conversion_function(x) - y = hessian_vp(x) + hessian_perturbation * x - return cp.from_dlpack(to_dlpack(y)) - - else: - from scipy.sparse.linalg import LinearOperator, eigsh - - def mv(x): - x_torch = torch.as_tensor(x, device=device, dtype=torch_dtype) - y = ( - (hessian_vp(x_torch) + hessian_perturbation * x_torch) - .detach() - .cpu() - .numpy() - ) - return y - - to_torch_conversion_function = partial(torch.as_tensor, dtype=torch_dtype) - - try: - eigen_vals, eigen_vecs = eigsh( - LinearOperator(matrix_shape, matvec=mv), - k=rank_estimate, - maxiter=max_iter, - tol=tol, - ncv=krylov_dimension, - return_eigenvectors=True, - ) - - except ArpackNoConvergence as e: - logger.warning( - f"ARPACK did not converge for parameters {max_iter=}, {tol=}, " - f"{krylov_dimension=}, {rank_estimate=}. \n " - f"Returning the best approximation found so far. " - f"Use those with care or modify parameters.\n Original error: {e}" - ) - - eigen_vals, eigen_vecs = e.eigenvalues, e.eigenvectors - - eigen_vals = to_torch_conversion_function(eigen_vals) - eigen_vecs = to_torch_conversion_function(eigen_vecs) - - return LowRankProductRepresentation(eigen_vals, eigen_vecs) - - -def model_hessian_low_rank( - model: torch.nn.Module, - loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], - training_data: DataLoader, - hessian_perturbation: float = 0.0, - rank_estimate: int = 10, - krylov_dimension: Optional[int] = None, - tol: float = 1e-6, - max_iter: Optional[int] = None, - eigen_computation_on_gpu: bool = False, - precompute_grad: bool = False, -) -> LowRankProductRepresentation: - r""" - Calculates a low-rank approximation of the Hessian matrix of the model's - loss function using the implicitly restarted Lanczos algorithm, i.e. - - \[ H_{\text{approx}} = V D V^T\] - - where \(D\) is a diagonal matrix with the top (in absolute value) `rank_estimate` - eigenvalues of the Hessian and \(V\) contains the corresponding eigenvectors. - - - Args: - model: A PyTorch model instance. The Hessian will be calculated with respect to - this model's parameters. - loss : A callable that computes the loss. - training_data: A DataLoader instance that provides the model's training data. - Used in calculating the Hessian-vector products. - hessian_perturbation: Optional regularization parameter added to the - Hessian-vector product for numerical stability. - rank_estimate: The number of eigenvalues and corresponding eigenvectors to - compute. Represents the desired rank of the Hessian approximation. - krylov_dimension: The number of Krylov vectors to use for the Lanczos method. - If not provided, it defaults to min(model.n_parameters, - max(2*rank_estimate + 1, 20)). - tol: The stopping criteria for the Lanczos algorithm, - which stops when the difference in the approximated eigenvalue is less than - `tol`. Defaults to 1e-6. - max_iter: The maximum number of iterations for the Lanczos method. - If not provided, it defaults to 10*model.n_parameters. - eigen_computation_on_gpu: If True, tries to execute the eigen pair approximation - on the provided device via cupy implementation. - Make sure, that either your model is small enough or you use a - small rank_estimate to fit your device's memory. - If False, the eigen pair approximation is executed on the CPU by - scipy wrapper to ARPACK. - precompute_grad: If True, the full data gradient is precomputed and kept - in memory, which can speed up the hessian vector product computation. - Set this to False, if you can't afford to keep the full computation graph - in memory. - - Returns: - [LowRankProductRepresentation] - [pydvl.influence.torch.functional.LowRankProductRepresentation] - instance that contains the top (up until rank_estimate) eigenvalues - and corresponding eigenvectors of the Hessian. - """ - raw_hvp = create_hvp_function( - model, loss, training_data, use_average=True, precompute_grad=precompute_grad - ) - n_params = sum([p.numel() for p in model.parameters() if p.requires_grad]) - device = next(model.parameters()).device - return lanzcos_low_rank_hessian_approx( - hessian_vp=raw_hvp, - matrix_shape=(n_params, n_params), - hessian_perturbation=hessian_perturbation, - rank_estimate=rank_estimate, - krylov_dimension=krylov_dimension, - tol=tol, - max_iter=max_iter, - device=device, - eigen_computation_on_gpu=eigen_computation_on_gpu, - ) - - def randomized_nystroem_approximation( mat_mat_prod: Union[torch.Tensor, Callable[[torch.Tensor], torch.Tensor]], input_dim: int, @@ -1093,3 +897,113 @@ def mat_mat_prod(x: torch.Tensor): shift_func=shift_func, mat_vec_device=operator.device, ) + + +def operator_spectral_approximation( + operator: "TensorOperator", + rank: int = 10, + krylov_dimension: Optional[int] = None, + tol: float = 1e-6, + max_iter: Optional[int] = None, + eigen_computation_on_gpu: bool = False, +): + r""" + Calculates a low-rank approximation of an operator $H$ using the implicitly + restarted Lanczos algorithm, i.e.: + + \[ H_{\text{approx}} = V D V^T\] + + where \(D\) is a diagonal matrix with the top (in absolute value) `rank` + eigenvalues of the Hessian and \(V\) contains the corresponding eigenvectors. + + Args: + operator: The operator to approximate. + rank: The number of eigenvalues and corresponding eigenvectors + to compute. Represents the desired rank of the Hessian approximation. + krylov_dimension: The number of Krylov vectors to use for the Lanczos + method. If not provided, it defaults to + \( \min(\text{model.n_parameters}, + \max(2 \times \text{rank_estimate} + 1, 20)) \). + tol: The stopping criteria for the Lanczos algorithm, which stops when + the difference in the approximated eigenvalue is less than `tol`. + Defaults to 1e-6. + max_iter: The maximum number of iterations for the Lanczos method. If + not provided, it defaults to \( 10 \cdot \text{model.n_parameters}\). + eigen_computation_on_gpu: If True, tries to execute the eigen pair + approximation on the provided device via [cupy](https://cupy.dev/) + implementation. Ensure that either your model is small enough, or you + use a small rank_estimate to fit your device's memory. If False, the + eigen pair approximation is executed on the CPU with scipy's wrapper to + ARPACK. + + Returns: + [LowRankProductRepresentation] + [pydvl.influence.torch.functional.LowRankProductRepresentation] + instance that contains the top (up until rank_estimate) eigenvalues + and corresponding eigenvectors of the Hessian. + """ + + if operator.input_size == 1: + # in the trivial case, return early + eigen_vec = torch.ones((1, 1), dtype=operator.dtype, device=operator.device) + eigen_val = operator.apply(eigen_vec).squeeze() + return LowRankProductRepresentation(eigen_val, eigen_vec) + + torch_dtype = operator.dtype + + if eigen_computation_on_gpu: + try: + import cupy as cp + from cupyx.scipy.sparse.linalg import LinearOperator, eigsh + from torch.utils.dlpack import from_dlpack, to_dlpack + except ImportError as e: + raise ImportError( + "Missing cupy, check the installation instructions " + "at https://docs.cupy.dev/en/stable/install.html " + "or set eigen_computation_on_gpu " + f"to False: {e}" + ) + + def to_torch_conversion_function(x: cp.NDArray) -> torch.Tensor: + return from_dlpack(x.toDlpack()).to(torch_dtype) + + def mv(x): + x = to_torch_conversion_function(x) + y = operator.apply(x) + return cp.from_dlpack(to_dlpack(y)) + + else: + from scipy.sparse.linalg import LinearOperator, eigsh + + def mv(x): + x_torch = torch.as_tensor(x, device=operator.device, dtype=torch_dtype) + y = operator.apply(x_torch).detach().cpu().numpy() + return y + + to_torch_conversion_function = partial(torch.as_tensor, dtype=torch_dtype) + + try: + matrix_shape = (operator.input_size, operator.input_size) + eigen_vals, eigen_vecs = eigsh( + LinearOperator(matrix_shape, matvec=mv), + k=min(rank, operator.input_size - 1), + maxiter=max_iter, + tol=tol, + ncv=krylov_dimension, + return_eigenvectors=True, + ) + + except ArpackNoConvergence as e: + warnings.warn( + f"ARPACK did not converge for parameters {max_iter=}, {tol=}, " + f"{krylov_dimension=}, {rank=}. \n " + f"Returning the best approximation found so far. " + f"Use those with care or modify parameters.\n Original error: {e}" + ) + + eigen_vals, eigen_vecs = e.eigenvalues, e.eigenvectors + + eigen_vals = to_torch_conversion_function(eigen_vals) + eigen_vecs = to_torch_conversion_function(eigen_vecs) + + return LowRankProductRepresentation(eigen_vals, eigen_vecs) diff --git a/src/pydvl/influence/torch/influence_function_model.py b/src/pydvl/influence/torch/influence_function_model.py index f1f7e1c61..9e7d96325 100644 --- a/src/pydvl/influence/torch/influence_function_model.py +++ b/src/pydvl/influence/torch/influence_function_model.py @@ -6,6 +6,7 @@ from __future__ import annotations +import copy import logging from abc import ABC, abstractmethod from collections import OrderedDict @@ -34,19 +35,16 @@ HessianBatchOperation, ) from .functional import ( - LowRankProductRepresentation, - create_batch_hvp_function, create_hvp_function, - create_matrix_jacobian_product_function, create_per_sample_gradient_function, create_per_sample_mixed_derivative_function, gauss_newton, hessian, - model_hessian_low_rank, - model_hessian_nystroem_approximation, operator_nystroem_approximation, + operator_spectral_approximation, ) from .operator import ( + CgOperator, DirectSolveOperator, GaussNewtonOperator, HessianOperator, @@ -54,7 +52,7 @@ LissaOperator, LowRankOperator, ) -from .pre_conditioner import PreConditioner +from .preconditioner import Preconditioner from .util import ( BlockMode, EkfacRepresentation, @@ -441,7 +439,7 @@ def is_thread_safe(self) -> bool: return False -class CgInfluence(TorchInfluenceFunctionModel): +class CgInfluence(TorchComposableInfluence[CgOperator]): r""" Given a model and training data, it uses conjugate gradient to calculate the inverse of the Hessian Vector Product. More precisely, it finds x such that \(Hx = @@ -453,25 +451,22 @@ class CgInfluence(TorchInfluenceFunctionModel): this model's parameters. loss: A callable that takes the model's output and target as input and returns the scalar loss. - hessian_regularization: Optional regularization parameter added + regularization: Optional regularization parameter added to the Hessian-vector product for numerical stability. - x0: Initial guess for hvp. If None, defaults to b. rtol: Maximum relative tolerance of result. atol: Absolute tolerance of result. maxiter: Maximum number of iterations. If None, defaults to 10*len(b). progress: If True, display progress bars for computing in the non-block mode (use_block_cg=False). - precompute_grad: If True, the full data gradient is precomputed and kept - in memory, which can speed up the hessian vector product computation. - Set this to False, if you can't afford to keep the full computation graph - in memory. - pre_conditioner: Optional pre-conditioner to improve convergence of conjugate + preconditioner: Optional preconditioner to improve convergence of conjugate gradient method - use_block_cg: If True, use block variant of conjugate gradient method, which - solves several right hand sides simultaneously + solve_simultaneously: If True, use a variant of conjugate gradient method to + simultaneously solve for several right hand sides. warn_on_max_iteration: If True, logs a warning, if the desired tolerance is not achieved within `maxiter` iterations. If False, the log level for this information is `logging.DEBUG` + block_structure: Union[BlockMode, OrderedDict[str, List[str]]] = BlockMode.FULL, + second_order_mode: SecondOrderMode = SecondOrderMode.HESSIAN, """ @@ -479,327 +474,84 @@ def __init__( self, model: nn.Module, loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], - hessian_regularization: float = 0.0, - x0: Optional[torch.Tensor] = None, - rtol: float = 1e-7, - atol: float = 1e-7, + regularization: Optional[Union[float, Dict[str, Optional[float]]]] = None, + rtol: float = 1e-4, + atol: float = 1e-6, maxiter: Optional[int] = None, progress: bool = False, precompute_grad: bool = False, - pre_conditioner: Optional[PreConditioner] = None, - use_block_cg: bool = False, + preconditioner: Optional[Preconditioner] = None, + solve_simultaneously: bool = False, warn_on_max_iteration: bool = True, + block_structure: Union[BlockMode, OrderedDict[str, List[str]]] = BlockMode.FULL, + second_order_mode: SecondOrderMode = SecondOrderMode.HESSIAN, ): - super().__init__(model, loss) + super().__init__(model, block_structure, regularization) + self.loss = loss self.warn_on_max_iteration = warn_on_max_iteration - self.use_block_cg = use_block_cg - self.pre_conditioner = pre_conditioner + self.solve_simultaneously = solve_simultaneously + self.preconditioner = preconditioner self.precompute_grad = precompute_grad self.progress = progress self.maxiter = maxiter self.atol = atol self.rtol = rtol - self.x0 = x0 - self.hessian_regularization = hessian_regularization - - train_dataloader: DataLoader - - @property - def is_fitted(self): - try: - return self.train_dataloader is not None - except AttributeError: - return False - - @log_duration(log_level=logging.INFO) - def fit(self, data: DataLoader) -> CgInfluence: - self.train_dataloader = data - if self.pre_conditioner is not None: - hvp = create_hvp_function( - self.model, - self.loss, - self.train_dataloader, - precompute_grad=self.precompute_grad, - ) - - def model_hessian_mat_mat_prod(x: torch.Tensor): - return torch.func.vmap(hvp, in_dims=1, randomness="same")(x).t() - - self.pre_conditioner.fit( - model_hessian_mat_mat_prod, - self.n_parameters, - self.model_dtype, - self.model_device, - self.hessian_regularization, - ) - return self - - @log_duration - def influences( - self, - x_test: torch.Tensor, - y_test: torch.Tensor, - x: Optional[torch.Tensor] = None, - y: Optional[torch.Tensor] = None, - mode: InfluenceMode = InfluenceMode.Up, - ) -> torch.Tensor: - r""" - Compute an approximation of - - \[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, - f_{\theta}(x_{\text{test}})), - \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle, \] - - for the case of up-weighting influence, resp. - - \[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, - f_{\theta}(x_{\text{test}})), - \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \] - - for the case of perturbation-type influence. The approximate action of - $H^{-1}$ is achieved via the [conjugate gradient - method](https://en.wikipedia.org/wiki/Conjugate_gradient_method). + self.second_order_mode = second_order_mode + def with_regularization( + self, regularization: Union[float, Dict[str, Optional[float]]] + ) -> TorchComposableInfluence: + """ + Update the regularization parameter. Args: - x_test: model input to use in the gradient computations of - $H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, - f_{\theta}(x_{\text{test}}))$ - y_test: label tensor to compute gradients - x: optional model input to use in the gradient computations - $\nabla_{\theta}\ell(y, f_{\theta}(x))$, - resp. $\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))$, - if None, use $x=x_{\text{test}}$ - y: optional label tensor to compute gradients - mode: enum value of [InfluenceMode] - [pydvl.influence.base_influence_function_model.InfluenceMode] + regularization: Either a positive float or a dictionary with the + block names as keys and the regularization values as values. Returns: - A tensor representing the element-wise scalar products for the - provided batch. + The modified instance """ - return super().influences(x_test, y_test, x, y, mode=mode) - - @log_duration - def _solve_hvp(self, rhs: torch.Tensor) -> torch.Tensor: - if len(self.train_dataloader) == 0: - raise ValueError("Training dataloader must not be empty.") - - if self.use_block_cg: - return self._solve_pbcg(rhs) - - hvp = create_hvp_function( - self.model, - self.loss, - self.train_dataloader, - precompute_grad=self.precompute_grad, - ) - - def reg_hvp(v: torch.Tensor): - return hvp(v) + self.hessian_regularization * v.type(rhs.dtype) - - y_norm = torch.linalg.norm(rhs, dim=0) - - stopping_val = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2) - - batch_cg = torch.zeros_like(rhs) - - for idx, (bi, _tol) in enumerate( - tqdm( - zip(rhs, stopping_val), - disable=not self.progress, - desc="Conjugate gradient", - ) - ): - batch_result = self._solve_pcg( - reg_hvp, - bi, - tol=_tol, - x0=self.x0, - maxiter=self.maxiter, - ) - batch_cg[idx] = batch_result - - return batch_cg + self._regularization_dict = self._build_regularization_dict(regularization) + for k, reg in self._regularization_dict.items(): + self.block_mapper.composable_block_dict[k].op.regularization = reg + return self - def _solve_pcg( + def _create_block( self, - hvp: Callable[[torch.Tensor], torch.Tensor], - b: torch.Tensor, - *, - tol: float, - x0: Optional[torch.Tensor] = None, - maxiter: Optional[int] = None, - ) -> torch.Tensor: - r""" - Conjugate gradient solver for the Hessian vector product. - - Args: - hvp: A callable Hvp, operating with tensors of size N. - b: A vector or matrix, the right hand side of the equation \(Hx = b\). - x0: Initial guess for hvp. - rtol: Maximum relative tolerance of result. - atol: Absolute tolerance of result. - maxiter: Maximum number of iterations. If None, defaults to 10*len(b). - - Returns: - A tensor with the solution of \(Ax=b\). - """ - - if x0 is None: - x0 = torch.clone(b) - if maxiter is None: - maxiter = len(b) * 10 - - x = x0 - - r0 = b - hvp(x) - - if self.pre_conditioner is not None: - p = z0 = self.pre_conditioner.solve(r0) - else: - p = z0 = r0 - - for k in range(maxiter): - if torch.norm(r0) < tol: - logger.debug(f"Terminated cg after {k} iterations with residuum={r0}") - break - Ap = hvp(p) - alpha = torch.dot(r0, z0) / torch.dot(p, Ap) - x += alpha * p - r = r0 - alpha * Ap - - if self.pre_conditioner is not None: - z = self.pre_conditioner.solve(r) - else: - z = r - - beta = torch.dot(r, z) / torch.dot(r0, z0) + block_params: Dict[str, torch.nn.Parameter], + data: DataLoader, + regularization: Optional[float], + ) -> TorchOperatorGradientComposition: + op: Union[HessianOperator, GaussNewtonOperator] - r0 = r - p = z + beta * p - z0 = z - else: - log_level = logging.WARNING if self.warn_on_max_iteration else logging.DEBUG - logger.log( - log_level, - f"Reached max number of iterations {maxiter=} without " - f"achieving the desired tolerance {tol}. \n" - f"Achieved residuum is {torch.norm(r0)}.\n" - f"Consider increasing 'maxiter', the desired tolerance or the " - f"parameter 'hessian_regularization'.", + if self.second_order_mode is SecondOrderMode.GAUSS_NEWTON: + op = GaussNewtonOperator( + self.model, self.loss, data, restrict_to=block_params ) + else: + op = HessianOperator(self.model, self.loss, data, restrict_to=block_params) - return x - - def _solve_pbcg( - self, - rhs: torch.Tensor, - ): - hvp = create_hvp_function( - self.model, - self.loss, - self.train_dataloader, - precompute_grad=self.precompute_grad, - ) + preconditioner = None + if self.preconditioner is not None: + preconditioner = copy.copy(self.preconditioner).fit(op, regularization) - # The block variant of conjugate gradient is known to suffer from breakdown, - # due to the possibility of rank deficiency of the iterates of the parameter - # matrix P^tAP, which destabilizes the direct solver. - # The paper `Randomized Nyström Preconditioning, - # Frangella, Zachary and Tropp, Joel A. and Udell, Madeleine, - # SIAM J. Matrix Anal. Appl., 2023` - # proposes a simple orthogonalization pre-processing. However, we observed, that - # this stabilization only worked for double precision. We thus implement - # a different stabilization strategy described in - # `A breakdown-free block conjugate gradient method, Ji, Hao and Li, Yaohang, - # BIT Numerical Mathematics, 2017` - - def mat_mat(x: torch.Tensor): - return torch.vmap( - lambda u: hvp(u) + self.hessian_regularization * u, - in_dims=1, - randomness="same", - )(x) - - X = torch.clone(rhs.T) - - R = (rhs - mat_mat(X)).T - Z = R if self.pre_conditioner is None else self.pre_conditioner.solve(R) - P, _, _ = torch.linalg.svd(Z, full_matrices=False) - active_indices = torch.as_tensor( - list(range(X.shape[-1])), dtype=torch.long, device=self.model_device + cg_op = CgOperator( + op, + regularization, + rtol=self.rtol, + atol=self.atol, + maxiter=self.maxiter, + progress=self.progress, + preconditioner=preconditioner, + use_block_cg=self.solve_simultaneously, + warn_on_max_iteration=self.warn_on_max_iteration, ) + gp = TorchGradientProvider(self.model, self.loss, restrict_to=block_params) + return TorchOperatorGradientComposition(cg_op, gp) - maxiter = self.maxiter if self.maxiter is not None else len(rhs) * 10 - y_norm = torch.linalg.norm(rhs, dim=1) - tol = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2) - - # In the case the parameter dimension is smaller than the number of right - # hand sides, we do not shrink the indices due to resulting wrong - # dimensionality of the svd decomposition. We consider this an edge case, which - # does not need optimization - shrink_finished_indices = rhs.shape[0] <= rhs.shape[1] - - for k in range(maxiter): - Q = mat_mat(P).T - p_t_ap = P.T @ Q - alpha = torch.linalg.solve(p_t_ap, P.T @ R) - X[:, active_indices] += P @ alpha - R -= Q @ alpha - - B = torch.linalg.norm(R, dim=0) - non_finished_indices = torch.nonzero(B > tol) - num_remaining_indices = non_finished_indices.numel() - non_finished_indices = non_finished_indices.squeeze() - - if num_remaining_indices == 1: - non_finished_indices = non_finished_indices.unsqueeze(-1) - - if num_remaining_indices == 0: - logger.debug( - f"Terminated block cg after {k} iterations with max " - f"residuum={B.max()}" - ) - break - - # Reduce problem size by removing finished columns from the iteration - if shrink_finished_indices: - active_indices = active_indices[non_finished_indices] - R = R[:, non_finished_indices] - P = P[:, non_finished_indices] - Q = Q[:, non_finished_indices] - p_t_ap = p_t_ap[:, non_finished_indices][non_finished_indices, :] - tol = tol[non_finished_indices] - - Z = R if self.pre_conditioner is None else self.pre_conditioner.solve(R) - beta = -torch.linalg.solve(p_t_ap, Q.T @ Z) - Z_tmp = Z + P @ beta - - if Z_tmp.ndim == 1: - Z_tmp = Z_tmp.unsqueeze(-1) - - # Orthogonalization search directions to stabilize the action of - # (P^tAP)^{-1} - P, _, _ = torch.linalg.svd(Z_tmp, full_matrices=False) - else: - log_level = logging.WARNING if self.warn_on_max_iteration else logging.DEBUG - logger.log( - log_level, - f"Reached max number of iterations {maxiter=} of block cg " - f"without achieving the desired tolerance {tol.min()}. \n" - f"Achieved max residuum is " - f"{torch.linalg.norm(R, dim=0).max()}.\n" - f"Consider increasing 'maxiter', the desired tolerance or " - f"the parameter 'hessian_regularization'.", - ) - - return X.T - - def to(self, device: torch.device): - if self.pre_conditioner is not None: - self.pre_conditioner = self.pre_conditioner.to(device) - return super().to(device) + @property + def is_thread_safe(self) -> bool: + return False class LissaInfluence(TorchComposableInfluence[LissaOperator[BatchOperationType]]): @@ -915,7 +667,7 @@ def is_thread_safe(self) -> bool: return False -class ArnoldiInfluence(TorchInfluenceFunctionModel): +class ArnoldiInfluence(TorchComposableInfluence[LowRankOperator]): r""" Solves the linear system Hx = b, where H is the Hessian of the model's loss function and b is the given right-hand side vector. @@ -935,155 +687,89 @@ class ArnoldiInfluence(TorchInfluenceFunctionModel): this model's parameters. loss: A callable that takes the model's output and target as input and returns the scalar loss. - hessian_regularization: Optional regularization parameter added - to the Hessian-vector product for numerical stability. - rank_estimate: The number of eigenvalues and corresponding eigenvectors + regularization: The regularization parameter. In case a dictionary is provided, + the keys must be a subset of the block identifiers. + rank: The number of eigenvalues and corresponding eigenvectors to compute. Represents the desired rank of the Hessian approximation. krylov_dimension: The number of Krylov vectors to use for the Lanczos method. Defaults to min(model's number of parameters, - max(2 times rank_estimate + 1, 20)). + max(2 times rank + 1, 20)). tol: The stopping criteria for the Lanczos algorithm. - Ignored if `low_rank_representation` is provided. max_iter: The maximum number of iterations for the Lanczos method. - Ignored if `low_rank_representation` is provided. eigen_computation_on_gpu: If True, tries to execute the eigen pair approximation on the model's device via a cupy implementation. Ensure the model size or rank_estimate is appropriate for device memory. If False, the eigen pair approximation is executed on the CPU by the scipy wrapper to ARPACK. - precompute_grad: If True, the full data gradient is precomputed and kept - in memory, which can speed up the hessian vector product computation. - Set this to False, if you can't afford to keep the full computation graph - in memory. + use_woodbury: If True, uses the [Sherman–Morrison–Woodbury + formula](https://en.wikipedia.org/wiki/Woodbury_matrix_identity) for the + computation of the inverse action, which is more precise but needs + additional computation. """ - low_rank_representation: LowRankProductRepresentation - def __init__( self, model: nn.Module, loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], - hessian_regularization: float = 0.0, - rank_estimate: int = 10, + regularization: Optional[Union[float, Dict[str, Optional[float]]]] = None, + rank: int = 10, krylov_dimension: Optional[int] = None, tol: float = 1e-6, max_iter: Optional[int] = None, eigen_computation_on_gpu: bool = False, - precompute_grad: bool = False, + block_structure: Union[BlockMode, OrderedDict[str, List[str]]] = BlockMode.FULL, + second_order_mode: SecondOrderMode = SecondOrderMode.HESSIAN, + use_woodbury: bool = False, ): - super().__init__(model, loss) - self.hessian_regularization = hessian_regularization - self.rank_estimate = rank_estimate + super().__init__(model, block_structure, regularization) + self.use_woodbury = use_woodbury + self.second_order_mode = second_order_mode + self.loss = loss + self.rank = rank self.tol = tol self.max_iter = max_iter self.krylov_dimension = krylov_dimension self.eigen_computation_on_gpu = eigen_computation_on_gpu - self.precompute_grad = precompute_grad - - @property - def is_fitted(self): - try: - return self.low_rank_representation is not None - except AttributeError: - return False - - @log_duration(log_level=logging.INFO) - def fit(self, data: DataLoader) -> ArnoldiInfluence: - r""" - Fitting corresponds to the computation of the low rank decomposition - - \[ V D^{-1} V^T \] - of the Hessian defined by the provided data loader. - - Args: - data: The data to compute the Hessian with. - - Returns: - The fitted instance. - - """ - low_rank_representation = model_hessian_low_rank( - self.model, - self.loss, - data, - hessian_perturbation=0.0, # regularization is applied, when computing values - rank_estimate=self.rank_estimate, - krylov_dimension=self.krylov_dimension, - tol=self.tol, - max_iter=self.max_iter, - eigen_computation_on_gpu=self.eigen_computation_on_gpu, - precompute_grad=self.precompute_grad, - ) - self.low_rank_representation = low_rank_representation.to(self.model_device) + def with_regularization( + self, regularization: Union[float, Dict[str, Optional[float]]] + ) -> TorchComposableInfluence: + self._regularization_dict = self._build_regularization_dict(regularization) + for k, reg in self._regularization_dict.items(): + self.block_mapper.composable_block_dict[k].op.regularization = reg return self - def _non_symmetric_values( + def _create_block( self, - x_test: torch.Tensor, - y_test: torch.Tensor, - x: torch.Tensor, - y: torch.Tensor, - mode: InfluenceMode = InfluenceMode.Up, - ) -> torch.Tensor: - if mode == InfluenceMode.Up: - mjp = create_matrix_jacobian_product_function( - self.model, self.loss, self.low_rank_representation.projections.T - ) - left = mjp(self.model_params, x_test, y_test) - - inverse_regularized_eigenvalues = 1.0 / ( - self.low_rank_representation.eigen_vals + self.hessian_regularization - ) - - right = mjp( - self.model_params, x, y - ) * inverse_regularized_eigenvalues.unsqueeze(-1) - values = torch.einsum("ij, ik -> jk", left, right) - elif mode == InfluenceMode.Perturbation: - factors = self.influence_factors(x_test, y_test) - values = self.influences_from_factors(factors, x, y, mode=mode) - else: - raise UnsupportedInfluenceModeException(mode) - return values - - def _symmetric_values( - self, x: torch.Tensor, y: torch.Tensor, mode: InfluenceMode - ) -> torch.Tensor: - if mode == InfluenceMode.Up: - left = create_matrix_jacobian_product_function( - self.model, self.loss, self.low_rank_representation.projections.T - )(self.model_params, x, y) - inverse_regularized_eigenvalues = 1.0 / ( - self.low_rank_representation.eigen_vals + self.hessian_regularization + block_params: Dict[str, torch.nn.Parameter], + data: DataLoader, + regularization: Optional[float], + ) -> TorchOperatorGradientComposition: + gp = TorchGradientProvider(self.model, self.loss, restrict_to=block_params) + op: Union[HessianOperator, GaussNewtonOperator] + if self.second_order_mode is SecondOrderMode.GAUSS_NEWTON: + op = GaussNewtonOperator( + self.model, self.loss, data, restrict_to=block_params ) - right = left * inverse_regularized_eigenvalues.unsqueeze(-1) - values = torch.einsum("ij, ik -> jk", left, right) - elif mode == InfluenceMode.Perturbation: - factors = self.influence_factors(x, y) - values = self.influences_from_factors(factors, x, y, mode=mode) else: - raise UnsupportedInfluenceModeException(mode) - return values - - @log_duration - def _solve_hvp(self, rhs: torch.Tensor) -> torch.Tensor: - inverse_regularized_eigenvalues = 1.0 / ( - self.low_rank_representation.eigen_vals + self.hessian_regularization + op = HessianOperator(self.model, self.loss, data, restrict_to=block_params) + low_rank_representation = operator_spectral_approximation( + op, + self.rank, + krylov_dimension=self.krylov_dimension, + tol=self.tol, + max_iter=self.max_iter, + eigen_computation_on_gpu=self.eigen_computation_on_gpu, ) - - projected_rhs = self.low_rank_representation.projections.t() @ rhs.t() - result = self.low_rank_representation.projections @ ( - projected_rhs * inverse_regularized_eigenvalues.unsqueeze(-1) + low_rank_op = LowRankOperator( + low_rank_representation, regularization, exact=self.use_woodbury ) + return TorchOperatorGradientComposition(low_rank_op, gp) - return result.t() - - def to(self, device: torch.device): - if self.is_fitted: - self.low_rank_representation = self.low_rank_representation.to(device) - return super().to(device) + @property + def is_thread_safe(self) -> bool: + return False class EkfacInfluence(TorchInfluenceFunctionModel): diff --git a/src/pydvl/influence/torch/operator.py b/src/pydvl/influence/torch/operator.py index 42bc07dfd..9941712d0 100644 --- a/src/pydvl/influence/torch/operator.py +++ b/src/pydvl/influence/torch/operator.py @@ -1,4 +1,5 @@ import logging +import warnings from typing import Callable, Dict, Generic, Optional, Tuple import torch @@ -6,7 +7,13 @@ from torch.utils.data import DataLoader from tqdm import tqdm -from .base import TensorDictOperator, TensorOperator, TorchBatch +from .base import ( + LowRankBilinearForm, + OperatorBilinearForm, + TensorDictOperator, + TensorOperator, + TorchBatch, +) from .batch_operation import ( BatchOperationType, ChunkAveraging, @@ -17,6 +24,8 @@ TensorAveragingType, ) from .functional import LowRankProductRepresentation +from .preconditioner import Preconditioner +from .util import LossType logger = logging.getLogger(__name__) @@ -498,7 +507,7 @@ class LowRankOperator(TensorOperator): def __init__( self, low_rank_representation: LowRankProductRepresentation, - regularization: float, + regularization: Optional[float] = None, exact: bool = True, ): @@ -525,6 +534,10 @@ def regularization(self, value: float): raise ValueError("regularization must be non-negative") self._regularization = value + @property + def low_rank_representation(self) -> LowRankProductRepresentation: + return self._low_rank_representation + @property def device(self): return self._low_rank_representation.device @@ -565,3 +578,311 @@ def _apply_to_mat(self, mat: torch.Tensor) -> torch.Tensor: def input_size(self) -> int: result: int = self._low_rank_representation.projections.shape[0] return result + + def as_bilinear_form(self) -> LowRankBilinearForm: + return LowRankBilinearForm(self) + + +class MatrixOperator(TensorOperator): + """ + A simple wrapper for a [torch.Tensor][torch.Tensor] acting as a matrix mapping. + """ + + def __init__(self, matrix: torch.Tensor): + self.matrix = matrix + + @property + def device(self): + return self.matrix.device + + @property + def dtype(self): + return self.matrix.dtype + + def to(self, device: torch.device): + self.matrix = self.matrix.to(device) + + def _apply_to_vec(self, vec: torch.Tensor) -> torch.Tensor: + return self._apply_to_mat(vec.unsqueeze(dim=0)) + + def _apply_to_mat(self, mat: torch.Tensor) -> torch.Tensor: + return (self.matrix @ mat.t()).t() + + @property + def input_size(self) -> int: + result: int = self.matrix.shape[-1] + return result + + +class CgOperator(TensorOperator): + r""" + Given an operator , it uses conjugate gradient to calculate the + action of its inverse. More precisely, it finds x such that \(Ax = + A\), with \(A\) being the matrix represented by the operator. For more info, see + [Conjugate Gradient][conjugate-gradient]. + + Args: + operator: + regularization: Optional regularization parameter added + to the matrix vector product for numerical stability. + rtol: Maximum relative tolerance of result. + atol: Absolute tolerance of result. + maxiter: Maximum number of iterations. If None, defaults to 10*len(b). + progress: If True, display progress bars for computing in the non-block mode + (use_block_cg=False). + preconditioner: Optional pre-conditioner to improve convergence of conjugate + gradient method + use_block_cg: If True, use block variant of conjugate gradient method, which + solves several right hand sides simultaneously + warn_on_max_iteration: If True, logs a warning, if the desired tolerance is not + achieved within `maxiter` iterations. If False, the log level for this + information is `logging.DEBUG` + + """ + + def __init__( + self, + operator: TensorOperator, + regularization: Optional[float] = None, + rtol: float = 1e-7, + atol: float = 1e-7, + maxiter: Optional[int] = None, + progress: bool = False, + preconditioner: Optional[Preconditioner] = None, + use_block_cg: bool = False, + warn_on_max_iteration: bool = True, + ): + + if regularization is not None and regularization < 0: + raise ValueError("regularization must be non-negative") + + self.progress = progress + self.warn_on_max_iteration = warn_on_max_iteration + self.use_block_cg = use_block_cg + self.preconditioner = preconditioner + self.maxiter = maxiter + self.atol = atol + self.rtol = rtol + self._regularization = regularization + self.operator = operator + + @property + def regularization(self): + return self._regularization + + @regularization.setter + def regularization(self, value: float): + if value < 0: + raise ValueError("regularization must be non-negative") + self._regularization = value + if self.preconditioner is not None: + if self.preconditioner.modify_regularization_requires_fit: + warnings.warn( + "Modifying the regularization value requires " + "re-fitting the preconditioner" + ) + self.preconditioner.fit(self.operator, value) + else: + self.preconditioner.regularization = value + + @property + def device(self): + return self.operator.device + + @property + def dtype(self): + return self.operator.dtype + + def to(self, device: torch.device): + self.operator = self.operator.to(device) + if self.preconditioner is not None: + self.preconditioner = self.preconditioner.to(device) + return self + + def _reg_operator_apply(self, x: torch.Tensor): + result = self.operator.apply(x) + if self._regularization is not None: + result += self._regularization * x + return result + + @property + def input_size(self) -> int: + return self.operator.input_size + + def _apply_to_vec(self, vec: torch.Tensor) -> torch.Tensor: + return self._apply_to_mat(vec.unsqueeze(0)) + + def _apply_to_mat(self, mat: torch.Tensor) -> torch.Tensor: + + if self.use_block_cg: + return self._solve_pbcg(mat) + + y_norm = torch.linalg.norm(mat, dim=0) + + stopping_val = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2) + + batch_cg = torch.zeros_like(mat) + + for idx, (bi, _tol) in enumerate( + tqdm( + zip(mat, stopping_val), + disable=not self.progress, + desc="Conjugate gradient", + ) + ): + batch_result = self._solve_pcg(bi, _tol) + batch_cg[idx] = batch_result + + return batch_cg + + def _solve_pcg( + self, + b: torch.Tensor, + tol: float, + ) -> torch.Tensor: + + x0 = torch.clone(b) + maxiter = self.maxiter + if maxiter is None: + maxiter = len(b) * 10 + + x = x0 + + r0 = b - self._reg_operator_apply(x) + + if self.preconditioner is not None: + p = z0 = self.preconditioner.solve(r0) + else: + p = z0 = r0 + + residuum = torch.norm(r0) + + for k in range(maxiter): + if residuum < tol: + logger.debug( + f"Terminated cg after {k} iterations with residuum={residuum}" + ) + break + Ap = self._reg_operator_apply(p) + alpha = torch.dot(r0, z0) / torch.dot(p, Ap) + x += alpha * p + r = r0 - alpha * Ap + + if self.preconditioner is not None: + z = self.preconditioner.solve(r) + else: + z = r + + beta = torch.dot(r, z) / torch.dot(r0, z0) + + r0 = r + residuum = torch.norm(r0) + p = z + beta * p + z0 = z + else: + log_msg = ( + f"Reached max number of iterations {maxiter=} without " + f"achieving the desired tolerance {tol}. \n" + f"Achieved residuum is {residuum}.\n" + f"Consider increasing 'maxiter', the desired tolerance or the " + f"parameter 'hessian_regularization'." + ) + if self.warn_on_max_iteration: + warnings.warn(log_msg) + else: + logger.debug(log_msg) + return x + + def _solve_pbcg( + self, + rhs: torch.Tensor, + ): + + # The block variant of conjugate gradient is known to suffer from breakdown, + # due to the possibility of rank deficiency of the iterates of the parameter + # matrix P^tAP, which destabilizes the direct solver. + # The paper `Randomized Nyström Preconditioning, + # Frangella, Zachary and Tropp, Joel A. and Udell, Madeleine, + # SIAM J. Matrix Anal. Appl., 2023` + # proposes a simple orthogonalization pre-processing. However, we observed, that + # this stabilization only worked for double precision. We thus implement + # a different stabilization strategy described in + # `A breakdown-free block conjugate gradient method, Ji, Hao and Li, Yaohang, + # BIT Numerical Mathematics, 2017` + + X = torch.clone(rhs.T) + + R = (rhs - self._reg_operator_apply(X.t())).T + B = torch.linalg.norm(R, dim=0) + Z = R if self.preconditioner is None else self.preconditioner.solve(R) + P, _, _ = torch.linalg.svd(Z, full_matrices=False) + active_indices = torch.as_tensor( + list(range(X.shape[-1])), dtype=torch.long, device=self.device + ) + + maxiter = self.maxiter if self.maxiter is not None else len(rhs) * 10 + y_norm = torch.linalg.norm(rhs, dim=1) + tol = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2) + + # In the case the parameter dimension is smaller than the number of right + # hand sides, we do not shrink the indices due to resulting wrong + # dimensionality of the svd decomposition. We consider this an edge case, which + # does not need optimization + shrink_finished_indices = rhs.shape[0] <= rhs.shape[1] + + for k in range(maxiter): + Q = self._reg_operator_apply(P.t()).T + p_t_ap = P.T @ Q + alpha = torch.linalg.solve(p_t_ap, P.T @ R) + X[:, active_indices] += P @ alpha + R -= Q @ alpha + + B = torch.linalg.norm(R, dim=0) + non_finished_indices = torch.nonzero(B > tol) + num_remaining_indices = non_finished_indices.numel() + non_finished_indices = non_finished_indices.squeeze() + + if num_remaining_indices == 1: + non_finished_indices = non_finished_indices.unsqueeze(-1) + + if num_remaining_indices == 0: + logger.debug( + f"Terminated block cg after {k} iterations with max " + f"residuum={B.max()}" + ) + break + + # Reduce problem size by removing finished columns from the iteration + if shrink_finished_indices: + active_indices = active_indices[non_finished_indices] + R = R[:, non_finished_indices] + P = P[:, non_finished_indices] + Q = Q[:, non_finished_indices] + p_t_ap = p_t_ap[:, non_finished_indices][non_finished_indices, :] + tol = tol[non_finished_indices] + + Z = R if self.preconditioner is None else self.preconditioner.solve(R) + beta = -torch.linalg.solve(p_t_ap, Q.T @ Z) + Z_tmp = Z + P @ beta + + if Z_tmp.ndim == 1: + Z_tmp = Z_tmp.unsqueeze(-1) + + # Orthogonalization search directions to stabilize the action of + # (P^tAP)^{-1} + P, _, _ = torch.linalg.svd(Z_tmp, full_matrices=False) + else: + log_msg = ( + f"Reached max number of iterations {maxiter=} of block cg " + f"without achieving the desired tolerance {tol.min()}. \n" + f"Achieved max residuum is " + f"{B.max()}.\n" + f"Consider increasing 'maxiter', the desired tolerance or " + f"the parameter 'hessian_regularization'." + ) + if self.warn_on_max_iteration: + warnings.warn(log_msg) + else: + logger.debug(log_msg) + + return X.T diff --git a/src/pydvl/influence/torch/pre_conditioner.py b/src/pydvl/influence/torch/preconditioner.py similarity index 57% rename from src/pydvl/influence/torch/pre_conditioner.py rename to src/pydvl/influence/torch/preconditioner.py index b4c629285..432ec81c5 100644 --- a/src/pydvl/influence/torch/pre_conditioner.py +++ b/src/pydvl/influence/torch/preconditioner.py @@ -1,17 +1,20 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Callable, Optional +from typing import TYPE_CHECKING, Optional import torch from ...utils.exceptions import NotFittedException -from .functional import LowRankProductRepresentation, randomized_nystroem_approximation +from .functional import LowRankProductRepresentation, operator_nystroem_approximation -__all__ = ["JacobiPreConditioner", "NystroemPreConditioner", "PreConditioner"] +if TYPE_CHECKING: + from .operator import TensorOperator +__all__ = ["JacobiPreconditioner", "NystroemPreconditioner", "Preconditioner"] -class PreConditioner(ABC): + +class Preconditioner(ABC): r""" Abstract base class for implementing pre-conditioners for improving the convergence of CG for systems of the form @@ -22,34 +25,57 @@ class PreConditioner(ABC): condition number than $A + \lambda \operatorname{I}$. """ + _reg: Optional[float] + + @property + def regularization(self): + return self._reg + + @regularization.setter + def regularization(self, value: float): + if self.modify_regularization_requires_fit: + raise NotImplementedError( + f"Adapting regularization for instances of type " + f"{type(self)} without re-fitting is not " + f"supported. Call the fit method instead." + ) + self._validate_regularization(value) + self._reg = value + + def _validate_regularization(self, value: Optional[float]): + if value is not None and value < 0: + raise ValueError("regularization must be non-negative") @property @abstractmethod - def is_fitted(self): + def modify_regularization_requires_fit(self) -> bool: pass + @property @abstractmethod + def is_fitted(self): + pass + def fit( self, - mat_mat_prod: Callable[[torch.Tensor], torch.Tensor], - size: int, - dtype: torch.dtype, - device: torch.device, - regularization: float = 0.0, + operator: "TensorOperator", + regularization: Optional[float] = None, ): r""" Implement this to fit the pre-conditioner to the matrix represented by the mat_mat_prod Args: - mat_mat_prod: a callable that computes the matrix-matrix product - size: size of the matrix represented by `mat_mat_prod` - dtype: data type of the matrix represented by `mat_mat_prod` - device: device of the matrix represented by `mat_mat_prod` + operator: The preconditioner is fitted to this operator regularization: regularization parameter $\lambda$ in the equation $ ( A + \lambda \operatorname{I})x = \operatorname{rhs} $ Returns: self """ + self._validate_regularization(regularization) + return self._fit(operator, regularization) + + @abstractmethod + def _fit(self, operator: "TensorOperator", regularization: Optional[float] = None): pass def solve(self, rhs: torch.Tensor): @@ -73,12 +99,12 @@ def _solve(self, rhs: torch.Tensor): pass @abstractmethod - def to(self, device: torch.device) -> PreConditioner: + def to(self, device: torch.device) -> Preconditioner: """Implement this to move the (potentially fitted) preconditioner to a specific device""" -class JacobiPreConditioner(PreConditioner): +class JacobiPreconditioner(Preconditioner): r""" Pre-conditioner for improving the convergence of CG for systems of the form @@ -101,60 +127,70 @@ class JacobiPreConditioner(PreConditioner): """ _diag: torch.Tensor - _reg: float def __init__(self, num_samples_estimator: int = 1): self.num_samples_estimator = num_samples_estimator @property def is_fitted(self): - return self._diag is not None and self._reg is not None + has_diag = hasattr(self, "_diag") and self._diag is not None + has_regularization = hasattr(self, "_reg") + return has_diag and has_regularization - def fit( + @property + def modify_regularization_requires_fit(self) -> bool: + return False + + def _fit( self, - mat_mat_prod: Callable[[torch.Tensor], torch.Tensor], - size: int, - dtype: torch.dtype, - device: torch.device, - regularization: float = 0.0, + operator: "TensorOperator", + regularization: Optional[float] = None, ): r""" Fits by computing an estimate of the diagonal of the matrix represented by - `mat_mat_prod` via Hutchinson's estimator + a [TensorOperator][pydvl.influence.torch.base.TensorOperator] + via Hutchinson's estimator Args: - mat_mat_prod: a callable representing the matrix-matrix product - size: size of the square matrix - dtype: needed data type of inputs for the mat_mat_prod - device: needed device for inputs of mat_mat_prod + operator: The preconditioner is fitted to this operator regularization: regularization parameter $\lambda$ in $(A+\lambda I)x=b$ """ random_samples = torch.randn( - size, self.num_samples_estimator, device=device, dtype=dtype + self.num_samples_estimator, + operator.input_size, + device=operator.device, + dtype=operator.dtype, ) + diagonal_estimate = torch.sum( - torch.mul(random_samples, mat_mat_prod(random_samples)), dim=1 + torch.mul(random_samples, operator.apply(random_samples)), dim=0 ) + diagonal_estimate /= self.num_samples_estimator self._diag = diagonal_estimate self._reg = regularization def _solve(self, rhs: torch.Tensor): - inv_diag = 1.0 / (self._diag + self._reg) + diag = self._diag + + if self._reg is not None: + diag = diag + self._reg + + inv_diag = 1.0 / diag if rhs.ndim == 1: return rhs * inv_diag return rhs * inv_diag.unsqueeze(-1) - def to(self, device: torch.device) -> JacobiPreConditioner: + def to(self, device: torch.device) -> JacobiPreconditioner: if self._diag is not None: self._diag = self._diag.to(device) return self -class NystroemPreConditioner(PreConditioner): +class NystroemPreconditioner(Preconditioner): r""" Pre-conditioner for improving the convergence of CG for systems of the form @@ -177,7 +213,6 @@ class NystroemPreConditioner(PreConditioner): """ _low_rank_approx: LowRankProductRepresentation - _regularization: float def __init__(self, rank: int): self._rank = rank @@ -192,61 +227,61 @@ def rank(self): @property def is_fitted(self): - return self._low_rank_approx is not None and self._regularization is not None + has_low_rank_approx = ( + hasattr(self, "_low_rank_approx") and self._low_rank_approx is not None + ) + has_regularization = hasattr(self, "_reg") and self._reg is not None + return has_low_rank_approx and has_regularization - def fit( + @property + def modify_regularization_requires_fit(self) -> bool: + return False + + def _fit( self, - mat_mat_prod: Callable[[torch.Tensor], torch.Tensor], - size: int, - dtype: torch.dtype, - device: torch.device, - regularization: float = 0.0, + operator: "TensorOperator", + regularization: Optional[float] = None, ): r""" Fits by computing a low-rank approximation of the matrix represented by `mat_mat_prod` via Nystroem approximation Args: - mat_mat_prod: a callable representing the matrix-matrix product - size: size of the square matrix - dtype: needed data type of inputs for the mat_mat_prod - device: needed device for inputs of mat_mat_prod + operator: The preconditioner is fitted to this operator regularization: regularization parameter $\lambda$ in $(A+\lambda I)x=b$ """ - self._low_rank_approx = randomized_nystroem_approximation( - mat_mat_prod, size, self._rank, dtype, mat_vec_device=device - ) - self._regularization = regularization + self._low_rank_approx = operator_nystroem_approximation(operator, self._rank) + self._reg = regularization def _solve(self, rhs: torch.Tensor): rhs_is_one_dim = rhs.ndim == 1 + b = torch.atleast_2d(rhs).t() if rhs_is_one_dim else rhs - rhs_view = torch.atleast_2d(rhs).t() if rhs_is_one_dim else rhs + U = self._low_rank_approx.projections - regularized_eigenvalues = ( - self._low_rank_approx.eigen_vals + self._regularization - ) - lambda_rank = self._low_rank_approx.eigen_vals[-1] + self._regularization + Sigma = self._low_rank_approx.eigen_vals + lambda_rank = self._low_rank_approx.eigen_vals[-1] - proj_rhs = self._low_rank_approx.projections.t() @ rhs_view + if self._reg is not None: + Sigma = Sigma + self._reg + lambda_rank = lambda_rank + self._reg - inverse_regularized_eigenvalues = lambda_rank / regularized_eigenvalues + U_t_b = U.t() @ b - result = self._low_rank_approx.projections @ ( - proj_rhs * inverse_regularized_eigenvalues.unsqueeze(-1) - ) + Sigma_inv = lambda_rank / Sigma - result += rhs_view - self._low_rank_approx.projections @ proj_rhs + result = U @ (U_t_b * Sigma_inv.unsqueeze(-1)) + result += b - U @ U_t_b if rhs_is_one_dim: result = result.squeeze() return result - def to(self, device: torch.device) -> NystroemPreConditioner: + def to(self, device: torch.device) -> NystroemPreconditioner: if self._low_rank_approx is not None: self._low_rank_approx = self._low_rank_approx.to(device) return self diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index 8f72ccf27..147ae1d7a 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -272,7 +272,7 @@ def plot_shapley( def plot_influence_distribution( - influences: NDArray[np.float_], index: int, title_extra: str = "" + influences: NDArray[np.float64], index: int, title_extra: str = "" ) -> plt.Axes: """Plots the histogram of the influence that all samples in the training set have over a single sample index. @@ -292,7 +292,7 @@ def plot_influence_distribution( def plot_influence_distribution_by_label( - influences: NDArray[np.float_], labels: NDArray[np.float_], title_extra: str = "" + influences: NDArray[np.float64], labels: NDArray[np.float64], title_extra: str = "" ): """Plots the histogram of the influence that all samples in the training set have over a single sample index, separated by labels. diff --git a/src/pydvl/reporting/scores.py b/src/pydvl/reporting/scores.py index 671765cd8..5b1c09f07 100644 --- a/src/pydvl/reporting/scores.py +++ b/src/pydvl/reporting/scores.py @@ -13,7 +13,7 @@ def compute_removal_score( u: Utility, values: ValuationResult, - percentages: Union[NDArray[np.float_], Iterable[float]], + percentages: Union[NDArray[np.float64], Iterable[float]], *, remove_best: bool = False, progress: bool = False, diff --git a/src/pydvl/utils/numeric.py b/src/pydvl/utils/numeric.py index d79cbefec..6b6533508 100644 --- a/src/pydvl/utils/numeric.py +++ b/src/pydvl/utils/numeric.py @@ -279,20 +279,20 @@ def running_moments( @overload def running_moments( - previous_avg: NDArray[np.float_], - previous_variance: NDArray[np.float_], + previous_avg: NDArray[np.float64], + previous_variance: NDArray[np.float64], count: int, - new_value: NDArray[np.float_], -) -> Tuple[NDArray[np.float_], NDArray[np.float_]]: + new_value: NDArray[np.float64], +) -> Tuple[NDArray[np.float64], NDArray[np.float64]]: ... def running_moments( - previous_avg: float | NDArray[np.float_], - previous_variance: float | NDArray[np.float_], + previous_avg: float | NDArray[np.float64], + previous_variance: float | NDArray[np.float64], count: int, - new_value: float | NDArray[np.float_], -) -> Tuple[float | NDArray[np.float_], float | NDArray[np.float_]]: + new_value: float | NDArray[np.float64], +) -> Tuple[float | NDArray[np.float64], float | NDArray[np.float64]]: """Uses Welford's algorithm to calculate the running average and variance of a set of numbers. @@ -323,7 +323,7 @@ def running_moments( def top_k_value_accuracy( - y_true: NDArray[np.float_], y_pred: NDArray[np.float_], k: int = 3 + y_true: NDArray[np.float64], y_pred: NDArray[np.float64], k: int = 3 ) -> float: """Computes the top-k accuracy for the estimated values by comparing indices of the highest k values. diff --git a/src/pydvl/utils/score.py b/src/pydvl/utils/score.py index f077c9a53..05b47266b 100644 --- a/src/pydvl/utils/score.py +++ b/src/pydvl/utils/score.py @@ -64,7 +64,7 @@ class Scorer: """ _name: str - range: NDArray[np.float_] + range: NDArray[np.float64] def __init__( self, diff --git a/src/pydvl/value/least_core/common.py b/src/pydvl/value/least_core/common.py index 487e015b5..0e0ceb553 100644 --- a/src/pydvl/value/least_core/common.py +++ b/src/pydvl/value/least_core/common.py @@ -29,8 +29,8 @@ class LeastCoreProblem(NamedTuple): - utility_values: NDArray[np.float_] - A_lb: NDArray[np.float_] + utility_values: NDArray[np.float64] + A_lb: NDArray[np.float64] def lc_solve_problem( @@ -115,7 +115,7 @@ def lc_solve_problem( solver_options=solver_options, ) - values: Optional[NDArray[np.float_]] + values: Optional[NDArray[np.float64]] if subsidy is None: logger.debug("No values were found") @@ -221,13 +221,13 @@ def _map_func( def _solve_least_core_linear_program( - A_eq: NDArray[np.float_], - b_eq: NDArray[np.float_], - A_lb: NDArray[np.float_], - b_lb: NDArray[np.float_], + A_eq: NDArray[np.float64], + b_eq: NDArray[np.float64], + A_lb: NDArray[np.float64], + b_lb: NDArray[np.float64], solver_options: dict, non_negative_subsidy: bool = False, -) -> Tuple[Optional[NDArray[np.float_]], Optional[float]]: +) -> Tuple[Optional[NDArray[np.float64]], Optional[float]]: r"""Solves the Least Core's linear program using cvxopt. $$ @@ -299,12 +299,12 @@ def _solve_least_core_linear_program( def _solve_egalitarian_least_core_quadratic_program( subsidy: float, - A_eq: NDArray[np.float_], - b_eq: NDArray[np.float_], - A_lb: NDArray[np.float_], - b_lb: NDArray[np.float_], + A_eq: NDArray[np.float64], + b_eq: NDArray[np.float64], + A_lb: NDArray[np.float64], + b_lb: NDArray[np.float64], solver_options: dict, -) -> Optional[NDArray[np.float_]]: +) -> Optional[NDArray[np.float64]]: r"""Solves the egalitarian Least Core's quadratic program using cvxopt. $$ diff --git a/src/pydvl/value/result.py b/src/pydvl/value/result.py index 76a7ff28b..6a714e1bf 100644 --- a/src/pydvl/value/result.py +++ b/src/pydvl/value/result.py @@ -202,9 +202,9 @@ class ValuationResult( """ _indices: NDArray[IndexT] - _values: NDArray[np.float_] + _values: NDArray[np.float64] _counts: NDArray[np.int_] - _variances: NDArray[np.float_] + _variances: NDArray[np.float64] _data: Dataset _names: NDArray[NameT] _algorithm: str @@ -216,8 +216,8 @@ class ValuationResult( def __init__( self, *, - values: NDArray[np.float_], - variances: Optional[NDArray[np.float_]] = None, + values: NDArray[np.float64], + variances: Optional[NDArray[np.float64]] = None, counts: Optional[NDArray[np.int_]] = None, indices: Optional[NDArray[IndexT]] = None, data_names: Optional[Sequence[NameT] | NDArray[NameT]] = None, @@ -299,20 +299,20 @@ def sort( self._sort_order = reverse @property - def values(self) -> NDArray[np.float_]: + def values(self) -> NDArray[np.float64]: """The values, possibly sorted.""" return self._values[self._sort_positions] @property - def variances(self) -> NDArray[np.float_]: + def variances(self) -> NDArray[np.float64]: """The variances, possibly sorted.""" return self._variances[self._sort_positions] @property - def stderr(self) -> NDArray[np.float_]: + def stderr(self) -> NDArray[np.float64]: """The raw standard errors, possibly sorted.""" return cast( - NDArray[np.float_], np.sqrt(self.variances / np.maximum(1, self.counts)) + NDArray[np.float64], np.sqrt(self.variances / np.maximum(1, self.counts)) ) @property diff --git a/src/pydvl/value/shapley/classwise.py b/src/pydvl/value/shapley/classwise.py index 3dab20902..f3982f81b 100644 --- a/src/pydvl/value/shapley/classwise.py +++ b/src/pydvl/value/shapley/classwise.py @@ -166,7 +166,7 @@ def __str__(self): def __call__( self: "ClasswiseScorer", model: SupervisedModel, - x_test: NDArray[np.float_], + x_test: NDArray[np.float64], y_test: NDArray[np.int_], ) -> float: ( @@ -180,7 +180,7 @@ def __call__( def estimate_in_class_and_out_of_class_score( self, model: SupervisedModel, - x_test: NDArray[np.float_], + x_test: NDArray[np.float64], y_test: NDArray[np.int_], rescale_scores: bool = True, ) -> Tuple[float, float]: diff --git a/src/pydvl/value/shapley/gt.py b/src/pydvl/value/shapley/gt.py index 6ced22139..17d83e69b 100644 --- a/src/pydvl/value/shapley/gt.py +++ b/src/pydvl/value/shapley/gt.py @@ -49,7 +49,7 @@ log = logging.getLogger(__name__) -T = TypeVar("T", NDArray[np.float_], float) +T = TypeVar("T", NDArray[np.float64], float) GTConstants = namedtuple("GTConstants", ["kk", "Z", "q", "q_tot", "T"]) @@ -266,7 +266,7 @@ def reducer( results_it: Iterable[Tuple[NDArray, NDArray]] ) -> Tuple[NDArray, NDArray]: return np.concatenate(list(x[0] for x in results_it)).astype( - np.float_ + np.float64 ), np.concatenate(list(x[1] for x in results_it)).astype(np.int_) seed_sequence = ensure_seed_sequence(seed) diff --git a/src/pydvl/value/shapley/knn.py b/src/pydvl/value/shapley/knn.py index 5e3a28ae9..06cb9903d 100644 --- a/src/pydvl/value/shapley/knn.py +++ b/src/pydvl/value/shapley/knn.py @@ -73,7 +73,7 @@ def knn_shapley(u: Utility, *, progress: bool = True) -> ValuationResult: # closest to farthest _, indices = nns.kneighbors(u.data.x_test) - values: NDArray[np.float_] = np.zeros_like(u.data.indices, dtype=np.float_) + values: NDArray[np.float64] = np.zeros_like(u.data.indices, dtype=np.float64) n = len(u.data) yt = u.data.y_train iterator = enumerate(zip(u.data.y_test, indices), start=1) diff --git a/src/pydvl/value/stopping.py b/src/pydvl/value/stopping.py index e3947788e..328e8b556 100644 --- a/src/pydvl/value/stopping.py +++ b/src/pydvl/value/stopping.py @@ -575,7 +575,7 @@ class HistoryDeviation(StoppingCriterion): pin_converged: If `True`, once an index has converged, it is pinned """ - _memory: NDArray[np.float_] + _memory: NDArray[np.float64] def __init__( self, @@ -666,7 +666,7 @@ def __init__( raise ValueError("rtol must be in (0, 1)") self.rtol = rtol self.burn_in = burn_in - self._memory: NDArray[np.float_] | None = None + self._memory: NDArray[np.float64] | None = None self._corr = 0.0 self._completion = 0.0 self._iterations = 0 diff --git a/tests/influence/conftest.py b/tests/influence/conftest.py index 7fd7bef17..e5a2ebce3 100644 --- a/tests/influence/conftest.py +++ b/tests/influence/conftest.py @@ -45,10 +45,10 @@ def linear_model(problem_dimension: Tuple[int, int], condition_number: float): def linear_derivative_analytical( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], - x: NDArray[np.float_], - y: NDArray[np.float_], -) -> NDArray[np.float_]: + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], + x: NDArray[np.float64], + y: NDArray[np.float64], +) -> NDArray[np.float64]: """ Given a linear model it returns the first order derivative wrt its parameters. More precisely, given a couple of matrices $A(\theta)$ and $b(\theta')$, with @@ -71,10 +71,10 @@ def linear_derivative_analytical( def linear_hessian_analytical( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], - x: NDArray[np.float_], + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], + x: NDArray[np.float64], lam: float = 0.0, -) -> NDArray[np.float_]: +) -> NDArray[np.float64]: """ Given a linear model it returns the hessian wrt. its parameters. More precisely, given a couple of matrices $A(\theta)$ and $b(\theta')$, with @@ -103,10 +103,10 @@ def linear_hessian_analytical( def linear_mixed_second_derivative_analytical( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], - x: NDArray[np.float_], - y: NDArray[np.float_], -) -> NDArray[np.float_]: + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], + x: NDArray[np.float64], + y: NDArray[np.float64], +) -> NDArray[np.float64]: """ Given a linear model it returns a second order partial derivative wrt its parameters . @@ -137,13 +137,13 @@ def linear_mixed_second_derivative_analytical( def linear_analytical_influence_factors( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], - x: NDArray[np.float_], - y: NDArray[np.float_], - x_test: NDArray[np.float_], - y_test: NDArray[np.float_], + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], + x: NDArray[np.float64], + y: NDArray[np.float64], + x_test: NDArray[np.float64], + y_test: NDArray[np.float64], hessian_regularization: float = 0, -) -> NDArray[np.float_]: +) -> NDArray[np.float64]: """ Given a linear model it calculates its influence factors. :param linear_model: A tuple of arrays representing the linear model. @@ -165,13 +165,13 @@ def linear_analytical_influence_factors( def add_noise_to_linear_model( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], train_set_size: int, test_set_size: int, noise: float = 0.01, ) -> Tuple[ - Tuple[NDArray[np.float_], NDArray[np.float_]], - Tuple[NDArray[np.float_], NDArray[np.float_]], + Tuple[NDArray[np.float64], NDArray[np.float64]], + Tuple[NDArray[np.float64], NDArray[np.float64]], ]: A, b = linear_model o_d, i_d = tuple(A.shape) @@ -199,11 +199,11 @@ def add_noise_to_linear_model( def analytical_linear_influences( - linear_model: Tuple[NDArray[np.float_], NDArray[np.float_]], - x: NDArray[np.float_], - y: NDArray[np.float_], - x_test: NDArray[np.float_], - y_test: NDArray[np.float_], + linear_model: Tuple[NDArray[np.float64], NDArray[np.float64]], + x: NDArray[np.float64], + y: NDArray[np.float64], + x_test: NDArray[np.float64], + y_test: NDArray[np.float64], mode: InfluenceMode = InfluenceMode.Up, hessian_regularization: float = 0, ): diff --git a/tests/influence/test_influence_calculator.py b/tests/influence/test_influence_calculator.py index dea5551e6..bfd976e2a 100644 --- a/tests/influence/test_influence_calculator.py +++ b/tests/influence/test_influence_calculator.py @@ -48,7 +48,7 @@ lambda model, loss, train_dataLoader, hessian_reg: ArnoldiInfluence( model, loss, - hessian_regularization=hessian_reg, + regularization=hessian_reg, ).fit(train_dataLoader), ], ids=["cg", "direct", "arnoldi"], @@ -71,7 +71,7 @@ def influence_model(model_and_data, test_case, influence_factory): model, loss, hessian_reg, - use_block_cg=True, + solve_simultaneously=True, ).fit(train_dataLoader), lambda model, loss, train_dataLoader, hessian_reg: DirectInfluence( model, loss, hessian_reg @@ -79,7 +79,7 @@ def influence_model(model_and_data, test_case, influence_factory): lambda model, loss, train_dataLoader, hessian_reg: ArnoldiInfluence( model, loss, - hessian_regularization=hessian_reg, + regularization=hessian_reg, ).fit(train_dataLoader), lambda model, loss, train_dataLoader, hessian_reg: NystroemSketchInfluence( model, @@ -170,7 +170,7 @@ def test_dask_influence_nn( inf_model = ArnoldiInfluence( model, test_case.loss, - hessian_regularization=test_case.hessian_reg, + regularization=test_case.hessian_reg, ).fit(train_dataloader) converter = TorchNumpyConverter() @@ -274,7 +274,7 @@ def test_thread_safety_violation_error( inf_model = ArnoldiInfluence( model, test_case.loss, - hessian_regularization=test_case.hessian_reg, + regularization=test_case.hessian_reg, ) with pytest.raises(ThreadSafetyViolationError): DaskInfluenceCalculator( @@ -294,7 +294,7 @@ def test_sequential_calculator(model_and_data, test_case, mocker): inf_model = ArnoldiInfluence( model, test_case.loss, - hessian_regularization=test_case.hessian_reg, + regularization=test_case.hessian_reg, ).fit(train_dataloader) seq_calculator = SequentialInfluenceCalculator(inf_model) diff --git a/tests/influence/torch/test_influence_model.py b/tests/influence/torch/test_influence_model.py index 1082bcc9b..929fc286a 100644 --- a/tests/influence/torch/test_influence_model.py +++ b/tests/influence/torch/test_influence_model.py @@ -18,10 +18,10 @@ LissaInfluence, NystroemSketchInfluence, ) -from pydvl.influence.torch.pre_conditioner import ( - JacobiPreConditioner, - NystroemPreConditioner, - PreConditioner, +from pydvl.influence.torch.preconditioner import ( + JacobiPreconditioner, + NystroemPreconditioner, + Preconditioner, ) from pydvl.influence.torch.util import BlockMode, SecondOrderMode from pydvl.influence.types import UnsupportedInfluenceModeException @@ -370,7 +370,7 @@ def direct_influences_from_factors( [ [ lambda model, loss, train_dataLoader, hessian_reg: CgInfluence( - model, loss, hessian_regularization=hessian_reg + model, loss, regularization=hessian_reg ).fit(train_dataLoader), 1e-1, ], @@ -396,9 +396,9 @@ def direct_influences_from_factors( lambda model, loss, train_dataLoader, hessian_reg: CgInfluence( model, loss, - hessian_regularization=hessian_reg, - pre_conditioner=NystroemPreConditioner(10), - use_block_cg=True, + regularization=hessian_reg, + preconditioner=NystroemPreconditioner(10), + solve_simultaneously=True, ).fit(train_dataLoader), 1e-4, ], @@ -562,9 +562,8 @@ def test_influences_lissa( lambda model, loss, hessian_reg, rank: ArnoldiInfluence( model, loss, - hessian_regularization=hessian_reg, - rank_estimate=rank, - precompute_grad=True, + regularization=hessian_reg, + rank=rank, ), lambda model, loss, hessian_reg, rank: NystroemSketchInfluence( model, loss, regularization=hessian_reg, rank=rank @@ -743,10 +742,10 @@ def test_influences_ekfac( @pytest.mark.torch @pytest.mark.parametrize("use_block_cg", [True, False]) @pytest.mark.parametrize( - "pre_conditioner", + "preconditioner", [ - JacobiPreConditioner(), - NystroemPreConditioner(rank=5), + JacobiPreconditioner(), + NystroemPreconditioner(rank=5), None, ], ) @@ -763,7 +762,7 @@ def test_influences_cg( direct_influences, direct_factors, use_block_cg: bool, - pre_conditioner: PreConditioner, + preconditioner: Preconditioner, device: torch.device, ): model, loss, x_train, y_train, x_test, y_test = model_and_data @@ -776,8 +775,8 @@ def test_influences_cg( loss, test_case.hessian_reg, maxiter=5, - pre_conditioner=pre_conditioner, - use_block_cg=use_block_cg, + preconditioner=preconditioner, + solve_simultaneously=use_block_cg, ) influence_model = influence_model.fit(train_dataloader) @@ -844,6 +843,25 @@ def test_influences_cg( partial( NystroemSketchInfluence, rank=10, second_order_mode=SecondOrderMode.GAUSS_NEWTON ), + partial(ArnoldiInfluence, rank=10, use_woodbury=True), + partial( + ArnoldiInfluence, + rank=10, + second_order_mode=SecondOrderMode.GAUSS_NEWTON, + use_woodbury=True, + ), + partial( + CgInfluence, + maxiter=10, + preconditioner=JacobiPreconditioner(), + solve_simultaneously=True, + ), + partial( + CgInfluence, + maxiter=10, + preconditioner=NystroemPreconditioner(rank=5), + solve_simultaneously=True, + ), ] @@ -897,7 +915,7 @@ def test_composable_influence( x_test, y_test, x_train, y_train, mode=test_case.mode ) - threshold = 1 - 1e-3 + threshold = 1 - 1e-4 check_correlation( direct_influences.reshape(-1), infl_values.reshape(-1), corr_val=threshold ) diff --git a/tests/influence/torch/test_pre_conditioner.py b/tests/influence/torch/test_pre_conditioner.py index 8aa05b863..5e658ab9e 100644 --- a/tests/influence/torch/test_pre_conditioner.py +++ b/tests/influence/torch/test_pre_conditioner.py @@ -1,9 +1,10 @@ import pytest import torch -from pydvl.influence.torch.pre_conditioner import ( - JacobiPreConditioner, - NystroemPreConditioner, +from pydvl.influence.torch.operator import MatrixOperator +from pydvl.influence.torch.preconditioner import ( + JacobiPreconditioner, + NystroemPreconditioner, ) @@ -35,9 +36,10 @@ def low_rank_mat(): return approx_low_rank_matrix(size, rank) +@pytest.mark.torch @pytest.mark.parametrize("num_samples_estimator", [1, 3, 5]) def test_jacobi_preconditioner_condition_number(high_cond_mat, num_samples_estimator): - preconditioner = JacobiPreConditioner(num_samples_estimator=num_samples_estimator) + preconditioner = JacobiPreconditioner(num_samples_estimator=num_samples_estimator) size = high_cond_mat.shape[0] regularization = 0.1 @@ -45,9 +47,7 @@ def test_jacobi_preconditioner_condition_number(high_cond_mat, num_samples_estim A = high_cond_mat original_cond_number = torch.linalg.cond(A + regularization * torch.eye(size)) - preconditioner.fit( - lambda x: A @ x, size, high_cond_mat.dtype, high_cond_mat.device, regularization - ) + preconditioner.fit(MatrixOperator(A), regularization) assert preconditioner.is_fitted preconditioned_A = preconditioner.solve(A + regularization * torch.eye(size)) @@ -55,12 +55,13 @@ def test_jacobi_preconditioner_condition_number(high_cond_mat, num_samples_estim # Assert that the condition number has decreased assert preconditioned_cond_number < original_cond_number * 10 ** ( - -0.5 * (num_samples_estimator) + -0.5 * num_samples_estimator ) +@pytest.mark.torch def test_nystroem_preconditioner_condition_number(low_rank_mat): - preconditioner = NystroemPreConditioner(60) + preconditioner = NystroemPreconditioner(60) size = low_rank_mat.shape[0] regularization = 1e-2 @@ -70,10 +71,7 @@ def test_nystroem_preconditioner_condition_number(low_rank_mat): ) preconditioner.fit( - lambda x: low_rank_mat @ x, - low_rank_mat.shape[0], - low_rank_mat.dtype, - low_rank_mat.device, + MatrixOperator(low_rank_mat), regularization, ) assert preconditioner.is_fitted diff --git a/tests/influence/torch/test_util.py b/tests/influence/torch/test_util.py index a1b782a8c..e0610dd79 100644 --- a/tests/influence/torch/test_util.py +++ b/tests/influence/torch/test_util.py @@ -4,6 +4,8 @@ import numpy as np import pytest +from pydvl.influence.torch.operator import MatrixOperator + torch = pytest.importorskip("torch") import torch.nn from numpy.typing import NDArray @@ -14,7 +16,7 @@ from pydvl.influence.torch.functional import ( create_batch_hvp_function, create_hvp_function, - lanzcos_low_rank_hessian_approx, + operator_spectral_approximation, ) from pydvl.influence.torch.util import ( BlockMode, @@ -160,17 +162,15 @@ def test_get_hvp_function(model_data, tol: float, use_avg: bool, batch_size: int [astuple(tp) for tp in test_parameters], indirect=["model_data"], ) -def test_lanzcos_low_rank_hessian_approx( +def test_operator_spectral_approximation( model_data, batch_size: int, rank_estimate, regularization ): _, _, _, vec, H_analytical = model_data - reg_H_analytical = H_analytical + regularization * torch.eye(H_analytical.shape[0]) - low_rank_approx = lanzcos_low_rank_hessian_approx( - lambda z: reg_H_analytical @ z, - reg_H_analytical.shape, - rank_estimate=rank_estimate, - ) + + op = MatrixOperator(reg_H_analytical) + + low_rank_approx = operator_spectral_approximation(op, rank=rank_estimate) approx_result = low_rank_approx.projections @ ( torch.diag_embed(low_rank_approx.eigen_vals) @ (low_rank_approx.projections.t() @ vec.t()) @@ -179,15 +179,15 @@ def test_lanzcos_low_rank_hessian_approx( @pytest.mark.torch -def test_lanzcos_low_rank_hessian_approx_exception(): +def test_operator_spectral_approximation_exception(): """ - In case cuda is not available, and cupy is not installed, the call should raise an import exception + In case cuda is not available, and cupy is not installed, the call should raise an + import exception """ + op = MatrixOperator(torch.randn(3, 3)) if not torch.cuda.is_available(): with pytest.raises(ImportError): - lanzcos_low_rank_hessian_approx( - lambda x: x, (3, 3), eigen_computation_on_gpu=True - ) + operator_spectral_approximation(op, rank=2, eigen_computation_on_gpu=True) @pytest.mark.parametrize( diff --git a/tests/test_results.py b/tests/test_results.py index 0b42fb48d..3f02fa849 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -31,19 +31,17 @@ def dummy_values(values, names): ) def test_sorting(values, names, ranks_asc, dummy_values): dummy_values.sort(key="value") - assert np.alltrue([it.value for it in dummy_values] == sorted(values)) - assert np.alltrue(dummy_values.indices == ranks_asc) - assert np.alltrue( - [it.value for it in reversed(dummy_values)] == sorted(values, reverse=True) - ) + assert [it.value for it in dummy_values] == sorted(values) + assert dummy_values.indices.tolist() == ranks_asc + assert [it.value for it in reversed(dummy_values)] == sorted(values, reverse=True) dummy_values.sort(reverse=True) - assert np.alltrue([it.value for it in dummy_values] == sorted(values, reverse=True)) - assert np.alltrue(dummy_values.indices == list(reversed(ranks_asc))) + assert [it.value for it in dummy_values] == sorted(values, reverse=True) + assert dummy_values.indices.tolist() == list(reversed(ranks_asc)) dummy_values.sort(key="index") - assert np.alltrue(dummy_values.indices == list(range(len(values)))) - assert np.alltrue([it.value for it in dummy_values] == values) + assert dummy_values.indices.tolist() == list(range(len(values))) + assert [it.value for it in dummy_values] == values @pytest.mark.parametrize( @@ -55,16 +53,16 @@ def test_dataframe_sorting(values, names, ranks_asc, dummy_values): import pandas df = dummy_values.to_dataframe(use_names=False) - assert np.alltrue(df.index.values == ranks_asc) + assert all(df.index.values == ranks_asc) df = dummy_values.to_dataframe(use_names=True) - assert np.alltrue(df.index.values == sorted_names) - assert np.alltrue(df["dummy_valuator"].values == sorted(values)) + assert all(df.index.values == sorted_names) + assert all(df["dummy_valuator"].values == sorted(values)) dummy_values.sort(reverse=True) df = dummy_values.to_dataframe(use_names=True) - assert np.alltrue(df.index.values == list(reversed(sorted_names))) - assert np.alltrue(df["dummy_valuator"].values == sorted(values, reverse=True)) + assert all(df.index.values == list(reversed(sorted_names))) + assert all(df["dummy_valuator"].values == sorted(values, reverse=True)) except ImportError: pass @@ -87,15 +85,15 @@ def test_todataframe(ranks_asc, dummy_values): df = dummy_values.to_dataframe() assert "dummy_valuator" in df.columns assert "dummy_valuator_stderr" in df.columns - assert np.alltrue(df.index.values == ranks_asc) + assert all(df.index.values == ranks_asc) df = dummy_values.to_dataframe(column="val") assert "val" in df.columns assert "val_stderr" in df.columns - assert np.alltrue(df.index.values == ranks_asc) + assert all(df.index.values == ranks_asc) df = dummy_values.to_dataframe(use_names=True) - assert np.alltrue(df.index.values == [it.name for it in dummy_values]) + assert all(df.index.values == [it.name for it in dummy_values]) @pytest.mark.parametrize( @@ -385,7 +383,7 @@ def test_adding_different_indices( [ ([0, 1, 2], np.int32, ["a", "b", "c"], " float: config=cached_func_config, ) - def reduce_func(chunks: NDArray[np.float_]) -> float: + def reduce_func(chunks: NDArray[np.float64]) -> float: return np.sum(chunks).item() map_reduce_job = MapReduceJob( diff --git a/tests/utils/test_numeric.py b/tests/utils/test_numeric.py index 225e5e0dd..1e0246542 100644 --- a/tests/utils/test_numeric.py +++ b/tests/utils/test_numeric.py @@ -51,7 +51,7 @@ def test_random_powerset(n, max_subsets): counts are lower. """ s = np.arange(n) - item_counts = np.zeros_like(s, dtype=np.float_) + item_counts = np.zeros_like(s, dtype=np.float64) size_counts = np.zeros(n + 1) for subset in random_powerset(s, n_samples=max_subsets): size_counts[len(subset)] += 1 diff --git a/tests/value/shapley/test_classwise.py b/tests/value/shapley/test_classwise.py index 85f4b9f30..6876394b2 100644 --- a/tests/value/shapley/test_classwise.py +++ b/tests/value/shapley/test_classwise.py @@ -418,7 +418,7 @@ def dataset_manual_derivation() -> Dataset: @pytest.fixture(scope="function") def dataset_left_right_margins( n_element: int, left_margin: float, right_margin: float -) -> Tuple[NDArray[np.float_], NDArray[np.int_], Dict[str, float]]: +) -> Tuple[NDArray[np.float64], NDArray[np.int_], Dict[str, float]]: """ The label set is represented as 0000011100011111, with adjustable left and right margins. The left margin denotes the percentage of zeros at the beginning, while the