






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)




class 							snake_case_       (    nn.Module      ):

						"""simple docstring"""



						def __init__(self:					Optional[int]   )    ->     Optional[int]:




											'''simple docstring'''



											super().__init__()
											__a					:						List[Any]      =					nn.Linear(3 ,					4   )
											__a					:						Any      =					nn.BatchNormad(4   )
											__a					:						int      =					nn.Linear(4 ,					5   )



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					Any   )    ->     int:




											'''simple docstring'''



											return self.lineara(self.batchnorm(self.lineara(__UpperCAmelCase   )   )   )




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Dict:




											'''simple docstring'''



											__a					:						Optional[int]      =					ModelForTest()
											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(__UpperCAmelCase ,					model.state_dict()   )
																__a					:						Dict      =					os.path.join(__UpperCAmelCase ,					"index.json"   )
																self.assertTrue(os.path.isfile(__UpperCAmelCase   )   )
																# TODO: add tests on what is inside the index

																for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
																					__a					:						List[Any]      =					os.path.join(__UpperCAmelCase ,					f'{key}.dat'   )
																					self.assertTrue(os.path.isfile(__UpperCAmelCase   )   )
																					# TODO: add tests on the fact weights are properly loaded



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     int:




											'''simple docstring'''



											__a					:						List[Any]      =					[torch.floataa, torch.floataa, torch.bfloataa]

											for dtype in dtypes:
																__a					:						Optional[Any]      =					torch.randn(2 ,					3 ,					dtype=__UpperCAmelCase   )
																with TemporaryDirectory() as tmp_dir:
																					__a					:						Any      =					offload_weight(__UpperCAmelCase ,					"weight" ,					__UpperCAmelCase ,					{}   )
																					__a					:						Dict      =					os.path.join(__UpperCAmelCase ,					"weight.dat"   )
																					self.assertTrue(os.path.isfile(__UpperCAmelCase   )   )
																					self.assertDictEqual(__UpperCAmelCase ,					{"weight": {"shape": [2, 3], "dtype": str(__UpperCAmelCase   ).split("."   )[1]}}   )

																					__a					:						Union[str, Any]      =					load_offloaded_weight(__UpperCAmelCase ,					index["weight"]   )
																					self.assertTrue(torch.equal(__UpperCAmelCase ,					__UpperCAmelCase   )   )



						def        UpperCAmelCase__					(self:					Any   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Optional[int]      =					ModelForTest()
											__a					:						List[Any]      =					model.state_dict()
											__a					:						int      =					{k: v for k, v in state_dict.items() if "linear2" not in k}
											__a					:						List[Any]      =					{k: v for k, v in state_dict.items() if "linear2" in k}

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(__UpperCAmelCase ,					__UpperCAmelCase   )
																__a					:						Optional[Any]      =					OffloadedWeightsLoader(state_dict=__UpperCAmelCase ,					save_folder=__UpperCAmelCase   )

																# Every key is there with the right value
																self.assertEqual(sorted(__UpperCAmelCase   ) ,					sorted(state_dict.keys()   )   )
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(__UpperCAmelCase ,					weight_map[key]   )   )

											__a					:						Union[str, Any]      =					{k: v for k, v in state_dict.items() if "weight" in k}
											__a					:						Optional[int]      =					{k: v for k, v in state_dict.items() if "weight" not in k}

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(__UpperCAmelCase ,					__UpperCAmelCase   )
																__a					:						Union[str, Any]      =					OffloadedWeightsLoader(state_dict=__UpperCAmelCase ,					save_folder=__UpperCAmelCase   )

																# Every key is there with the right value
																self.assertEqual(sorted(__UpperCAmelCase   ) ,					sorted(state_dict.keys()   )   )
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(__UpperCAmelCase ,					weight_map[key]   )   )

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(__UpperCAmelCase ,					__UpperCAmelCase   )
																# Duplicates are removed
																__a					:						Any      =					OffloadedWeightsLoader(state_dict=__UpperCAmelCase ,					save_folder=__UpperCAmelCase   )

																# Every key is there with the right value
																self.assertEqual(sorted(__UpperCAmelCase   ) ,					sorted(state_dict.keys()   )   )
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(__UpperCAmelCase ,					weight_map[key]   )   )



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[int]      =					{"a.1": 0, "a.10": 1, "a.2": 2}
											__a					:						Any      =					extract_submodules_state_dict(__UpperCAmelCase ,					["a.1", "a.2"]   )
											self.assertDictEqual(__UpperCAmelCase ,					{"a.1": 0, "a.2": 2}   )

											__a					:						Tuple      =					{"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
											__a					:						Union[str, Any]      =					extract_submodules_state_dict(__UpperCAmelCase ,					["a.1", "a.2"]   )
											self.assertDictEqual(__UpperCAmelCase ,					{"a.1.a": 0, "a.2.a": 2}   )













def     a_   (__A				,       __A				,       __A				,       __A      )				->						int:



					"""simple docstring"""
					__a      ,			__a					:						Any      =					len(__A      ), len(grid[0]      )
					if (
					    min(__A				,       __A      ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col)      )

					__a					:						Dict      =					0
					count += depth_first_search(__A				,       row + 1				,       __A				,       __A      )
					count += depth_first_search(__A				,       row - 1				,       __A				,       __A      )
					count += depth_first_search(__A				,       __A				,       col + 1				,       __A      )
					count += depth_first_search(__A				,       __A				,       col - 1				,       __A      )

					visit.remove((row, col)      )
					return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





