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       lowercase__		(     nn.Module):


						def __init__( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().__init__()
												SCREAMING_SNAKE_CASE							:					Tuple           =							nn.Linear(3					,	4		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.BatchNormad(4		)
												SCREAMING_SNAKE_CASE							:					Dict           =							nn.Linear(4					,	5		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__		)		)		)






class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							ModelForTest()
												with TemporaryDirectory() as tmp_dir:
																		offload_state_dict(UpperCamelCase__					,	model.state_dict()		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							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"]:
																								SCREAMING_SNAKE_CASE							:					Optional[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  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							[torch.floataa, torch.floataa, torch.bfloataa]

												for dtype in dtypes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							torch.randn(2					,	3					,	dtype=UpperCamelCase__		)
																		with TemporaryDirectory() as tmp_dir:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							offload_weight(UpperCamelCase__					,	'''weight'''					,	UpperCamelCase__					,	{}		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(UpperCamelCase__					,	'''weight.dat'''		)
																								self.assertTrue(os.path.isfile(UpperCamelCase__		)		)
																								self.assertDictEqual(UpperCamelCase__					,	{'''weight''': {'''shape''': [2, 3], '''dtype''': str(UpperCamelCase__		).split('''.'''		)[1]}}		)

																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_offloaded_weight(UpperCamelCase__					,	index['''weight''']		)
																								self.assertTrue(torch.equal(UpperCamelCase__					,	UpperCamelCase__		)		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							ModelForTest()
												SCREAMING_SNAKE_CASE							:					Any           =							model.state_dict()
												SCREAMING_SNAKE_CASE							:					Tuple           =							{k: v for k, v in state_dict.items() if '''linear2''' not in k}
												SCREAMING_SNAKE_CASE							:					Tuple           =							{k: v for k, v in state_dict.items() if '''linear2''' in k}

												with TemporaryDirectory() as tmp_dir:
																		offload_state_dict(UpperCamelCase__					,	UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					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]		)		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							{k: v for k, v in state_dict.items() if '''weight''' in k}
												SCREAMING_SNAKE_CASE							:					List[Any]           =							{k: v for k, v in state_dict.items() if '''weight''' not in k}

												with TemporaryDirectory() as tmp_dir:
																		offload_state_dict(UpperCamelCase__					,	UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[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
																		SCREAMING_SNAKE_CASE							:					int           =							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  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							{'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
												SCREAMING_SNAKE_CASE							:					int           =							extract_submodules_state_dict(UpperCamelCase__					,	['''a.1''', '''a.2''']		)
												self.assertDictEqual(UpperCamelCase__					,	{'''a.1''': 0, '''a.2''': 2}		)

												SCREAMING_SNAKE_CASE							:					Dict           =							{'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							extract_submodules_state_dict(UpperCamelCase__					,	['''a.1''', '''a.2''']		)
												self.assertDictEqual(UpperCamelCase__					,	{'''a.1.a''': 0, '''a.2.a''': 2}		)






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





