


'''simple docstring'''
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      ):

		"""simple docstring"""

		def __init__(	self							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__     :List[str]							      =				nn.Linear(3					,     4							)
							UpperCamelCase__     :str							      =				nn.BatchNormad(4							)
							UpperCamelCase__     :Tuple							      =				nn.Linear(4					,     5							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_							)							)							)



class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				ModelForTest()
							with TemporaryDirectory() as tmp_dir:
												offload_state_dict(UpperCamelCase_					,     model.state_dict()							)
												UpperCamelCase__     :Optional[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"]:
																	UpperCamelCase__     :Union[str, 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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				[torch.floataa, torch.floataa, torch.bfloataa]

							for dtype in dtypes:
												UpperCamelCase__     :str							      =				torch.randn(2					,     3					,     dtype=UpperCamelCase_							)
												with TemporaryDirectory() as tmp_dir:
																	UpperCamelCase__     :str							      =				offload_weight(UpperCamelCase_					,     '''weight'''					,     UpperCamelCase_					,     {}							)
																	UpperCamelCase__     :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]}}							)

																	UpperCamelCase__     :Tuple							      =				load_offloaded_weight(UpperCamelCase_					,     index['''weight''']							)
																	self.assertTrue(torch.equal(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				ModelForTest()
							UpperCamelCase__     :List[str]							      =				model.state_dict()
							UpperCamelCase__     :Dict							      =				{k: v for k, v in state_dict.items() if '''linear2''' not in k}
							UpperCamelCase__     :Union[str, Any]							      =				{k: v for k, v in state_dict.items() if '''linear2''' in k}

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

							UpperCamelCase__     :Dict							      =				{k: v for k, v in state_dict.items() if '''weight''' in k}
							UpperCamelCase__     :Dict							      =				{k: v for k, v in state_dict.items() if '''weight''' not in k}

							with TemporaryDirectory() as tmp_dir:
												offload_state_dict(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :Dict							      =				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
												UpperCamelCase__     :Optional[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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				{'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
							UpperCamelCase__     :Tuple							      =				extract_submodules_state_dict(UpperCamelCase_					,     ['''a.1''', '''a.2''']							)
							self.assertDictEqual(UpperCamelCase_					,     {'''a.1''': 0, '''a.2''': 2}							)

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



'''simple docstring'''
import json
import sys


def     a   (    __a				,      __a     )						->			str:



					'''simple docstring'''






					with open(__a				,      encoding='''utf-8'''     ) as f:
										UpperCamelCase__     :List[str]							      =				json.load(__a     )

					UpperCamelCase__     :int							      =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

					for benchmark_name in sorted(__a     ):
										UpperCamelCase__     :Optional[Any]							      =				results[benchmark_name]

										UpperCamelCase__     :int							      =				benchmark_name.split('''/'''     )[-1]
										output_md.append(f'''### Benchmark: {benchmark_file_name}'''     )

										UpperCamelCase__     :List[str]							      =				'''| metric |'''
										UpperCamelCase__     :str							      =				'''|--------|'''
										UpperCamelCase__     :Union[str, Any]							      =				'''| new / old (diff) |'''
										for metric_name in sorted(__a     ):
															UpperCamelCase__     :List[Any]							      =				benchmark_res[metric_name]
															UpperCamelCase__     :Optional[int]							      =				metric_vals['''new''']
															UpperCamelCase__     :Any							      =				metric_vals.get('''old'''				,      __a     )
															UpperCamelCase__     :Optional[int]							      =				metric_vals.get('''diff'''				,      __a     )

															UpperCamelCase__     :List[str]							      =				f''' {new_val:f}''' if isinstance(__a				,      (int, float)     ) else '''None'''

															if old_val is not None:
																				val_str += f''' / {old_val:f}''' if isinstance(__a				,      (int, float)     ) else "None"
															if dif_val is not None:
																				val_str += f''' ({dif_val:f})''' if isinstance(__a				,      (int, float)     ) else "None"

															title += " " + metric_name + " |"
															lines += "---|"
															value += val_str + " |"

										output_md += [title, lines, value, " "]

					output_md.append('''</details>'''     )

					with open(__a				,      '''w'''				,      encoding='''utf-8'''     ) as f:
										f.writelines('''\n'''.join(__a     )     )


if __name__ == "__main__":
		__snake_case							   =  sys.argv[1]
		__snake_case							   =  sys.argv[2]

		format_json_to_md(input_json_file, output_md_file)