
import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

lowercase__							:							Optional[int]													=     logging.getLogger(__name__)






@require_tensorboard
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_config'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			for child in Path(F"{dirpath}/{project_name}"     ).glob('''*/**'''     ):
																									__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		child.iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''tensor''': torch.tensor(1     )}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
																			# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																			with open(SCREAMING_SNAKE_CASE_       ,		'''rb'''     ) as f:
																									__UpperCamelCase       					=     f.read()
																			__UpperCamelCase       					=     False
																			while data:
																									__UpperCamelCase       					=     struct.unpack('''Q'''       ,		data[:8]     )

																									__UpperCamelCase       					=     data[12 : 12 + int(header[0]     )]  # 8+4
																									__UpperCamelCase       					=     data[12 + int(header[0]     ) + 4 :]
																									__UpperCamelCase       					=     event_pba.Event()

																									event.ParseFromString(SCREAMING_SNAKE_CASE_     )
																									if event.HasField('''summary'''     ):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											__UpperCamelCase       					=     True
																			self.assertTrue(SCREAMING_SNAKE_CASE_       ,		'''Converted tensor was not found in the log file!'''     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_       ,		'''Logging with `tensorboard` requires a `logging_dir`'''     ):
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     ProjectConfiguration(total_limit=30     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_       ,		project_config=SCREAMING_SNAKE_CASE_     )






@require_wandb
@mock.patch.dict(os.environ				,						{'WANDB_MODE': 'offline'}      )
class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().setUp()
													# wandb let's us override where logs are stored to via the WANDB_DIR env var
													self.add_mocks(mock.patch.dict(os.environ       ,		{'''WANDB_DIR''': self.tmpdir}     )     )



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = True     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     rF"{section} ([\S\s]*?)\n\n"
													if record:
																			__UpperCamelCase       					=     rF"Record: {pattern}"
													__UpperCamelCase       					=     re.findall(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )[0]
													# A config
													if section == "config" or section == "history":
																			__UpperCamelCase       					=     re.findall(r'''"([a-zA-Z0-9_.,]+)'''       ,		SCREAMING_SNAKE_CASE_     )
																			return {key: val for key, val in zip(cleaned_record[0::2]       ,		cleaned_record[1::2]     )}
													# Everything else
													else:
																			return dict(re.findall(r'''(\w+): "([^\s]+)"'''       ,		SCREAMING_SNAKE_CASE_     )     )



							@skip
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_config'''
													__UpperCamelCase       					=     Accelerator(log_with='''wandb'''     )
													__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
													__UpperCamelCase       					=     {'''wandb''': {'''tags''': ['''my_tag''']}}
													accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
													accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
													accelerator.end_training()
													# The latest offline log is stored at wandb/latest-run/*.wandb
													for child in Path(F"{self.tmpdir}/wandb/latest-run"     ).glob('''*'''     ):
																			if child.is_file() and child.suffix == ".wandb":
																									__UpperCamelCase       					=     subprocess.check_output(
																									    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(SCREAMING_SNAKE_CASE_     )]       ,		env=os.environ.copy()     ).decode('''utf8'''       ,		'''ignore'''     )
																									break

        # Check HPS through careful parsing and cleaning
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''config'''     )
													self.assertEqual(logged_items['''num_iterations''']       ,		'''12'''     )
													self.assertEqual(logged_items['''learning_rate''']       ,		'''0.01'''     )
													self.assertEqual(logged_items['''some_boolean''']       ,		'''false'''     )
													self.assertEqual(logged_items['''some_string''']       ,		'''some_value'''     )
													self.assertEqual(logged_items['''some_string''']       ,		'''some_value'''     )

													# Run tags
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''run'''       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(logged_items['''tags''']       ,		'''my_tag'''     )

													# Actual logging
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''history'''     )
													self.assertEqual(logged_items['''total_loss''']       ,		'''0.1'''     )
													self.assertEqual(logged_items['''iteration''']       ,		'''1'''     )
													self.assertEqual(logged_items['''my_text''']       ,		'''some_value'''     )
													self.assertEqual(logged_items['''_step''']       ,		'''0'''     )





def 		A_	(							self							:				str	, snake_case							:				str	, snake_case							:				str	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     run_name
						__UpperCamelCase       					=     OfflineExperiment(project_name=snake_case	, offline_directory=snake_case	)
						logger.info(f"Initialized offline CometML project {self.run_name}"	)
						logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''	)






@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE_				,						'__init__'				,						SCREAMING_SNAKE_CASE_      )
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[str]:





													'''simple docstring'''






													for log in log_list:
																			__UpperCamelCase       					=     json.loads(SCREAMING_SNAKE_CASE_     )['''payload''']
																			if is_param and "param" in j.keys():
																									if j["param"]["paramName"] == key:
																															return j["param"]["paramValue"]
																			if "log_other" in j.keys():
																									if j["log_other"]["key"] == key:
																															return j["log_other"]["val"]
																			if "metric" in j.keys():
																									if j["metric"]["metricName"] == key:
																															return j["metric"]["metricValue"]



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





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     log.split('''\n'''     )[:-1]
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''num_iterations'''       ,		SCREAMING_SNAKE_CASE_     )       ,		12     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''learning_rate'''       ,		SCREAMING_SNAKE_CASE_     )       ,		0.0_1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_boolean'''       ,		SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_string'''       ,		SCREAMING_SNAKE_CASE_     )       ,		'''some_value'''     )



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





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     log.split('''\n'''     )[:-1]
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''curr_step'''       ,		SCREAMING_SNAKE_CASE_     )       ,		0     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''total_loss'''     )       ,		0.1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''iteration'''     )       ,		1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''my_text'''     )       ,		'''some_value'''     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					[
							    'total_loss',
							    'iteration',
							    'my_text',
							    'learning_rate',
							    'num_iterations',
							    'some_boolean',
							    'some_string',
							]

							_snake_case       						=					'my_custom_tracker'
							_snake_case       						=					False



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     open(F"{dir}/log.csv"       ,		'''w+'''     )
													__UpperCamelCase       					=     csv.DictWriter(self.f       ,		fieldnames=self._col_names     )
													self.writer.writeheader()



							@property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return self.writer



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call init'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call log'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													self.f.close()






class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers('''Some name'''       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''''',
																									    '''iteration''': '''''',
																									    '''my_text''': '''''',
																									    '''learning_rate''': '''0.01''',
																									    '''num_iterations''': '''12''',
																									    '''some_boolean''': '''False''',
																									    '''some_string''': '''some_value''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers('''Some name'''     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''0.1''',
																									    '''iteration''': '''1''',
																									    '''my_text''': '''some_value''',
																									    '''learning_rate''': '''''',
																									    '''num_iterations''': '''''',
																									    '''some_boolean''': '''''',
																									    '''some_string''': '''''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



							def __iter__(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result




