



import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__A       =						datasets.utils.logging.get_logger(__name__)

__A       =						['''names''', '''prefix''']
__A       =						['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A       =						['''encoding_errors''', '''on_bad_lines''']
__A       =						['''date_format''']






@dataclass
class 		lowercase						(			datasets.BuilderConfig):






	"""simple docstring"""


	a__				: str		       =							","
	a__				: Optional[str]		       =							None
	a__				: Optional[Union[int, List[int], str]]		       =							"infer"
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[Union[int, str, List[int], List[str]]]		       =							None
	a__				: Optional[Union[List[int], List[str]]]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: Optional[Literal["c", "python", "pyarrow"]]		       =							None
	a__				: Dict[Union[int, str], Callable[[Any], Any]]		       =							None
	a__				: Optional[list]		       =							None
	a__				: Optional[list]		       =							None
	a__				: bool		       =							False
	a__				: Optional[Union[int, List[int]]]		       =							None
	a__				: Optional[int]		       =							None
	a__				: Optional[Union[str, List[str]]]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: bool		       =							True
	a__				: Optional[str]		       =							None
	a__				: str		       =							"."
	a__				: Optional[str]		       =							None
	a__				: str		       =							'"'
	a__				: int		       =							0
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: int		       =							0
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: Optional[str]		       =							None
	a__				: int		       =							1_0000
	a__				: Optional[datasets.Features]		       =							None
	a__				: Optional[str]		       =							"strict"
	a__				: Literal["error", "warn", "skip"]		       =							"error"
	a__				: Optional[str]		       =							None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							if self.delimiter is not None:
													UpperCAmelCase_=       self.delimiter
							if self.column_names is not None:
													UpperCAmelCase_=       self.column_names




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       {
							    """sep""": self.sep,
							    """header""": self.header,
							    """names""": self.names,
							    """index_col""": self.index_col,
							    """usecols""": self.usecols,
							    """prefix""": self.prefix,
							    """mangle_dupe_cols""": self.mangle_dupe_cols,
							    """engine""": self.engine,
							    """converters""": self.converters,
							    """true_values""": self.true_values,
							    """false_values""": self.false_values,
							    """skipinitialspace""": self.skipinitialspace,
							    """skiprows""": self.skiprows,
							    """nrows""": self.nrows,
							    """na_values""": self.na_values,
							    """keep_default_na""": self.keep_default_na,
							    """na_filter""": self.na_filter,
							    """verbose""": self.verbose,
							    """skip_blank_lines""": self.skip_blank_lines,
							    """thousands""": self.thousands,
							    """decimal""": self.decimal,
							    """lineterminator""": self.lineterminator,
							    """quotechar""": self.quotechar,
							    """quoting""": self.quoting,
							    """escapechar""": self.escapechar,
							    """comment""": self.comment,
							    """encoding""": self.encoding,
							    """dialect""": self.dialect,
							    """error_bad_lines""": self.error_bad_lines,
							    """warn_bad_lines""": self.warn_bad_lines,
							    """skipfooter""": self.skipfooter,
							    """doublequote""": self.doublequote,
							    """memory_map""": self.memory_map,
							    """float_precision""": self.float_precision,
							    """chunksize""": self.chunksize,
							    """encoding_errors""": self.encoding_errors,
							    """on_bad_lines""": self.on_bad_lines,
							    """date_format""": self.date_format,
							}

							# some kwargs must not be passed if they don't have a default value
							# some others are deprecated and we can also not pass them if they are the default value
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,  __UpperCAmelCase					):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

							return pd_read_csv_kwargs






class 		lowercase						(			datasets.ArrowBasedBuilder):






	"""simple docstring"""


	a__				: int		       =							CsvConfig




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							return datasets.DatasetInfo(features=self.config.features					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							if not self.config.data_files:
													raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""					)
							UpperCAmelCase_=       dl_manager.download_and_extract(self.config.data_files					)
							if isinstance(__UpperCAmelCase						,  (str, list, tuple)					):
													UpperCAmelCase_=       data_files
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,  gen_kwargs={"""files""": files}					)]
							UpperCAmelCase_=       []
							for split_name, files in data_files.items():
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													splits.append(datasets.SplitGenerator(name=__UpperCAmelCase						,  gen_kwargs={"""files""": files}					)					)
							return splits




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						pa.Table					)      ->			pa.Table:
							if self.config.features is not None:
													UpperCAmelCase_=       self.config.features.arrow_schema
													if all(not require_storage_cast(__UpperCAmelCase					) for feature in self.config.features.values()					):
																			# cheaper cast
																			UpperCAmelCase_=       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,  schema=__UpperCAmelCase					)
													else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			UpperCAmelCase_=       table_cast(__UpperCAmelCase						,  __UpperCAmelCase					)
							return pa_table




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_=       (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase					) else object
							        for name, dtype, feature in zip(schema.names						,  schema.types						,  self.config.features.values()					)
							    }
							    if schema is not None
							    else None
							)
							for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase					)					):
													UpperCAmelCase_=       pd.read_csv(__UpperCAmelCase						,  iterator=__UpperCAmelCase						,  dtype=__UpperCAmelCase						,  **self.config.pd_read_csv_kwargs					)
													try:
																			for batch_idx, df in enumerate(__UpperCAmelCase					):
																									UpperCAmelCase_=       pa.Table.from_pandas(__UpperCAmelCase					)
																									# Uncomment for debugging (will print the Arrow table size and elements)
																									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																									yield (file_idx, batch_idx), self._cast_table(__UpperCAmelCase					)
													except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase					)}: {e}"""					)
																			raise









import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def 	__a					(   )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       torch.nn.Linear(2      ,4    )
						UpperCAmelCase_=       torch.optim.AdamW(model.parameters()      ,lr=1.0    )
						UpperCAmelCase_=       torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_      ,max_lr=0.01      ,steps_per_epoch=2      ,epochs=1    )
						UpperCAmelCase_=       DataLoader(TensorDataset(torch.tensor([1, 2, 3]    )    )    )
						UpperCAmelCase_=       DataLoader(TensorDataset(torch.tensor([4, 5, 6]    )    )    )

						return model, optimizer, scheduler, train_dl, valid_dl



def 	__a					(   lowerCAmelCase_       : Any    )				->			Union[str, Any]:






						'''simple docstring'''



						return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       torch.nn.Linear(*tuple(model.weight.T.shape    )    ).state_dict()
						model.load_state_dict(lowerCAmelCase_    )






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	@require_cuda
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       Accelerator()
							assert PartialState._shared_state["_cpu"] is False
							assert PartialState._shared_state["device"].type == "cuda"
							with self.assertRaises(__UpperCAmelCase					):
													UpperCAmelCase_=       Accelerator(cpu=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_=       GradientState()
							assert state.num_steps == 1
							UpperCAmelCase_=       4
							assert state.num_steps == 4

							assert state.sync_gradients is True
							UpperCAmelCase_=       False
							assert state.sync_gradients is False
							GradientState._reset_state()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()

							(
							    (
							    UpperCAmelCase_
							),       (
							    UpperCAmelCase_
							),       (
							    UpperCAmelCase_
							),       (
							    UpperCAmelCase_
							),       (
							    UpperCAmelCase_
							),       
							)=       accelerator.prepare(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)

							self.assertTrue(prepared_model in accelerator._models					)
							self.assertTrue(prepared_optimizer in accelerator._optimizers					)
							self.assertTrue(prepared_scheduler in accelerator._schedulers					)
							self.assertTrue(prepared_train_dl in accelerator._dataloaders					)
							self.assertTrue(prepared_valid_dl in accelerator._dataloaders					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Dict:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()
							accelerator.prepare(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
							accelerator.free_memory()

							self.assertTrue(len(accelerator._models					) == 0					)
							self.assertTrue(len(accelerator._optimizers					) == 0					)
							self.assertTrue(len(accelerator._schedulers					) == 0					)
							self.assertTrue(len(accelerator._dataloaders					) == 0					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							PartialState._reset_state()

							# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
							def noop(*__UpperCAmelCase     :						Dict						,  **__UpperCAmelCase     :						Tuple					):
													pass

							with patch("""torch.cuda.set_device"""						,  __UpperCAmelCase					), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""					):
													UpperCAmelCase_=       Accelerator()
													self.assertEqual(str(accelerator.state.device					)						,  """cuda:64"""					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()
							accelerator.prepare(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)

							UpperCAmelCase_=       get_signature(__UpperCAmelCase					)

							with tempfile.TemporaryDirectory() as tmpdirname:
													accelerator.save_state(__UpperCAmelCase					)

													# make sure random weights don't match
													load_random_weights(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) > 1E-3					)

													# make sure loaded weights match
													accelerator.load_state(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) < 1E-3					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()
							accelerator.prepare(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)

							UpperCAmelCase_=       get_signature(__UpperCAmelCase					)

							# saving hook
							def save_config(__UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						Tuple					):
													UpperCAmelCase_=       {"""class_name""": models[0].__class__.__name__}

													with open(os.path.join(__UpperCAmelCase						,  """data.json"""					)						,  """w"""					) as f:
																			json.dump(__UpperCAmelCase						,  __UpperCAmelCase					)

        # loading hook
							def load_config(__UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]					):
													with open(os.path.join(__UpperCAmelCase						,  """data.json"""					)						,  """r"""					) as f:
																			UpperCAmelCase_=       json.load(__UpperCAmelCase					)

													UpperCAmelCase_=       config["""class_name"""]

							UpperCAmelCase_=       accelerator.register_save_state_pre_hook(__UpperCAmelCase					)
							UpperCAmelCase_=       accelerator.register_load_state_pre_hook(__UpperCAmelCase					)

							with tempfile.TemporaryDirectory() as tmpdirname:
													accelerator.save_state(__UpperCAmelCase					)

													# make sure random weights don't match with hooks
													load_random_weights(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) > 1E-3					)

													# random class name to verify correct one is loaded
													UpperCAmelCase_=       """random"""

													# make sure loaded weights match with hooks
													accelerator.load_state(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) < 1E-3					)

													# mode.class_name is loaded from config
													self.assertTrue(model.class_name == model.__class__.__name__					)

							# remove hooks
							save_hook.remove()
							load_hook.remove()

							with tempfile.TemporaryDirectory() as tmpdirname:
													accelerator.save_state(__UpperCAmelCase					)

													# make sure random weights don't match with hooks removed
													load_random_weights(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) > 1E-3					)

													# random class name to verify correct one is loaded
													UpperCAmelCase_=       """random"""

													# make sure loaded weights match with hooks removed
													accelerator.load_state(__UpperCAmelCase					)
													self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase					)					) < 1E-3					)

													# mode.class_name is NOT loaded from config
													self.assertTrue(model.class_name != model.__class__.__name__					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()
							UpperCAmelCase_=       None

							# This should work
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       accelerator.prepare(
							    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
							self.assertTrue(dummy_obj is None					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							UpperCAmelCase_=       Accelerator()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       create_components()
							UpperCAmelCase_=       [1, 2, 3]

							# This should work
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       accelerator.prepare(
							    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Dummy object should have `_is_accelerate_prepared` set to `True`"""						,  )
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Model is missing `_is_accelerator_prepared` or is set to `False`"""						,  )
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""						,  )
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""						,  )
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""						,  )
							self.assertEqual(
							    getattr(__UpperCAmelCase						,  """_is_accelerate_prepared"""						,  __UpperCAmelCase					)						,  __UpperCAmelCase						,  """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""						,  )




	@slow
	@require_bnb
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Optional[Any]:
							from transformers import AutoModelForCausalLM

							UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""						,  load_in_abit=__UpperCAmelCase						,  device_map={"""""": 0}						,  )
							UpperCAmelCase_=       Accelerator()

							# This should work
							UpperCAmelCase_=       accelerator.prepare(__UpperCAmelCase					)




	@slow
	@require_bnb
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							from transformers import AutoModelForCausalLM

							UpperCAmelCase_=       Accelerator()

							with init_empty_weights():
													UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
													    """EleutherAI/gpt-neo-125m"""						,  )
													model.tie_weights()
													UpperCAmelCase_=       infer_auto_device_map(__UpperCAmelCase					)
													UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""						,  device_map=__UpperCAmelCase						,  load_in_abit=__UpperCAmelCase						,  llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase					)

							# This should not work and get value error
							with self.assertRaises(__UpperCAmelCase					):
													UpperCAmelCase_=       accelerator.prepare(__UpperCAmelCase					)




	@slow
	@require_bnb
	@require_multi_gpu
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Tuple:
							from transformers import AutoModelForCausalLM

							UpperCAmelCase_=       {"""distributed_type""": DistributedType.MULTI_GPU}

							with init_empty_weights():
													UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
													    """EleutherAI/gpt-neo-125m"""						,  )
													model.tie_weights()
													UpperCAmelCase_=       infer_auto_device_map(__UpperCAmelCase					)
													UpperCAmelCase_=       1

							UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""						,  load_in_abit=__UpperCAmelCase						,  device_map=__UpperCAmelCase						,  )
							UpperCAmelCase_=       Accelerator()

							# This should not work and get value error
							with self.assertRaises(__UpperCAmelCase					):
													UpperCAmelCase_=       accelerator.prepare(__UpperCAmelCase					)

							PartialState._reset_state()




	@slow
	@require_bnb
	@require_multi_gpu
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							from transformers import AutoModelForCausalLM

							with init_empty_weights():
													UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
													    """EleutherAI/gpt-neo-125m"""						,  )
													UpperCAmelCase_=       infer_auto_device_map(__UpperCAmelCase					)
													UpperCAmelCase_=       1

							UpperCAmelCase_=       AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""						,  load_in_abit=__UpperCAmelCase						,  device_map=__UpperCAmelCase						,  )
							UpperCAmelCase_=       Accelerator()

							# This should work
							UpperCAmelCase_=       accelerator.prepare(__UpperCAmelCase					)




	@require_cuda
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       torch.nn.Linear(10						,  10					)
							UpperCAmelCase_=       torch.optim.SGD(model.parameters()						,  lr=0.01					)
							UpperCAmelCase_=       Accelerator(cpu=__UpperCAmelCase					)
							UpperCAmelCase_=       accelerator.prepare(__UpperCAmelCase					)




