
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_snake_case      :							Any     	=    logging.get_logger(__name__)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Dict		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:float		,    **lowerCAmelCase__							:List[Any]     )  ->Optional[int]:
										lowercase         =      feature_size
										lowercase         =      sampling_rate
										lowercase         =      padding_value

										lowercase         =      kwargs.pop("padding_side"		,    "right"     )
										lowercase         =      kwargs.pop("return_attention_mask"		,    lowerCAmelCase__     )

										super().__init__(**lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]		,    lowerCAmelCase__							:Union[bool, str, PaddingStrategy] = True		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[bool] = None		,    lowerCAmelCase__							:Optional[Union[str, TensorType]] = None		,    )  ->BatchFeature:
										# If we have a list of dicts, let's convert it in a dict of lists
										# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
										if isinstance(lowerCAmelCase__		,    (list, tuple)     ) and isinstance(processed_features[0]		,    (dict, BatchFeature)     ):
														lowercase         =      {
														    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
														}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
														raise ValueError(
														    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
														    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
														    F''' {list(processed_features.keys()     )}'''     )

										lowercase         =      processed_features[self.model_input_names[0]]
										lowercase         =      (
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(lowerCAmelCase__     ) == 0:
														if return_attention_mask:
																		lowercase         =      []
														return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										lowercase         =      required_input[0]
										if isinstance(lowerCAmelCase__		,    (list, tuple)     ):
														# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
														lowercase         =      0
														while len(required_input[index]     ) == 0:
																		index += 1
														if index < len(lowerCAmelCase__     ):
																		lowercase         =      required_input[index][0]

										if return_tensors is None:
														if is_tf_tensor(lowerCAmelCase__     ):
																		lowercase         =      "tf"
														elif is_torch_tensor(lowerCAmelCase__     ):
																		lowercase         =      "pt"
														elif isinstance(lowerCAmelCase__		,    (int, float, list, tuple, np.ndarray)     ):
																		lowercase         =      "np"
														else:
																		raise ValueError(
																		    F'''type of {first_element} unknown: {type(lowerCAmelCase__     )}. '''
																		    "Should be one of a python, numpy, pytorch or tensorflow object."     )

										for key, value in processed_features.items():
														if isinstance(value[0]		,    (int, float)     ):
																		lowercase         =      to_numpy(lowerCAmelCase__     )
														else:
																		lowercase         =      [to_numpy(lowerCAmelCase__     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
										lowercase         =      self._get_padding_strategies(padding=lowerCAmelCase__		,    max_length=lowerCAmelCase__     )

										lowercase         =      processed_features[self.model_input_names[0]]

										lowercase         =      len(lowerCAmelCase__     )
										if not all(len(lowerCAmelCase__     ) == batch_size for v in processed_features.values()     ):
														raise ValueError("Some items in the output dictionary have a different batch size than others."     )

										lowercase         =      []
										for i in range(lowerCAmelCase__     ):
														lowercase         =      {k: v[i] for k, v in processed_features.items()}
														# truncation
														lowercase         =      self._truncate(
														    lowerCAmelCase__		,    max_length=lowerCAmelCase__		,    pad_to_multiple_of=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    )
														truncated_inputs.append(lowerCAmelCase__     )

										if padding_strategy == PaddingStrategy.LONGEST:
														# make sure that `max_length` cannot be longer than the longest truncated length
														lowercase         =      max(len(input_slice[self.model_input_names[0]]     ) for input_slice in truncated_inputs     )
														lowercase         =      PaddingStrategy.MAX_LENGTH

										lowercase         =      {}
										for i in range(lowerCAmelCase__     ):
														# padding
														lowercase         =      self._pad(
														    truncated_inputs[i]		,    max_length=lowerCAmelCase__		,    padding_strategy=lowerCAmelCase__		,    pad_to_multiple_of=lowerCAmelCase__		,    return_attention_mask=lowerCAmelCase__		,    )

														for key, value in outputs.items():
																		if key not in batch_outputs:
																						lowercase         =      []
																		if value.dtype is np.dtype(np.floataa     ):
																						lowercase         =      value.astype(np.floataa     )
																		batch_outputs[key].append(lowerCAmelCase__     )

										return BatchFeature(lowerCAmelCase__		,    tensor_type=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Union[Dict[str, np.ndarray], BatchFeature]		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[bool] = None		,    )  ->dict:
										lowercase         =      processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
														lowercase         =      len(lowerCAmelCase__     )

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														lowercase         =      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										lowercase         =      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase__     ) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
														lowercase         =      np.ones(len(lowerCAmelCase__     )		,    dtype=np.intaa     )

										if needs_to_be_padded:
														lowercase         =      max_length - len(lowerCAmelCase__     )
														if self.padding_side == "right":
																		if return_attention_mask:
																						lowercase         =      np.pad(
																						    processed_features["attention_mask"]		,    (0, difference)     )
																		lowercase         =      ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																		lowercase         =      np.pad(
																		    lowerCAmelCase__		,    lowerCAmelCase__		,    "constant"		,    constant_values=self.padding_value     )
														elif self.padding_side == "left":
																		if return_attention_mask:
																						lowercase         =      np.pad(
																						    processed_features["attention_mask"]		,    (difference, 0)     )
																		lowercase         =      ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																		lowercase         =      np.pad(
																		    lowerCAmelCase__		,    lowerCAmelCase__		,    "constant"		,    constant_values=self.padding_value     )
														else:
																		raise ValueError("Invalid padding strategy:" + str(self.padding_side     )     )

										return processed_features





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Union[Dict[str, np.ndarray], BatchFeature]		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[bool] = None		,    )  ->Union[str, Any]:
										if not truncation:
														return processed_features
										elif truncation and max_length is None:
														raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."     )

										lowercase         =      processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														lowercase         =      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										lowercase         =      len(lowerCAmelCase__     ) > max_length

										if needs_to_be_truncated:
														lowercase         =      processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																		lowercase         =      processed_features["attention_mask"][:max_length]

										return processed_features





						def        SCREAMING_SNAKE_CASE(			self							:int		,    lowerCAmelCase__							:Optional[Any]=False		,    lowerCAmelCase__							:Any=None     )  ->Dict:

										# Get padding strategy
										if padding is not False:
														if padding is True:
																		lowercase         =      PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		lowercase         =      PaddingStrategy(lowerCAmelCase__     )
														elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		lowercase         =      padding
										else:
														lowercase         =      PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
														if padding_strategy == PaddingStrategy.MAX_LENGTH:
																		raise ValueError(
																		    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''     )

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
														raise ValueError(
														    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
														    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."     )

										return padding_strategy


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  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      torch.nn.Linear(2		,    4						)
				lowercase         =      torch.optim.AdamW(model.parameters()		,    lr=1.0						)
				lowercase         =      torch.optim.lr_scheduler.OneCycleLR(__magic_name__		,    max_lr=0.0_1		,    steps_per_epoch=2		,    epochs=1						)
				lowercase         =      DataLoader(TensorDataset(torch.tensor([1, 2, 3]						)						)						)
				lowercase         =      DataLoader(TensorDataset(torch.tensor([4, 5, 6]						)						)						)

				return model, optimizer, scheduler, train_dl, valid_dl

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      torch.nn.Linear(*tuple(model.weight.T.shape						)						).state_dict()
				model.load_state_dict(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@require_cuda
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										lowercase         =      Accelerator()
										assert PartialState._shared_state["_cpu"] is False
										assert PartialState._shared_state["device"].type == "cuda"
										with self.assertRaises(lowerCAmelCase__     ):
														lowercase         =      Accelerator(cpu=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Dict:
										lowercase         =      Accelerator()
										lowercase         =      GradientState()
										assert state.num_steps == 1
										lowercase         =      4
										assert state.num_steps == 4

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





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()

										(
										    (
										    lowercase
										)     ,	(
										    lowercase
										)     ,	(
										    lowercase
										)     ,	(
										    lowercase
										)     ,	(
										    lowercase
										)     ,	
										)         =      accelerator.prepare(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										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							:List[Any]     )  ->Tuple:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()
										accelerator.prepare(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										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							:List[str]     )  ->Optional[int]:
										PartialState._reset_state()

										# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
										def noop(*lowerCAmelCase__							:int		,    **lowerCAmelCase__							:Optional[Any]     ):
														pass

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





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Union[str, Any]:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()
										accelerator.prepare(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      get_signature(lowerCAmelCase__     )

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

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

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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()
										accelerator.prepare(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      get_signature(lowerCAmelCase__     )

										# saving hook
										def save_config(lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Optional[Any]     ):
														lowercase         =      {"class_name": models[0].__class__.__name__}

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

        # loading hook
										def load_config(lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:List[str]     ):
														with open(os.path.join(lowerCAmelCase__		,    "data.json"     )		,    "r"     ) as f:
																		lowercase         =      json.load(lowerCAmelCase__     )

														lowercase         =      config["class_name"]

										lowercase         =      accelerator.register_save_state_pre_hook(lowerCAmelCase__     )
										lowercase         =      accelerator.register_load_state_pre_hook(lowerCAmelCase__     )

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

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

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

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

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

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

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

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->List[Any]:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()
										lowercase         =      None

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





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										lowercase         =      Accelerator()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      create_components()
										lowercase         =      [1, 2, 3]

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





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

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

										# This should work
										lowercase         =      accelerator.prepare(lowerCAmelCase__     )





						@slow
						@require_bnb
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[str]:
										from transformers import AutoModelForCausalLM

										lowercase         =      Accelerator()

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

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

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





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

										lowercase         =      {"distributed_type": DistributedType.MULTI_GPU}

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

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

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

										PartialState._reset_state()





						@slow
						@require_bnb
						@require_multi_gpu
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Tuple:
										from transformers import AutoModelForCausalLM

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

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

										# This should work
										lowercase         =      accelerator.prepare(lowerCAmelCase__     )





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