






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    FlaubertForMultipleChoice,
							    FlaubertForQuestionAnswering,
							    FlaubertForQuestionAnsweringSimple,
							    FlaubertForSequenceClassification,
							    FlaubertForTokenClassification,
							    FlaubertModel,
							    FlaubertWithLMHeadModel,
							)
							from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class       _UpperCAmelCase					(       lowerCamelCase_	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    int,  lowerCamelCase						:    List[Any]=13,  lowerCamelCase						:    Any=7,  lowerCamelCase						:    Union[str, Any]=True,  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    Optional[int]=False,  lowerCamelCase						:    Union[str, Any]=False,  lowerCamelCase						:    Optional[int]=False,  lowerCamelCase						:    Optional[int]=2,  lowerCamelCase						:    Tuple=99,  lowerCamelCase						:    Optional[Any]=0,  lowerCamelCase						:    Union[str, Any]=32,  lowerCamelCase						:    int=5,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Dict=512,  lowerCamelCase						:    List[str]=12,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    List[str]=0.02,  lowerCamelCase						:    Any=3,  lowerCamelCase						:    Tuple=4,  lowerCamelCase						:    List[Any]="last",  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Dict=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_input_lengths
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_labels
								lowercase__    =			gelu_activation
								lowercase__    =			sinusoidal_embeddings
								lowercase__    =			causal
								lowercase__    =			asm
								lowercase__    =			n_langs
								lowercase__    =			vocab_size
								lowercase__    =			n_special
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			summary_type
								lowercase__    =			use_proj
								lowercase__    =			scope




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )
								lowercase__    =			random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__    =			None
								if self.use_input_lengths:
												lowercase__    =			(
												    ids_tensor([self.batch_size],  vocab_size=2  ) + self.seq_length - 2
												)  # small variation of seq_length

								lowercase__    =			None
								if self.use_token_type_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.n_langs  )

								lowercase__    =			None
								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size],  2  ).float()
												lowercase__    =			ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__    =			self.get_config()

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								return FlaubertConfig(
								    vocab_size=self.vocab_size,  n_special=self.n_special,  emb_dim=self.hidden_size,  n_layers=self.num_hidden_layers,  n_heads=self.num_attention_heads,  dropout=self.hidden_dropout_prob,  attention_dropout=self.attention_probs_dropout_prob,  gelu_activation=self.gelu_activation,  sinusoidal_embeddings=self.sinusoidal_embeddings,  asm=self.asm,  causal=self.causal,  n_langs=self.n_langs,  max_position_embeddings=self.max_position_embeddings,  initializer_range=self.initializer_range,  summary_type=self.summary_type,  use_proj=self.use_proj,  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    List[str],  ):

								'''simple docstring'''
								lowercase__    =			FlaubertModel(config=__snake_case  )
								model.to(__snake_case  )
								model.eval()
								lowercase__    =			model(__snake_case,  lengths=__snake_case,  langs=__snake_case  )
								lowercase__    =			model(__snake_case,  langs=__snake_case  )
								lowercase__    =			model(__snake_case  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Dict,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    str,  lowerCamelCase						:    Tuple,  ):

								'''simple docstring'''
								lowercase__    =			FlaubertWithLMHeadModel(__snake_case  )
								model.to(__snake_case  )
								model.eval()

								lowercase__    =			model(__snake_case,  token_type_ids=__snake_case,  labels=__snake_case  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    int,  lowerCamelCase						:    List[str],  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    List[Any],  ):

								'''simple docstring'''
								lowercase__    =			FlaubertForQuestionAnsweringSimple(__snake_case  )
								model.to(__snake_case  )
								model.eval()

								lowercase__    =			model(__snake_case  )

								lowercase__    =			model(__snake_case,  start_positions=__snake_case,  end_positions=__snake_case  )
								self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Dict,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    str,  ):

								'''simple docstring'''
								lowercase__    =			FlaubertForQuestionAnswering(__snake_case  )
								model.to(__snake_case  )
								model.eval()

								lowercase__    =			model(__snake_case  )

								lowercase__    =			model(
								    __snake_case,  start_positions=__snake_case,  end_positions=__snake_case,  cls_index=__snake_case,  is_impossible=__snake_case,  p_mask=__snake_case,  )

								lowercase__    =			model(
								    __snake_case,  start_positions=__snake_case,  end_positions=__snake_case,  cls_index=__snake_case,  is_impossible=__snake_case,  )

								((lowercase__					) ,											)    =			result_with_labels.to_tuple()

								lowercase__    =			model(__snake_case,  start_positions=__snake_case,  end_positions=__snake_case  )

								((lowercase__					) ,											)    =			result_with_labels.to_tuple()

								self.parent.assertEqual(result_with_labels.loss.shape,  ()  )
								self.parent.assertEqual(result.start_top_log_probs.shape,  (self.batch_size, model.config.start_n_top)  )
								self.parent.assertEqual(result.start_top_index.shape,  (self.batch_size, model.config.start_n_top)  )
								self.parent.assertEqual(
								    result.end_top_log_probs.shape,  (self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
								self.parent.assertEqual(
								    result.end_top_index.shape,  (self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
								self.parent.assertEqual(result.cls_logits.shape,  (self.batch_size,)  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    List[Any],  ):

								'''simple docstring'''
								lowercase__    =			FlaubertForSequenceClassification(__snake_case  )
								model.to(__snake_case  )
								model.eval()

								lowercase__    =			model(__snake_case  )
								lowercase__    =			model(__snake_case,  labels=__snake_case  )

								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)  )




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    int,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[Any],  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			FlaubertForTokenClassification(__snake_case  )
								model.to(__snake_case  )
								model.eval()

								lowercase__    =			model(__snake_case,  attention_mask=__snake_case,  labels=__snake_case  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)  )




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Dict,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  ):

								'''simple docstring'''
								lowercase__    =			self.num_choices
								lowercase__    =			FlaubertForMultipleChoice(config=__snake_case  )
								model.to(__snake_case  )
								model.eval()
								lowercase__    =			input_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__    =			token_type_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__    =			input_mask.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__    =			model(
								    __snake_case,  attention_mask=__snake_case,  token_type_ids=__snake_case,  labels=__snake_case,  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()
								(
								    (
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,						(
								    lowercase__					
								) ,											
								)    =			config_and_inputs
								lowercase__    =			{
								    '''input_ids''': input_ids,
								    '''token_type_ids''': token_type_ids,
								    '''lengths''': input_lengths,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict



@require_torch
class       _UpperCAmelCase					(       lowerCamelCase_		,lowerCamelCase_		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (
				    (
				        FlaubertModel,
				        FlaubertWithLMHeadModel,
				        FlaubertForQuestionAnswering,
				        FlaubertForQuestionAnsweringSimple,
				        FlaubertForSequenceClassification,
				        FlaubertForTokenClassification,
				        FlaubertForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__											=       (
				    {
				        '''feature-extraction''': FlaubertModel,
				        '''fill-mask''': FlaubertWithLMHeadModel,
				        '''question-answering''': FlaubertForQuestionAnsweringSimple,
				        '''text-classification''': FlaubertForSequenceClassification,
				        '''token-classification''': FlaubertForTokenClassification,
				        '''zero-shot''': FlaubertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Dict,  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith('''Fast'''  )
								):
												# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
												# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
												# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
												return True

								return False




				def 				lowercase__   (     self						:    Tuple,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    List[Any]=False  ):

								'''simple docstring'''
								lowercase__    =			super()._prepare_for_class(__snake_case,  __snake_case,  return_labels=__snake_case  )

								if return_labels:
												if model_class.__name__ == "FlaubertForQuestionAnswering":
																lowercase__    =			torch.zeros(
																    self.model_tester.batch_size,  dtype=torch.long,  device=__snake_case  )
																lowercase__    =			torch.zeros(
																    self.model_tester.batch_size,  dtype=torch.long,  device=__snake_case  )

								return inputs_dict




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			FlaubertModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=__snake_case,  emb_dim=37  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*__snake_case  )




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*__snake_case  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_simple_qa(*__snake_case  )




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*__snake_case  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*__snake_case  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_token_classif(*__snake_case  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_multiple_choice(*__snake_case  )




				@slow
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			FlaubertModel.from_pretrained(__snake_case  )
												self.assertIsNotNone(__snake_case  )




				@slow
				@require_torch_gpu
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == FlaubertForMultipleChoice:
																return

												lowercase__    =			True
												lowercase__    =			model_class(config=__snake_case  )

												lowercase__    =			self._prepare_for_class(__snake_case,  __snake_case  )
												lowercase__    =			torch.jit.trace(
												    __snake_case,  (inputs_dict['''input_ids'''].to('''cpu'''  ), inputs_dict['''attention_mask'''].to('''cpu'''  ))  )

												with tempfile.TemporaryDirectory() as tmp:
																torch.jit.save(__snake_case,  os.path.join(__snake_case,  '''traced_model.pt'''  )  )
																lowercase__    =			torch.jit.load(os.path.join(__snake_case,  '''traced_model.pt'''  ),  map_location=__snake_case  )
																loaded(inputs_dict['''input_ids'''].to(__snake_case  ),  inputs_dict['''attention_mask'''].to(__snake_case  )  )



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				@slow
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''  )
								lowercase__    =			torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]  )
								with torch.no_grad():
												lowercase__    =			model(__snake_case  )[0]
								lowercase__    =			torch.Size((1, 11, 768)  )
								self.assertEqual(output.shape,  __snake_case  )
								lowercase__    =			torch.tensor(
								    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]  )

								self.assertTrue(torch.allclose(output[:, :3, :3],  __snake_case,  atol=1E-4  )  )








import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

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

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


snake_case							:       List[Any]     			=      logging.getLogger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				str=2					,    __lowerCAmelCase							:				Optional[int]=3					,    __lowerCAmelCase							:				List[str]=1_6					,    __lowerCAmelCase							:				int = 1_0					,    __lowerCAmelCase							:				int = 2   ):

							def get_dataset(__lowerCAmelCase							:				Dict   ):
														a__             =     torch.randn(batch_size * n_batches					,    1   )
														return TensorDataset(__lowerCAmelCase					,    a * x + b + 0.1 * torch.randn(batch_size * n_batches					,    1   )   )

							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							return (train_dataloader, valid_dataloader)






def 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Optional[int]=None   ):
							a__             =     []
							for epoch in range(__lowerCAmelCase   ):
														# Train quickly
														model.train()
														for batch in dataloader:
																					a__      ,					a__             =     batch
																					a__             =     model(__lowerCAmelCase   )
																					a__             =     torch.nn.functional.mse_loss(__lowerCAmelCase					,    __lowerCAmelCase   )
																					accelerator.backward(__lowerCAmelCase   )
																					optimizer.step()
																					optimizer.zero_grad()
														rands.append(random.random()   )  # Introduce some randomness
														if scheduler is not None:
																					scheduler.step()
							return rands







class        snake_case_   (nn.Module      ):
					def __init__(      self   :Any      )					->    Union[str, Any]:
												super().__init__()
												a__             =     nn.Parameter(torch.randn(1      )      )
												a__             =     nn.Parameter(torch.randn(1      )      )



					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Union[str, Any]      )					->    str:
												return x * self.a + self.b







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(total_limit=1			,project_dir=__snake_case			,automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()

																			# Save second state
																			accelerator.save_state()
																			self.assertEqual(len(os.listdir(accelerator.project_dir      )      )			,1      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    str:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			# Train baseline
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			a__             =     os.path.join(__snake_case			,'initial'      )
																			accelerator.save_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

																			# Train partially
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			accelerator.load_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			a__             =     os.path.join(__snake_case			,'checkpoint'      )
																			accelerator.save_state(__snake_case      )

																			# Load everything back in and make sure all states work
																			accelerator.load_state(__snake_case      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )

																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

																			# Train partially
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(iteration=1			,automatic_checkpoint_naming=__snake_case      )
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			accelerator.save_state()

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_1'      )      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												a__             =     torch.tensor([1, 2, 3]      )
												a__             =     torch.tensor([2, 3, 4]      )
												a__             =     DummyModel()
												a__             =     torch.optim.Adam(net.parameters()      )
												a__             =     Accelerator()
												with self.assertRaises(__snake_case      ) as ve:
																			accelerator.register_for_checkpointing(__snake_case			,__snake_case			,__snake_case			,__snake_case      )
												a__             =     str(ve.exception      )
												self.assertTrue('Item at index 0' in message      )
												self.assertTrue('Item at index 1' in message      )
												self.assertFalse('Item at index 2' in message      )
												self.assertFalse('Item at index 3' in message      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__             =     torch.optim.lr_scheduler.StepLR(__snake_case			,step_size=1			,gamma=0.99      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()
																			a__             =     scheduler.state_dict()
																			train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			self.assertNotEqual(__snake_case			,scheduler.state_dict()      )

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )
																			self.assertEqual(__snake_case			,scheduler.state_dict()      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case			,total_limit=2      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__             =     accelerator.prepare(__snake_case      )
																			# Save 3 states:
																			for _ in range(11      ):
																										accelerator.save_state()
																			self.assertTrue(not os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_9'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_10'      )      )      )



					@require_cuda
					def  lowerCamelCase__(      self   :Dict      )					->    str:
												a__             =     ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
												execute_subprocess_async(__snake_case			,env=os.environ.copy()      )


if __name__ == "__main__":
						snake_case							:       Tuple     			=      '''/tmp/accelerate/state_checkpointing'''
						snake_case							:       str     			=      DummyModel()
						snake_case							:       List[Any]     			=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
						snake_case							:       Union[str, Any]     			=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						snake_case			,     snake_case							:       str     			=      dummy_dataloaders()
						snake_case							:       Union[str, Any]     			=      ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						snake_case							:       Dict     			=      Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
						if accelerator.process_index == 0:
												if os.path.exists(savedir):
																		shutil.rmtree(savedir)
												os.makedirs(savedir)
						snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case							:       List[str]     			=      accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						snake_case			,     snake_case							:       Any     			=      accelerator.prepare(model, optimizer)
						train(3, model, train_dataloader, optimizer, accelerator, scheduler)
						# Check that the intial optimizer is loaded on the GPU
						for group in optimizer.param_groups:
												snake_case							:       Any     			=      group['''params'''][0].device
												break
						assert param_device.type == accelerator.device.type
						snake_case							:       Union[str, Any]     			=      model.cpu()
						accelerator.wait_for_everyone()
						accelerator.save_state()
						accelerator.wait_for_everyone()

						# Check CPU state
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
						for group in optimizer.param_groups:
												snake_case							:       int     			=      group['''params'''][0].device
												break
						assert (
						    param_device.type == torch.device('''cpu''').type
						), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

						# Check device state
						model.to(accelerator.device)
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
						for group in optimizer.param_groups:
												snake_case							:       Optional[int]     			=      group['''params'''][0].device
												break
						assert (
						    param_device.type == accelerator.device.type
						), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

						# Check error
						with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
												accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
						accelerator.wait_for_everyone()
						if accelerator.process_index == 0:
												shutil.rmtree(savedir)
						accelerator.wait_for_everyone()




