






from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TFMobileBertForMaskedLM,
	    TFMobileBertForMultipleChoice,
	    TFMobileBertForNextSentencePrediction,
	    TFMobileBertForPreTraining,
	    TFMobileBertForQuestionAnswering,
	    TFMobileBertForSequenceClassification,
	    TFMobileBertForTokenClassification,
	    TFMobileBertModel,
	)

@require_tf
class     _SCREAMING_SNAKE_CASE       (			_a		,   _a		,   unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  (
	    (
	        TFMobileBertModel,
	        TFMobileBertForMaskedLM,
	        TFMobileBertForNextSentencePrediction,
	        TFMobileBertForPreTraining,
	        TFMobileBertForQuestionAnswering,
	        TFMobileBertForSequenceClassification,
	        TFMobileBertForTokenClassification,
	        TFMobileBertForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	snake_case__							:     Any     	=  (
	    {
	        """feature-extraction""": TFMobileBertModel,
	        """fill-mask""": TFMobileBertForMaskedLM,
	        """question-answering""": TFMobileBertForQuestionAnswering,
	        """text-classification""": TFMobileBertForSequenceClassification,
	        """token-classification""": TFMobileBertForTokenClassification,
	        """zero-shot""": TFMobileBertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	snake_case__							:     Union[str, Any]     	=  False
	snake_case__							:     Dict     	=  False





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int=False	):
		UpperCamelCase     :Dict          =	super()._prepare_for_class(__lowerCamelCase ,  __lowerCamelCase ,  return_labels=__lowerCamelCase	)

		if return_labels:
			if model_class in get_values(__lowerCamelCase	):
				UpperCamelCase     :List[Any]          =	tf.zeros(self.model_tester.batch_size ,  dtype=tf.intaa	)

		return inputs_dict

	class     _SCREAMING_SNAKE_CASE       (			_a      ):





		def __init__(		self				:					int ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					str=13 ,  __lowerCamelCase				:					List[Any]=7 ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Union[str, Any]=99 ,  __lowerCamelCase				:					Union[str, Any]=32 ,  __lowerCamelCase				:					Optional[int]=32 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					List[Any]=4 ,  __lowerCamelCase				:					Tuple=37 ,  __lowerCamelCase				:					Any="gelu" ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					List[Any]=0.1 ,  __lowerCamelCase				:					Tuple=512 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Optional[Any]=2 ,  __lowerCamelCase				:					Any=0.02 ,  __lowerCamelCase				:					Optional[int]=3 ,  __lowerCamelCase				:					Tuple=4 ,  __lowerCamelCase				:					List[str]=None ,  ):
			UpperCamelCase     :Optional[int]          =	parent
			UpperCamelCase     :str          =	batch_size
			UpperCamelCase     :Optional[int]          =	seq_length
			UpperCamelCase     :Optional[Any]          =	is_training
			UpperCamelCase     :List[str]          =	use_input_mask
			UpperCamelCase     :Union[str, Any]          =	use_token_type_ids
			UpperCamelCase     :Tuple          =	use_labels
			UpperCamelCase     :Union[str, Any]          =	vocab_size
			UpperCamelCase     :Optional[Any]          =	hidden_size
			UpperCamelCase     :Any          =	num_hidden_layers
			UpperCamelCase     :List[str]          =	num_attention_heads
			UpperCamelCase     :List[str]          =	intermediate_size
			UpperCamelCase     :int          =	hidden_act
			UpperCamelCase     :Any          =	hidden_dropout_prob
			UpperCamelCase     :int          =	attention_probs_dropout_prob
			UpperCamelCase     :str          =	max_position_embeddings
			UpperCamelCase     :str          =	type_vocab_size
			UpperCamelCase     :Union[str, Any]          =	type_sequence_label_size
			UpperCamelCase     :Tuple          =	initializer_range
			UpperCamelCase     :List[Any]          =	num_labels
			UpperCamelCase     :Union[str, Any]          =	num_choices
			UpperCamelCase     :List[Any]          =	scope
			UpperCamelCase     :Any          =	embedding_size





		def 						_A (		self				:					Dict	):
			UpperCamelCase     :Dict          =	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

			UpperCamelCase     :List[Any]          =	None
			if self.use_input_mask:
				UpperCamelCase     :Optional[int]          =	random_attention_mask([self.batch_size, self.seq_length]	)

			UpperCamelCase     :Tuple          =	None
			if self.use_token_type_ids:
				UpperCamelCase     :Optional[int]          =	ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size	)

			UpperCamelCase     :Optional[int]          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Tuple          =	None
			if self.use_labels:
				UpperCamelCase     :Any          =	ids_tensor([self.batch_size] ,  self.type_sequence_label_size	)
				UpperCamelCase     :Optional[Any]          =	ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels	)
				UpperCamelCase     :Tuple          =	ids_tensor([self.batch_size] ,  self.num_choices	)

			UpperCamelCase     :Union[str, Any]          =	MobileBertConfig(
			    vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  initializer_range=self.initializer_range ,  embedding_size=self.embedding_size ,  )

			return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





		def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Dict	):
			UpperCamelCase     :Dict          =	TFMobileBertModel(config=__lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :str          =	model(__lowerCamelCase	)

			UpperCamelCase     :Any          =	[input_ids, input_mask]
			UpperCamelCase     :Dict          =	model(__lowerCamelCase	)

			UpperCamelCase     :int          =	model(__lowerCamelCase	)

			self.parent.assertEqual(
			    result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)	)
			self.parent.assertEqual(result.pooler_output.shape ,  (self.batch_size, self.hidden_size)	)





		def 						_A (		self				:					Any ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					List[Any]	):
			UpperCamelCase     :Optional[Any]          =	TFMobileBertForMaskedLM(config=__lowerCamelCase	)
			UpperCamelCase     :int          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :Dict          =	model(__lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)	)





		def 						_A (		self				:					int ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					int	):
			UpperCamelCase     :str          =	TFMobileBertForNextSentencePrediction(config=__lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :int          =	model(__lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape ,  (self.batch_size, 2)	)





		def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any]	):
			UpperCamelCase     :Optional[int]          =	TFMobileBertForPreTraining(config=__lowerCamelCase	)
			UpperCamelCase     :Any          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :Any          =	model(__lowerCamelCase	)
			self.parent.assertEqual(
			    result.prediction_logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)	)
			self.parent.assertEqual(result.seq_relationship_logits.shape ,  (self.batch_size, 2)	)





		def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any	):
			UpperCamelCase     :int          =	self.num_labels
			UpperCamelCase     :Union[str, Any]          =	TFMobileBertForSequenceClassification(config=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :List[str]          =	model(__lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)	)





		def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
			UpperCamelCase     :str          =	self.num_choices
			UpperCamelCase     :List[str]          =	TFMobileBertForMultipleChoice(config=__lowerCamelCase	)
			UpperCamelCase     :Dict          =	tf.tile(tf.expand_dims(__lowerCamelCase ,  1	) ,  (1, self.num_choices, 1)	)
			UpperCamelCase     :Tuple          =	tf.tile(tf.expand_dims(__lowerCamelCase ,  1	) ,  (1, self.num_choices, 1)	)
			UpperCamelCase     :Optional[Any]          =	tf.tile(tf.expand_dims(__lowerCamelCase ,  1	) ,  (1, self.num_choices, 1)	)
			UpperCamelCase     :int          =	{
			    """input_ids""": multiple_choice_inputs_ids,
			    """attention_mask""": multiple_choice_input_mask,
			    """token_type_ids""": multiple_choice_token_type_ids,
			}
			UpperCamelCase     :List[str]          =	model(__lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices)	)





		def 						_A (		self				:					int ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Dict	):
			UpperCamelCase     :str          =	self.num_labels
			UpperCamelCase     :int          =	TFMobileBertForTokenClassification(config=__lowerCamelCase	)
			UpperCamelCase     :Dict          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
			self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels)	)





		def 						_A (		self				:					int ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Any	):
			UpperCamelCase     :Union[str, Any]          =	TFMobileBertForQuestionAnswering(config=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
			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 						_A (		self				:					str	):
			UpperCamelCase     :Tuple          =	self.prepare_config_and_inputs()
			(
			    (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      (
			    UpperCamelCase
			)     ,      
			)     :Dict          =	config_and_inputs
			UpperCamelCase     :Optional[Any]          =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
			return config, inputs_dict





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :int          =	TFMobileBertModelTest.TFMobileBertModelTester(self	)
		UpperCamelCase     :Dict          =	ConfigTester(self ,  config_class=__lowerCamelCase ,  hidden_size=37	)





	def 						_A (		self				:					int	):
		self.config_tester.run_common_tests()





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_model(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Tuple          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCamelCase	)





	def 						_A (		self				:					str	):
		UpperCamelCase     :Union[str, Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCamelCase	)





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :str          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Union[str, Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     :str          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCamelCase	)





	@slow
	def 						_A (		self				:					int	):
		# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
		for model_name in ["google/mobilebert-uncased"]:
			UpperCamelCase     :Optional[int]          =	TFMobileBertModel.from_pretrained(__lowerCamelCase	)
			self.assertIsNotNone(__lowerCamelCase	)

@require_tf
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	@slow
	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :List[Any]          =	TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""	)
		UpperCamelCase     :str          =	tf.constant([[0, 1, 2, 3, 4, 5]]	)
		UpperCamelCase     :Dict          =	model(__lowerCamelCase	)[0]

		UpperCamelCase     :str          =	[1, 6, 30_522]
		self.assertEqual(output.shape ,  __lowerCamelCase	)

		UpperCamelCase     :int          =	tf.constant(
		    [
		        [
		            [-4.5919547, -9.248295, -9.645256],
		            [-6.7306175, -6.440284, -6.6052837],
		            [-7.2743506, -6.7847915, -6.024673],
		        ]
		    ]	)
		tf.debugging.assert_near(output[:, :3, :3] ,  __lowerCamelCase ,  atol=1E-4	)





'''simple docstring'''


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


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







def 				__lowerCamelCase				(   A__=2    ,						A__=3    ,						A__=16    ,						A__ = 10    ,						A__ = 2    )       ->	int:






   """simple docstring"""

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

   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   return (train_dataloader, valid_dataloader)







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__=None    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  []
   for epoch in range(A__    ):
      # Train quickly
      model.train()
      for batch in dataloader:
         UpperCamelCase				,      UpperCamelCase					=  batch
         UpperCamelCase					=  model(A__    )
         UpperCamelCase					=  torch.nn.functional.mse_loss(A__    ,						A__    )
         accelerator.backward(A__    )
         optimizer.step()
         optimizer.zero_grad()
      rands.append(random.random()    )  # Introduce some randomness
      if scheduler is not None:
         scheduler.step()
   return rands

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Tuple ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )






    def  A       (  self      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       return x * self.a + self.b

class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(total_limit=1  ,						project_dir=UpperCamelCase__  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir ) )  ,						1 )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          # Train baseline
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'initial' )
          accelerator.save_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          UpperCamelCase					=  train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

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

          # Load everything back in and make sure all states work
          accelerator.load_state(UpperCamelCase__ )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )

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

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(iteration=1  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  train(2  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_1' ) )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  torch.tensor([1, 2, 3] )
       UpperCamelCase					=  torch.tensor([2, 3, 4] )
       UpperCamelCase					=  DummyModel()
       UpperCamelCase					=  torch.optim.Adam(net.parameters() )
       UpperCamelCase					=  Accelerator()
       with self.assertRaises(UpperCamelCase__ ) as ve:
          accelerator.register_for_checkpointing(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  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  A       (  self      :					Dict ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase					=  torch.optim.lr_scheduler.StepLR(UpperCamelCase__  ,						step_size=1  ,						gamma=0.9_9 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()
          UpperCamelCase					=  scheduler.state_dict()
          train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertNotEqual(UpperCamelCase__  ,						scheduler.state_dict() )

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          self.assertEqual(UpperCamelCase__  ,						scheduler.state_dict() )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__  ,						total_limit=2 )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase					=  accelerator.prepare(UpperCamelCase__ )
          # Save 3 states:
          for _ in range(1_1 ):
             accelerator.save_state()
          self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_9' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_10' ) ) )






    @require_cuda
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
       execute_subprocess_async(UpperCamelCase__  ,						env=os.environ.copy() )


if __name__ == "__main__":
   _lowerCamelCase	:       Optional[int]      	=	"/tmp/accelerate/state_checkpointing"
   _lowerCamelCase	:       Union[str, Any]      	=	DummyModel()
   _lowerCamelCase	:       Optional[Any]      	=	torch.optim.Adam(params=model.parameters(), lr=1e-3)
   _lowerCamelCase	:       List[Any]      	=	torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	dummy_dataloaders()
   _lowerCamelCase	:       List[Any]      	=	ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   _lowerCamelCase	:       Any      	=	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)
   _lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	:       Union[str, Any]      	=	accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	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:
      _lowerCamelCase	:       Any      	=	group["params"][0].device
      break
   assert param_device.type == accelerator.device.type
   _lowerCamelCase	:       Tuple      	=	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:
      _lowerCamelCase	:       Optional[Any]      	=	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:
      _lowerCamelCase	:       Dict      	=	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()

