



'''simple docstring'''


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[Any]=9_9  ,						UpperCamelCase__      :					List[Any]=1_6  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Optional[int]=3_6  ,						UpperCamelCase__      :					str="gelu"  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Optional[int]=5_1_2  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=0.0_2  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  seq_length
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_input_mask
       UpperCamelCase					=  use_token_type_ids
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  hidden_dropout_prob
       UpperCamelCase					=  attention_probs_dropout_prob
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  type_vocab_size
       UpperCamelCase					=  type_sequence_label_size
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  num_choices
       UpperCamelCase					=  scope






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size )

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

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

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

       UpperCamelCase					=  self.get_config()

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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return MraConfig(
           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  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						)






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.get_config()
       UpperCamelCase					=  3_0_0
       return config






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  self.prepare_config_and_inputs()

       UpperCamelCase					=  True
       UpperCamelCase					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  MraModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  MraModel(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForQuestionAnswering(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						start_positions=UpperCamelCase__  ,						end_positions=UpperCamelCase__  ,						)
       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      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForSequenceClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForTokenClassification(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.num_choices
       UpperCamelCase					=  MraForMultipleChoice(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  input_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  token_type_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  input_mask.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices) )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  config_and_inputs
       UpperCamelCase					=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False

    _SCREAMING_SNAKE_CASE	  =  ()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MraModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
          UpperCamelCase					=  type
          self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )






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


       """simple docstring"""







       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  MraModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @unittest.skip(reason='MRA does not output attentions' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @slow
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  torch.Size((1, 2_5_6, 7_6_8) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )

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






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


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 2_5_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )

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






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


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       UpperCamelCase					=  torch.arange(4_0_9_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 4_0_9_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						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()

