



'''simple docstring'''


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Dict=1_4  ,						UpperCamelCase__      :					str=7  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Union[str, Any]=True  ,						UpperCamelCase__      :					Dict=False  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					Any=9_9  ,						UpperCamelCase__      :					Tuple=3_2  ,						UpperCamelCase__      :					Dict=4  ,						UpperCamelCase__      :					Optional[int]=4  ,						UpperCamelCase__      :					Optional[int]=4  ,						UpperCamelCase__      :					Tuple=3_7  ,						UpperCamelCase__      :					Union[str, Any]="gelu"  ,						UpperCamelCase__      :					int=0.1  ,						UpperCamelCase__      :					int=0.1  ,						UpperCamelCase__      :					Optional[Any]=5_1_2  ,						UpperCamelCase__      :					Optional[int]=0.0_2  ,						):


       """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					=  rotary_dim
       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					=  initializer_range
       UpperCamelCase					=  None
       UpperCamelCase					=  vocab_size - 1
       UpperCamelCase					=  vocab_size - 1
       UpperCamelCase					=  vocab_size - 1






    def  A       (  self      :					List[str] ):


       """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					=  GPTJConfig(
           vocab_size=self.vocab_size  ,						n_embd=self.hidden_size  ,						n_layer=self.num_hidden_layers  ,						n_head=self.num_attention_heads  ,						n_positions=self.max_position_embeddings  ,						use_cache=UpperCamelCase__  ,						bos_token_id=self.bos_token_id  ,						eos_token_id=self.eos_token_id  ,						pad_token_id=self.pad_token_id  ,						rotary_dim=self.rotary_dim  ,						)

       return (config, input_ids, input_mask)






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







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






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


       """simple docstring"""







       UpperCamelCase					=  2_0
       UpperCamelCase					=  model_class_name(UpperCamelCase__ )

       UpperCamelCase					=  model.init_cache(input_ids.shape[0]  ,						UpperCamelCase__ )
       UpperCamelCase					=  jnp.ones((input_ids.shape[0], max_decoder_length)  ,						dtype='i4' )

       UpperCamelCase					=  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1 )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1) )
       UpperCamelCase					=  model(
           input_ids[:, :-1]  ,						attention_mask=UpperCamelCase__  ,						past_key_values=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)

       UpperCamelCase					=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,						dtype='i4' )
       UpperCamelCase					=  model(
           input_ids[:, -1:]  ,						attention_mask=UpperCamelCase__  ,						past_key_values=outputs_cache.past_key_values  ,						position_ids=UpperCamelCase__  ,						)

       UpperCamelCase					=  model(UpperCamelCase__ )

       UpperCamelCase					=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1E-3  ,						msg=f"""Max diff is {diff}""" )






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


       """simple docstring"""







       UpperCamelCase					=  2_0
       UpperCamelCase					=  model_class_name(UpperCamelCase__ )

       UpperCamelCase					=  jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]  ,						axis=-1  ,						)

       UpperCamelCase					=  model.init_cache(input_ids.shape[0]  ,						UpperCamelCase__ )
       UpperCamelCase					=  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1 )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1) )

       UpperCamelCase					=  model(
           input_ids[:, :-1]  ,						attention_mask=UpperCamelCase__  ,						past_key_values=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)
       UpperCamelCase					=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,						dtype='i4' )
       UpperCamelCase					=  model(
           input_ids[:, -1:]  ,						past_key_values=outputs_cache.past_key_values  ,						attention_mask=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)

       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ )

       UpperCamelCase					=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1E-3  ,						msg=f"""Max diff is {diff}""" )

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






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    _SCREAMING_SNAKE_CASE	  =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  FlaxGPTJModelTester(self )






    def  A       (  self      :					int ):


       """simple docstring"""







       for model_class_name in self.all_model_classes:
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       for model_class_name in self.all_model_classes:
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






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


       """simple docstring"""







       UpperCamelCase					=  GPTaTokenizer.from_pretrained('gpt2'  ,						pad_token='<|endoftext|>'  ,						padding_side='left' )
       UpperCamelCase					=  tokenizer(['Hello this is a long string', 'Hey']  ,						return_tensors='np'  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__ )

       UpperCamelCase					=  FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
       UpperCamelCase					=  False
       UpperCamelCase					=  model.config.eos_token_id

       UpperCamelCase					=  jax.jit(model.generate )

       UpperCamelCase					=  jit_generate(
           inputs['input_ids']  ,						attention_mask=inputs['attention_mask']  ,						pad_token_id=tokenizer.pad_token_id ).sequences

       UpperCamelCase					=  tokenizer.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__ )

       UpperCamelCase					=  [
           'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
           'Hey, I\'m a little late to the party. I\'m going to',
       ]

       self.assertListEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    @is_pt_flax_cross_test
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             # prepare inputs
             UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ )
             UpperCamelCase					=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             UpperCamelCase					=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             UpperCamelCase					=  getattr(UpperCamelCase__  ,						UpperCamelCase__ )

             UpperCamelCase				,      UpperCamelCase					=  pt_inputs['input_ids'].shape
             UpperCamelCase					=  np.random.randint(0  ,						seq_length - 1  ,						size=(batch_size,) )
             for batch_idx, start_index in enumerate(UpperCamelCase__ ):
                UpperCamelCase					=  0
                UpperCamelCase					=  1
                UpperCamelCase					=  0
                UpperCamelCase					=  1
             UpperCamelCase					=  pt_model_class(UpperCamelCase__ ).eval()
             UpperCamelCase					=  model_class(UpperCamelCase__  ,						dtype=jnp.floataa )

             UpperCamelCase					=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,						UpperCamelCase__ )
             UpperCamelCase					=  fx_state

             with torch.no_grad():
                UpperCamelCase					=  pt_model(**UpperCamelCase__ ).to_tuple()

             UpperCamelCase					=  fx_model(**UpperCamelCase__ ).to_tuple()
             self.assertEqual(len(UpperCamelCase__ )  ,						len(UpperCamelCase__ )  ,						'Output lengths differ between Flax and PyTorch' )
             for fx_output, pt_output in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
                self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2 )

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(UpperCamelCase__ )
                UpperCamelCase					=  model_class.from_pretrained(UpperCamelCase__  ,						from_pt=UpperCamelCase__ )

             UpperCamelCase					=  fx_model_loaded(**UpperCamelCase__ ).to_tuple()
             self.assertEqual(
                 len(UpperCamelCase__ )  ,						len(UpperCamelCase__ )  ,						'Output lengths differ between Flax and PyTorch' )
             for fx_output_loaded, pt_output in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
                self.assert_almost_equals(fx_output_loaded[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2 )






    @is_pt_flax_cross_test
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             # prepare inputs
             UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ )
             UpperCamelCase					=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             UpperCamelCase					=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             UpperCamelCase					=  getattr(UpperCamelCase__  ,						UpperCamelCase__ )

             UpperCamelCase					=  pt_model_class(UpperCamelCase__ ).eval()
             UpperCamelCase					=  model_class(UpperCamelCase__  ,						dtype=jnp.floataa )

             UpperCamelCase					=  load_flax_weights_in_pytorch_model(UpperCamelCase__  ,						fx_model.params )
             UpperCamelCase				,      UpperCamelCase					=  pt_inputs['input_ids'].shape
             UpperCamelCase					=  np.random.randint(0  ,						seq_length - 1  ,						size=(batch_size,) )
             for batch_idx, start_index in enumerate(UpperCamelCase__ ):
                UpperCamelCase					=  0
                UpperCamelCase					=  1
                UpperCamelCase					=  0
                UpperCamelCase					=  1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
                UpperCamelCase					=  pt_model(**UpperCamelCase__ ).to_tuple()

             UpperCamelCase					=  fx_model(**UpperCamelCase__ ).to_tuple()
             self.assertEqual(len(UpperCamelCase__ )  ,						len(UpperCamelCase__ )  ,						'Output lengths differ between Flax and PyTorch' )
             for fx_output, pt_output in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
                self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2 )

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(UpperCamelCase__ )
                UpperCamelCase					=  pt_model_class.from_pretrained(UpperCamelCase__  ,						from_flax=UpperCamelCase__ )

             with torch.no_grad():
                UpperCamelCase					=  pt_model_loaded(**UpperCamelCase__ ).to_tuple()

             self.assertEqual(
                 len(UpperCamelCase__ )  ,						len(UpperCamelCase__ )  ,						'Output lengths differ between Flax and PyTorch' )
             for fx_output, pt_output in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
                self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2 )






    @tooslow
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       for model_class_name in self.all_model_classes:
          UpperCamelCase					=  model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
          UpperCamelCase					=  model(np.ones((1, 1) ) )
          self.assertIsNotNone(UpperCamelCase__ )






'''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()

