"""simple docstring"""


import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__UpperCamelCase									=			logging.getLogger(__name__)
__UpperCamelCase									=			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__UpperCamelCase									=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": (
                   "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
               )
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase__   )}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": (
                   "Override some existing default config settings when a model is trained from scratch. Example: "
                   "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
               )
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default="main"    ,		metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           }    ,		)







       def 		a_						( self)      ->      Tuple:
            if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                 raise ValueError(
                     '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path')




@dataclass
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "The name of the dataset to use (via the datasets library)."}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
       SCREAMING_SNAKE_CASE_			=      field(default=lowerCAmelCase__    ,		metadata={"help": "The input training data file (a text file)."}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Overwrite the cached training and evaluation sets"}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=5    ,		metadata={
               "help": "The percentage of the train set used as validation set in case there's no validation split"
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated. Default to the max input length of the model."
               )
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "The number of processes to use for the preprocessing."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=0.15    ,		metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}   )
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": (
                   "Whether to pad all samples to `max_seq_length`. "
                   "If False, will pad the samples dynamically when batching to the maximum length in the batch."
               )
           }    ,		)







       def 		a_						( self)      ->      Optional[Any]:
            if self.train_file is not None:
                 snake_case_					       =					self.train_file.split('.')[-1]
                 assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
            if self.validation_file is not None:
                 snake_case_					       =					self.validation_file.split('.')[-1]
                 assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     with open(UpperCAmelCase						,					'r'						,					encoding='utf-8'					) as f:
          snake_case_					       =					[json.loads(UpperCAmelCase					) for line in f.read().splitlines() if (len(UpperCAmelCase					) > 0 and not line.isspace())]
     assert len(UpperCAmelCase					) == len(UpperCAmelCase					)

     snake_case_					       =					{c: dataset[c] for c in dataset.column_names}
     snake_case_					       =					refs
     return Dataset.from_dict(UpperCAmelCase					)







def 					UpperCAmelCase    (		)				->      Optional[Any]:
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     snake_case_					       =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
     if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          snake_case_       ,	snake_case_       ,	snake_case_					       =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
     else:
          snake_case_       ,	snake_case_       ,	snake_case_					       =					parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     snake_case_					       =					None
     if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
          snake_case_					       =					get_last_checkpoint(training_args.output_dir					)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'					)
          elif last_checkpoint is not None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,					datefmt='%m/%d/%Y %H:%M:%S'						,					handlers=[logging.StreamHandler(sys.stdout					)]						,					)
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank					):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'						,					UpperCAmelCase					)

     # Set seed before initializing model.
     set_seed(training_args.seed					)

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          snake_case_					       =					load_dataset(data_args.dataset_name						,					data_args.dataset_config_name					)
          if "validation" not in datasets.keys():
               snake_case_					       =					load_dataset(
                   data_args.dataset_name						,					data_args.dataset_config_name						,					split=f'train[:{data_args.validation_split_percentage}%]'						,					)
               snake_case_					       =					load_dataset(
                   data_args.dataset_name						,					data_args.dataset_config_name						,					split=f'train[{data_args.validation_split_percentage}%:]'						,					)
     else:
          snake_case_					       =					{}
          if data_args.train_file is not None:
               snake_case_					       =					data_args.train_file
          if data_args.validation_file is not None:
               snake_case_					       =					data_args.validation_file
          snake_case_					       =					data_args.train_file.split('.'					)[-1]
          if extension == "txt":
               snake_case_					       =					'text'
          snake_case_					       =					load_dataset(UpperCAmelCase						,					data_files=UpperCAmelCase					)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     snake_case_					       =					{
         'cache_dir': model_args.cache_dir,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          snake_case_					       =					AutoConfig.from_pretrained(model_args.config_name						,					**UpperCAmelCase					)
     elif model_args.model_name_or_path:
          snake_case_					       =					AutoConfig.from_pretrained(model_args.model_name_or_path						,					**UpperCAmelCase					)
     else:
          snake_case_					       =					CONFIG_MAPPING[model_args.model_type]()
          logger.warning('You are instantiating a new config instance from scratch.'					)
          if model_args.config_overrides is not None:
               logger.info(f'Overriding config: {model_args.config_overrides}'					)
               config.update_from_string(model_args.config_overrides					)
               logger.info(f'New config: {config}'					)

     snake_case_					       =					{
         'cache_dir': model_args.cache_dir,
         'use_fast': model_args.use_fast_tokenizer,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          snake_case_					       =					AutoTokenizer.from_pretrained(model_args.tokenizer_name						,					**UpperCAmelCase					)
     elif model_args.model_name_or_path:
          snake_case_					       =					AutoTokenizer.from_pretrained(model_args.model_name_or_path						,					**UpperCAmelCase					)
     else:
          raise ValueError(
              'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
              'You can do it from another script, save it, and load it from here, using --tokenizer_name.'					)

     if model_args.model_name_or_path:
          snake_case_					       =					AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path						,					from_tf=bool('.ckpt' in model_args.model_name_or_path					)						,					config=UpperCAmelCase						,					cache_dir=model_args.cache_dir						,					revision=model_args.model_revision						,					use_auth_token=True if model_args.use_auth_token else None						,					)
     else:
          logger.info('Training new model from scratch'					)
          snake_case_					       =					AutoModelForMaskedLM.from_config(UpperCAmelCase					)

     model.resize_token_embeddings(len(UpperCAmelCase					)					)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          snake_case_					       =					datasets['train'].column_names
     else:
          snake_case_					       =					datasets['validation'].column_names
     snake_case_					       =					'text' if 'text' in column_names else column_names[0]

     snake_case_					       =					'max_length' if data_args.pad_to_max_length else False

     def tokenize_function(UpperCAmelCase					):
          # Remove empty lines
          snake_case_					       =					[line for line in examples['text'] if len(UpperCAmelCase					) > 0 and not line.isspace()]
          return tokenizer(examples['text']						,					padding=UpperCAmelCase						,					truncation=UpperCAmelCase						,					max_length=data_args.max_seq_length					)

     snake_case_					       =					datasets.map(
         UpperCAmelCase						,					batched=UpperCAmelCase						,					num_proc=data_args.preprocessing_num_workers						,					remove_columns=[text_column_name]						,					load_from_cache_file=not data_args.overwrite_cache						,					)

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          snake_case_					       =					add_chinese_references(tokenized_datasets['train']						,					data_args.train_ref_file					)
     if data_args.validation_ref_file is not None:
          snake_case_					       =					add_chinese_references(
              tokenized_datasets['validation']						,					data_args.validation_ref_file					)
     # If we have ref files, need to avoid it removed by trainer
     snake_case_					       =					data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          snake_case_					       =					False

     # Data collator
     # This one will take care of randomly masking the tokens.
     snake_case_					       =					DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase						,					mlm_probability=data_args.mlm_probability					)

     # Initialize our Trainer
     snake_case_					       =					Trainer(
         model=UpperCAmelCase						,					args=UpperCAmelCase						,					train_dataset=tokenized_datasets['train'] if training_args.do_train else None						,					eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None						,					tokenizer=UpperCAmelCase						,					data_collator=UpperCAmelCase						,					)

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               snake_case_					       =					last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
               snake_case_					       =					model_args.model_name_or_path
          else:
               snake_case_					       =					None
          snake_case_					       =					trainer.train(resume_from_checkpoint=UpperCAmelCase					)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          snake_case_					       =					os.path.join(training_args.output_dir						,					'train_results.txt'					)
          if trainer.is_world_process_zero():
               with open(UpperCAmelCase						,					'w'					) as writer:
                    logger.info('***** Train results *****'					)
                    for key, value in sorted(train_result.metrics.items()					):
                         logger.info(f'  {key} = {value}'					)
                         writer.write(f'{key} = {value}\n'					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir						,					'trainer_state.json'					)					)

    # Evaluation
     snake_case_					       =					{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'					)

          snake_case_					       =					trainer.evaluate()

          snake_case_					       =					math.exp(eval_output['eval_loss']					)
          snake_case_					       =					perplexity

          snake_case_					       =					os.path.join(training_args.output_dir						,					'eval_results_mlm_wwm.txt'					)
          if trainer.is_world_process_zero():
               with open(UpperCAmelCase						,					'w'					) as writer:
                    logger.info('***** Eval results *****'					)
                    for key, value in sorted(results.items()					):
                         logger.info(f'  {key} = {value}'					)
                         writer.write(f'{key} = {value}\n'					)

     return results







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      List[str]:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
    main()









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


__UpperCAmelCase								=						logging.getLogger(__name__)




def      lowercase__      (     __snake_case    :							List[Any]=2     ,       __snake_case    :							Union[str, Any]=3     ,       __snake_case    :							Any=16     ,       __snake_case    :							int = 10     ,       __snake_case    :							int = 2  ):


					'''simple docstring'''


					def get_dataset(__snake_case    :							Optional[Any]  ):
										UpperCAmelCase_    :      Optional[Any]            =						torch.randn(batch_size * n_batches     ,       1  )
										return TensorDataset(__snake_case     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1  )  )

					UpperCAmelCase_    :      Any            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      str            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					return (train_dataloader, valid_dataloader)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]
					for epoch in range(__snake_case  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						batch
															UpperCAmelCase_    :      List[Any]            =						model(__snake_case  )
															UpperCAmelCase_    :      int            =						torch.nn.functional.mse_loss(__snake_case     ,       __snake_case  )
															accelerator.backward(__snake_case  )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()  )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands



class   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self   )	->							Optional[Any]:
							super().__init__()
							UpperCAmelCase_    :      List[Any]            =						nn.Parameter(torch.randn(1   )   )
							UpperCAmelCase_    :      Optional[int]            =						nn.Parameter(torch.randn(1   )   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[Any]:
							return x * self.a + self.b





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[int]            =						ProjectConfiguration(total_limit=1				,		project_dir=_UpperCamelCase				,		automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Dict            =						Accelerator(project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						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 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[Any]            =						DummyModel()
												UpperCAmelCase_    :      str            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												UpperCAmelCase_    :      Any            =						os.path.join(_UpperCamelCase				,		'initial'   )
												accelerator.save_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												UpperCAmelCase_    :      Union[str, Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Union[str, Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Any            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      int            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[str]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												UpperCAmelCase_    :      Union[str, Any]            =						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_))    :      Optional[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Union[str, Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      Optional[int]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Any            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )

												# Train baseline
												UpperCAmelCase_    :      str            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()
												UpperCAmelCase_    :      Optional[Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Tuple            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      Any            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      str            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      List[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						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_))    :      List[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([1, 2, 3]   )
							UpperCAmelCase_    :      Any            =						torch.tensor([2, 3, 4]   )
							UpperCAmelCase_    :      Union[str, Any]            =						DummyModel()
							UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(net.parameters()   )
							UpperCAmelCase_    :      Any            =						Accelerator()
							with self.assertRaises(_UpperCamelCase   ) as ve:
												accelerator.register_for_checkpointing(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						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 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_    :      Dict            =						torch.optim.lr_scheduler.StepLR(_UpperCamelCase				,		step_size=1				,		gamma=0.99   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												UpperCAmelCase_    :      Dict            =						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 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[int]            =						DummyModel()
												UpperCAmelCase_    :      Dict            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase				,		total_limit=2   )
												# Train baseline
												UpperCAmelCase_    :      Optional[int]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						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 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
							execute_subprocess_async(_UpperCamelCase				,		env=os.environ.copy()   )


if __name__ == "__main__":
		__UpperCAmelCase								=						'/tmp/accelerate/state_checkpointing'
		__UpperCAmelCase								=						DummyModel()
		__UpperCAmelCase								=						torch.optim.Adam(params=model.parameters(), lr=1E-3)
		__UpperCAmelCase								=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
		__UpperCAmelCase							,   __UpperCAmelCase								=						dummy_dataloaders()
		__UpperCAmelCase								=						ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		__UpperCAmelCase								=						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)
		__UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		__UpperCAmelCase							,   __UpperCAmelCase								=						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:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert param_device.type == accelerator.device.type
		__UpperCAmelCase								=						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:
				__UpperCAmelCase								=						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:
				__UpperCAmelCase								=						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()






