import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)

_lowercase							:      Any      							='''▁'''

_lowercase							:      Optional[int]      							={
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase							:      Any      							={
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

_lowercase							:      Union[str, Any]      							={
    '''facebook/m2m100_418M''': 1_0_2_4,
}

# fmt: off
_lowercase							:      List[str]      							={
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				VOCAB_FILES_NAMES
  lowercase		:     Any            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase		:     Optional[Any]            =				PRETRAINED_VOCAB_FILES_MAP
  lowercase		:     str            =				["input_ids", "attention_mask"]

  lowercase		:     List[int]            =				[]
  lowercase		:     List[int]            =				[]







  def __init__(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="<s>"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="</s>"      ,	SCREAMING_SNAKE_CASE__    :					Tuple="</s>"      ,	SCREAMING_SNAKE_CASE__    :					List[str]="<pad>"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="<unk>"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="m2m100"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Dict[str, Any]] = None      ,	SCREAMING_SNAKE_CASE__    :					int=8      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	)    ->				None:
         A						:     Optional[Any]         ={} if sp_model_kwargs is None else sp_model_kwargs

         A						:     Any         =language_codes
         A						:     int         =FAIRSEQ_LANGUAGE_CODES[language_codes]
         A						:     Dict         ={lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

         A						:     Optional[int]         =kwargs.get('additional_special_tokens'      ,	[]     )
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(SCREAMING_SNAKE_CASE__     )
             for lang_code in fairseq_language_code
             if self.get_lang_token(SCREAMING_SNAKE_CASE__     ) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=SCREAMING_SNAKE_CASE__      ,	tgt_lang=SCREAMING_SNAKE_CASE__      ,	bos_token=SCREAMING_SNAKE_CASE__      ,	eos_token=SCREAMING_SNAKE_CASE__      ,	sep_token=SCREAMING_SNAKE_CASE__      ,	unk_token=SCREAMING_SNAKE_CASE__      ,	pad_token=SCREAMING_SNAKE_CASE__      ,	language_codes=SCREAMING_SNAKE_CASE__      ,	sp_model_kwargs=self.sp_model_kwargs      ,	num_madeup_words=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)

         A						:     Dict         =vocab_file
         A						:     List[str]         =load_json(SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         ={v: k for k, v in self.encoder.items()}
         A						:     Dict         =spm_file
         A						:     Any         =load_spm(SCREAMING_SNAKE_CASE__      ,	self.sp_model_kwargs     )

         A						:     Optional[Any]         =len(self.encoder     )

         A						:     List[str]         ={
             self.get_lang_token(SCREAMING_SNAKE_CASE__     ): self.encoder_size + i for i, lang_code in enumerate(SCREAMING_SNAKE_CASE__     )
         }
         A						:     int         ={lang_code: self.encoder_size + i for i, lang_code in enumerate(SCREAMING_SNAKE_CASE__     )}
         A						:     List[Any]         ={v: k for k, v in self.lang_token_to_id.items()}

         A						:     str         =src_lang if src_lang is not None else 'en'
         A						:     Optional[int]         =tgt_lang
         A						:     int         =self.get_lang_id(self._src_lang     )
         self.set_src_lang_special_tokens(self._src_lang     )

         A						:     Optional[Any]         =num_madeup_words







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				int:
         return len(self.encoder     ) + len(self.lang_token_to_id     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         return self._src_lang







  @src_lang.setter
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				None:
         A						:     Tuple         =new_src_lang
         self.set_src_lang_special_tokens(self._src_lang     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE__      ,	out_type=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Any:
         if token in self.lang_token_to_id:
                return self.lang_token_to_id[token]
         return self.encoder.get(SCREAMING_SNAKE_CASE__      ,	self.encoder[self.unk_token]     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				str:
         if index in self.id_to_lang_token:
                return self.id_to_lang_token[index]
         return self.decoder.get(SCREAMING_SNAKE_CASE__      ,	self.unk_token     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				Union[str, Any]:
         A						:     int         =[]
         A						:     Tuple         =''
         for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__     ) + token
                       A						:     Tuple         =[]
                else:
                       current_sub_tokens.append(SCREAMING_SNAKE_CASE__     )
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__     )
         return out_string.strip()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False     )    ->				List[int]:

         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=SCREAMING_SNAKE_CASE__      ,	token_ids_a=SCREAMING_SNAKE_CASE__      ,	already_has_special_tokens=SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =[1] * len(self.prefix_tokens     )
         A						:     Any         =[1] * len(self.suffix_tokens     )
         if token_ids_a is None:
                return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE__     )) + suffix_ones
         return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE__     )) + ([0] * len(SCREAMING_SNAKE_CASE__     )) + suffix_ones







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				List[int]:
         if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     List[Any]         ={self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab







  def __getstate__(				self    :					Optional[Any]     )    ->				Dict:
         A						:     str         =self.__dict__.copy()
         A						:     List[Any]         =None
         return state







  def __setstate__(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				None:
         A						:     Dict         =d

         # for backward compatibility
         if not hasattr(self      ,	'sp_model_kwargs'     ):
                A						:     List[Any]         ={}

         A						:     Any         =load_spm(self.spm_file      ,	self.sp_model_kwargs     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[str] = None     )    ->				Tuple[str]:
         A						:     int         =Path(SCREAMING_SNAKE_CASE__     )
         if not save_dir.is_dir():
                raise OSError(f'{save_directory} should be a directory'     )
         A						:     Dict         =save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         A						:     str         =save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder      ,	SCREAMING_SNAKE_CASE__     )

         if os.path.abspath(self.spm_file     ) != os.path.abspath(SCREAMING_SNAKE_CASE__     ) and os.path.isfile(self.spm_file     ):
                copyfile(self.spm_file      ,	SCREAMING_SNAKE_CASE__     )
         elif not os.path.isfile(self.spm_file     ):
                with open(SCREAMING_SNAKE_CASE__      ,	'wb'     ) as fi:
                       A						:     Union[str, Any]         =self.sp_model.serialized_model_proto()
                       fi.write(SCREAMING_SNAKE_CASE__     )

         return (str(SCREAMING_SNAKE_CASE__     ), str(SCREAMING_SNAKE_CASE__     ))







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str = "en"      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[str]] = None      ,	SCREAMING_SNAKE_CASE__    :					str = "ro"      ,	**SCREAMING_SNAKE_CASE__    :					List[str]      ,	)    ->				BatchEncoding:
         A						:     Any         =src_lang
         A						:     List[Any]         =tgt_lang
         self.set_src_lang_special_tokens(self.src_lang     )
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[str]      ,	**SCREAMING_SNAKE_CASE__    :					int     )    ->				Optional[int]:
         if src_lang is None or tgt_lang is None:
                raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'     )
         A						:     Any         =src_lang
         A						:     List[str]         =self(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =self.get_lang_id(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =tgt_lang_id
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Optional[int]:
         self.set_src_lang_special_tokens(self.src_lang     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         self.set_tgt_lang_special_tokens(self.tgt_lang     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				None:
         A						:     List[Any]         =self.get_lang_token(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =self.lang_token_to_id[lang_token]
         A						:     Dict         =[self.cur_lang_id]
         A						:     Tuple         =[self.eos_token_id]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				None:
         A						:     Optional[Any]         =self.get_lang_token(SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =self.lang_token_to_id[lang_token]
         A						:     Union[str, Any]         =[self.cur_lang_id]
         A						:     Dict         =[self.eos_token_id]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				str:
         return self.lang_code_to_token[lang]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				int:
         A						:     Tuple         =self.get_lang_token(SCREAMING_SNAKE_CASE__     )
         return self.lang_token_to_id[lang_token]







def A__  (			lowercase:     str,		lowercase:     Dict[str, Any]					)				->		sentencepiece.SentencePieceProcessor:
       A						:     str         =sentencepiece.SentencePieceProcessor(**lowercase					)
       spm.Load(str(lowercase					)					)
       return spm



def A__  (			lowercase:     str					)				->		Union[Dict, List]:
       with open(lowercase,		'r'					) as f:
              return json.load(lowercase					)



def A__  (			lowercase:     List[str],		lowercase:     str					)				->		None:
       with open(lowercase,		'w'					) as f:
              json.dump(lowercase,		lowercase,		indent=2					)







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=lowercase,		max_length=lowercase					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       for step, batch in enumerate(lowercase					):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device					)
              with torch.no_grad():
                     A						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A						:     int         =config['lr']
       A						:     Optional[Any]         =int(config['num_epochs']					)
       A						:     Union[str, Any]         =int(config['seed']					)
       A						:     List[str]         =int(config['batch_size']					)
       A						:     Optional[Any]         =args.model_name_or_path

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		warmup_num_steps=0					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     Tuple         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     List[str]         =0
       A						:     Tuple         =evaluate.load('glue',		'mrpc'					)
       A						:     Optional[int]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     Union[str, Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






