



"""simple docstring"""




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

_UpperCamelCase:					Optional[int]     				=	logging.get_logger(__name__)

_UpperCamelCase:					Dict     				=	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

_UpperCamelCase:					List[str]     				=	{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

_UpperCamelCase:					Optional[int]     				=	{
    'microsoft/deberta-base': 5_1_2,
    'microsoft/deberta-large': 5_1_2,
    'microsoft/deberta-xlarge': 5_1_2,
    'microsoft/deberta-base-mnli': 5_1_2,
    'microsoft/deberta-large-mnli': 5_1_2,
    'microsoft/deberta-xlarge-mnli': 5_1_2,
}

_UpperCamelCase:					List[Any]     				=	{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 	lowercase__  (      )  ->      int:






      '''simple docstring'''


      lowercase	:   List[Any]											= (
          list(range(ord('!'					)						,   ord('~'					) + 1					)					) + list(range(ord('¡'					)						,   ord('¬'					) + 1					)					) + list(range(ord('®'					)						,   ord('ÿ'					) + 1					)					)
      )
      lowercase	:   Optional[Any]											= bs[:]
      lowercase	:   List[str]											= 0
      for b in range(2**8					):
            if b not in bs:
                  bs.append(__SCREAMING_SNAKE_CASE					)
                  cs.append(2**8 + n					)
                  n += 1
      lowercase	:   Tuple											= [chr(__SCREAMING_SNAKE_CASE					) for n in cs]
      return dict(zip(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE					)					)


def 	lowercase__  (      _UpperCAmelCase					)  ->      Union[str, Any]:






      '''simple docstring'''


      lowercase	:   str											= set()
      lowercase	:   Dict											= word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)					)
            lowercase	:   int											= char
      return pairs





class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					VOCAB_FILES_NAMES
   _lowerCamelCase       				=					PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase       				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase       				=					['input_ids', 'attention_mask', 'token_type_ids']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				str,  lowerCAmelCase   :				str="replace",  lowerCAmelCase   :				Any="[CLS]",  lowerCAmelCase   :				Tuple="[SEP]",  lowerCAmelCase   :				str="[SEP]",  lowerCAmelCase   :				int="[CLS]",  lowerCAmelCase   :				int="[UNK]",  lowerCAmelCase   :				List[Any]="[PAD]",  lowerCAmelCase   :				List[Any]="[MASK]",  lowerCAmelCase   :				Tuple=False,  lowerCAmelCase   :				List[Any]=False,  **lowerCAmelCase   :				Union[str, Any],  )     ->							Union[str, Any]:
         lowercase	:   Dict											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else bos_token
         lowercase	:   Dict											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else eos_token
         lowercase	:   List[Any]											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else sep_token
         lowercase	:   str											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else cls_token
         lowercase	:   Tuple											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else unk_token
         lowercase	:   Optional[Any]											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowercase	:   Union[str, Any]											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else mask_token

         super().__init__(
             errors=_lowerCAmelCase,  bos_token=_lowerCAmelCase,  eos_token=_lowerCAmelCase,  unk_token=_lowerCAmelCase,  sep_token=_lowerCAmelCase,  cls_token=_lowerCAmelCase,  pad_token=_lowerCAmelCase,  mask_token=_lowerCAmelCase,  add_prefix_space=_lowerCAmelCase,  add_bos_token=_lowerCAmelCase,  **_lowerCAmelCase,  )
         lowercase	:   Tuple											= add_bos_token

         with open(_lowerCAmelCase,  encoding='utf-8'      ) as vocab_handle:
               lowercase	:   List[str]											= json.load(_lowerCAmelCase      )
         lowercase	:   Any											= {v: k for k, v in self.encoder.items()}
         lowercase	:   List[Any]											= errors  # how to handle errors in decoding
         lowercase	:   str											= bytes_to_unicode()
         lowercase	:   Union[str, Any]											= {v: k for k, v in self.byte_encoder.items()}
         with open(_lowerCAmelCase,  encoding='utf-8'      ) as merges_handle:
               lowercase	:   Optional[int]											= merges_handle.read().split('\n'      )[1:-1]
         lowercase	:   Optional[Any]											= [tuple(merge.split()      ) for merge in bpe_merges]
         lowercase	:   List[Any]											= dict(zip(_lowerCAmelCase,  range(len(_lowerCAmelCase      )      )      )      )
         lowercase	:   int											= {}
         lowercase	:   int											= add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowercase	:   Union[str, Any]											= re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'      )


   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def 							lowercase					(				self   :				Tuple      )     ->							Optional[Any]:
         return len(self.encoder      )


   def 							lowercase					(				self   :				int      )     ->							Optional[Any]:
         return dict(self.encoder,  **self.added_tokens_encoder      )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Dict      )     ->							int:
         if token in self.cache:
               return self.cache[token]
         lowercase	:   Union[str, Any]											= tuple(_lowerCAmelCase      )
         lowercase	:   List[str]											= get_pairs(_lowerCAmelCase      )

         if not pairs:
               return token

         while True:
               lowercase	:   Tuple											= min(_lowerCAmelCase,  key=lambda lowerCAmelCase      : self.bpe_ranks.get(_lowerCAmelCase,  float('inf'      )      )      )
               if bigram not in self.bpe_ranks:
                     break
               lowercase						,     lowercase	:   Tuple											= bigram
               lowercase	:   Optional[int]											= []
               lowercase	:   Dict											= 0
               while i < len(_lowerCAmelCase      ):
                     try:
                           lowercase	:   List[Any]											= word.index(_lowerCAmelCase,  _lowerCAmelCase      )
                     except ValueError:
                           new_word.extend(word[i:]      )
                           break
                     else:
                           new_word.extend(word[i:j]      )
                           lowercase	:   List[str]											= j

                     if word[i] == first and i < len(_lowerCAmelCase      ) - 1 and word[i + 1] == second:
                           new_word.append(first + second      )
                           i += 2
                     else:
                           new_word.append(word[i]      )
                           i += 1
               lowercase	:   Any											= tuple(_lowerCAmelCase      )
               lowercase	:   str											= new_word
               if len(_lowerCAmelCase      ) == 1:
                     break
               else:
                     lowercase	:   List[str]											= get_pairs(_lowerCAmelCase      )
         lowercase	:   Dict											= ' '.join(_lowerCAmelCase      )
         lowercase	:   Any											= word
         return word


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				List[int],  lowerCAmelCase   :				Optional[List[int]] = None      )     ->							List[int]:
         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase	:   Tuple											= [self.cls_token_id]
         lowercase	:   int											= [self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				List[int],  lowerCAmelCase   :				Optional[List[int]] = None,  lowerCAmelCase   :				bool = False      )     ->							List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowerCAmelCase,  token_ids_a=_lowerCAmelCase,  already_has_special_tokens=_lowerCAmelCase      )

         if token_ids_a is None:
               return [1] + ([0] * len(_lowerCAmelCase      )) + [1]
         return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				List[int],  lowerCAmelCase   :				Optional[List[int]] = None      )     ->							List[int]:
         lowercase	:   Optional[int]											= [self.sep_token_id]
         lowercase	:   Dict											= [self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
         return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				Optional[Any]      )     ->							List[Any]:
         lowercase	:   str											= []
         for token in re.findall(self.pat,  _lowerCAmelCase      ):
               lowercase	:   Optional[int]											= ''.join(
                   self.byte_encoder[b] for b in token.encode('utf-8'      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase      ).split(' '      )      )
         return bpe_tokens


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				str      )     ->							str:
         return self.encoder.get(_lowerCAmelCase,  self.encoder.get(self.unk_token      )      )


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				str      )     ->							Optional[Any]:
         return self.decoder.get(_lowerCAmelCase      )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Optional[int]      )     ->							Any:
         lowercase	:   Optional[int]											= ''.join(_lowerCAmelCase      )
         lowercase	:   Dict											= bytearray([self.byte_decoder[c] for c in text]      ).decode('utf-8',  errors=self.errors      )
         return text


   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				str,  lowerCAmelCase   :				Optional[str] = None      )     ->							Tuple[str]:
         if not os.path.isdir(_lowerCAmelCase      ):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
               return
         lowercase	:   int											= os.path.join(
             _lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
         lowercase	:   int											= os.path.join(
             _lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

         with open(_lowerCAmelCase,  'w',  encoding='utf-8'      ) as f:
               f.write(json.dumps(self.encoder,  indent=2,  sort_keys=_lowerCAmelCase,  ensure_ascii=_lowerCAmelCase      ) + '\n'      )

         lowercase	:   Tuple											= 0
         with open(_lowerCAmelCase,  'w',  encoding='utf-8'      ) as writer:
               writer.write('#version: 0.2\n'      )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),  key=lambda lowerCAmelCase      : kv[1]      ):
                     if index != token_index:
                           logger.warning(
                               f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                               ' Please check that the tokenizer is not corrupted!'      )
                           lowercase	:   int											= token_index
                     writer.write(' '.join(_lowerCAmelCase      ) + '\n'      )
                     index += 1

         return vocab_file, merge_file


   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Tuple=False,  **lowerCAmelCase   :				Union[str, Any]      )     ->							Dict:
         lowercase	:   List[str]											= kwargs.pop('add_prefix_space',  self.add_prefix_space      )
         if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase      ) > 0 and not text[0].isspace()):
               lowercase	:   Dict											= ' ' + text
         return (text, kwargs)






   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				"Conversation"      )     ->							List[int]:
         lowercase	:   Optional[Any]											= []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_lowerCAmelCase,  add_special_tokens=_lowerCAmelCase      ) + [self.eos_token_id]      )
         if len(_lowerCAmelCase      ) > self.model_max_length:
               lowercase	:   Any											= input_ids[-self.model_max_length :]
         return input_ids


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCamelCase:					Dict     =16
_UpperCamelCase:					Optional[int]     =32



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Accelerator		,				__SCREAMING_SNAKE_CASE					:  int = 16    ):






  """simple docstring"""




  _lowerCAmelCase						=		AutoTokenizer.from_pretrained('bert-base-cased'    )
  _lowerCAmelCase						=		load_dataset('glue'		,				'mrpc'    )

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

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _lowerCAmelCase						=		datasets.map(
        __SCREAMING_SNAKE_CASE		,				batched=__SCREAMING_SNAKE_CASE		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				)

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

  def collate_fn(__SCREAMING_SNAKE_CASE					:  Any    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowerCAmelCase						=		128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCAmelCase						=		16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase						=		8
    else:
      _lowerCAmelCase						=		None

    return tokenizer.pad(
        __SCREAMING_SNAKE_CASE		,				padding='longest'		,				max_length=__SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE		,				return_tensors='pt'		,				)

  # Instantiate dataloaders.
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['train']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['validation']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       _UpperCamelCase:					int     =mocked_dataloaders  # noqa: F811



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				__SCREAMING_SNAKE_CASE    ) == "1":
    _lowerCAmelCase						=		2
  # Initialize accelerator
  _lowerCAmelCase						=		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						=		config['lr']
  _lowerCAmelCase						=		int(config['num_epochs']    )
  _lowerCAmelCase						=		int(config['seed']    )
  _lowerCAmelCase						=		int(config['batch_size']    )

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps		,				'isdigit'    ):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase						=		args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						=		int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
  else:
    _lowerCAmelCase						=		None

  set_seed(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase	,       _lowerCAmelCase						=		get_dataloaders(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		evaluate.load('glue'		,				'mrpc'    )

  # If the batch size is too big we use gradient accumulation
  _lowerCAmelCase						=		1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _lowerCAmelCase						=		batch_size // MAX_GPU_BATCH_SIZE
    _lowerCAmelCase						=		MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase						=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,				return_dict=__SCREAMING_SNAKE_CASE    )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _lowerCAmelCase						=		model.to(accelerator.device    )

  # Instantiate optimizer
  _lowerCAmelCase						=		AdamW(params=model.parameters()		,				lr=__SCREAMING_SNAKE_CASE    )

  # Instantiate scheduler
  _lowerCAmelCase						=		get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE		,				num_warmup_steps=100		,				num_training_steps=(len(__SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps		,				)

  # 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.
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  _lowerCAmelCase						=		0
  # We also need to keep track of the stating epoch so files are named properly
  _lowerCAmelCase						=		0

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
      accelerator.load_state(args.resume_from_checkpoint    )
      _lowerCAmelCase						=		os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						=		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      _lowerCAmelCase						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						=		os.path.splitext(__SCREAMING_SNAKE_CASE    )[0]

    if "epoch" in training_difference:
      _lowerCAmelCase						=		int(training_difference.replace('epoch_'		,				''    )    ) + 1
      _lowerCAmelCase						=		None
    else:
      _lowerCAmelCase						=		int(training_difference.replace('step_'		,				''    )    )
      _lowerCAmelCase						=		resume_step // len(__SCREAMING_SNAKE_CASE    )
      resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE    )

    # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    model.train()
    # New Code #
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _lowerCAmelCase						=		accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						=		train_dataloader
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.loss
      _lowerCAmelCase						=		loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
        _lowerCAmelCase						=		f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
          accelerator.save_state(__SCREAMING_SNAKE_CASE    )

    model.eval()
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
      batch.to(accelerator.device    )
      with torch.no_grad():
        _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.logits.argmax(dim=-1    )
      _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.gather_for_metrics((predictions, batch['labels'])    )
      metric.add_batch(
          predictions=__SCREAMING_SNAKE_CASE		,				references=__SCREAMING_SNAKE_CASE		,				)

    _lowerCAmelCase						=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''		,				__SCREAMING_SNAKE_CASE    )

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						=		f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
      accelerator.save_state(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				choices=['no', 'fp16', 'bf16', 'fp8']		,				help='Whether to use mixed precision. Choose'
      'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
      'and an Nvidia Ampere GPU.'		,				)
  parser.add_argument('--cpu'		,				action='store_true'		,				help='If passed, will train on the CPU.'    )
  parser.add_argument(
      '--checkpointing_steps'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		,				)
  parser.add_argument(
      '--output_dir'		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='If the training should continue from a checkpoint folder.'		,				)
  _lowerCAmelCase						=		parser.parse_args()
  _lowerCAmelCase						=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()
