



'''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__    :     int			  =   {
    '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__    :     int			  =   {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

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



def     UpperCAmelCase     (    )   ->       Optional[Any]:



   """simple docstring"""






   A_  :		Dict						=					(
       list(range(ord("""!"""		)      ,		ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)      ,		ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)      ,		ord("""ÿ"""		) + 1		)		)
   )
   A_  :		List[str]						=					bs[:]
   A_  :		Optional[int]						=					0
   for b in range(2**8		):
      if b not in bs:
         bs.append(a_		)
         cs.append(2**8 + n		)
         n += 1
   A_  :		List[str]						=					[chr(a_		) for n in cs]
   return dict(zip(a_      ,		a_		)		)



def     UpperCAmelCase     (    a_		)   ->       List[Any]:



   """simple docstring"""






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







class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 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					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase="replace"					,    _lowerCamelCase="[CLS]"					,    _lowerCamelCase="[SEP]"					,    _lowerCamelCase="[SEP]"					,    _lowerCamelCase="[CLS]"					,    _lowerCamelCase="[UNK]"					,    _lowerCamelCase="[PAD]"					,    _lowerCamelCase="[MASK]"					,    _lowerCamelCase=False					,    _lowerCamelCase=False					,    **_lowerCamelCase					,    )    ->    Tuple:
    A_  :		List[Any]						=					AddedToken(_lowerCamelCase					,    lstrip=_lowerCamelCase					,    rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else bos_token
    A_  :		List[Any]						=					AddedToken(_lowerCamelCase					,    lstrip=_lowerCamelCase					,    rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else eos_token
    A_  :		Optional[Any]						=					AddedToken(_lowerCamelCase					,    lstrip=_lowerCamelCase					,    rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else sep_token
    A_  :		List[str]						=					AddedToken(_lowerCamelCase					,    lstrip=_lowerCamelCase					,    rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else cls_token
    A_  :		Any						=					AddedToken(_lowerCamelCase					,    lstrip=_lowerCamelCase					,    rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else unk_token
    A_  :		List[str]						=					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
    A_  :		int						=					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					,    )
    A_  :		Any						=					add_bos_token

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

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    A_  :		Tuple						=					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       UpperCAmelCase_       (      self  )    ->    int:
    return len(self.encoder  )







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







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    str:
    if token in self.cache:
       return self.cache[token]
    A_  :		Optional[int]						=					tuple(_lowerCamelCase  )
    A_  :		Optional[Any]						=					get_pairs(_lowerCamelCase  )

    if not pairs:
       return token

    while True:
       A_  :		Union[str, Any]						=					min(_lowerCamelCase					,    key=lambda _lowerCamelCase  : self.bpe_ranks.get(_lowerCamelCase					,    float("""inf"""  )  )  )
       if bigram not in self.bpe_ranks:
          break
       A_						,  A_  :		List[Any]						=					bigram
       A_  :		List[str]						=					[]
       A_  :		List[Any]						=					0
       while i < len(_lowerCamelCase  ):
          try:
             A_  :		int						=					word.index(_lowerCamelCase					,    _lowerCamelCase  )
          except ValueError:
             new_word.extend(word[i:]  )
             break
          else:
             new_word.extend(word[i:j]  )
             A_  :		List[Any]						=					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
       A_  :		Any						=					tuple(_lowerCamelCase  )
       A_  :		List[str]						=					new_word
       if len(_lowerCamelCase  ) == 1:
          break
       else:
          A_  :		str						=					get_pairs(_lowerCamelCase  )
    A_  :		List[str]						=					""" """.join(_lowerCamelCase  )
    A_  :		Dict						=					word
    return word







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None  )    ->    List[int]:
    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    A_  :		List[str]						=					[self.cls_token_id]
    A_  :		Any						=					[self.sep_token_id]
    return cls + token_ids_a + sep + token_ids_a + sep







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = 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       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None  )    ->    List[int]:
    A_  :		Optional[int]						=					[self.sep_token_id]
    A_  :		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       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Any						=					[]
    for token in re.findall(self.pat					,    _lowerCamelCase  ):
       A_  :		Dict						=					"""""".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       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    return self.encoder.get(_lowerCamelCase					,    self.encoder.get(self.unk_token  )  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Any:
    return self.decoder.get(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    A_  :		Tuple						=					"""""".join(_lowerCamelCase  )
    A_  :		Any						=					bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8"""					,    errors=self.errors  )
    return text







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None  )    ->    Tuple[str]:
    if not os.path.isdir(_lowerCamelCase  ):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
       return
    A_  :		Union[str, Any]						=					os.path.join(
        _lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
    A_  :		Optional[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"""  )

    A_  :		Optional[int]						=					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!"""  )
             A_  :		str						=					token_index
          writer.write(""" """.join(_lowerCamelCase  ) + """\n"""  )
          index += 1

    return vocab_file, merge_file







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False					,    **_lowerCamelCase  )    ->    str:
    A_  :		Optional[int]						=					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()):
       A_  :		Tuple						=					""" """ + text
    return (text, kwargs)







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[int]:
    A_  :		Union[str, 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:
       A_  :		Any						=					input_ids[-self.model_max_length :]
    return input_ids





'''simple docstring'''





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

   if hasattr(args.checkpointing_steps      ,		"""isdigit"""		):
      if args.checkpointing_steps == "epoch":
         A_  :		Tuple						=					args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_  :		List[str]						=					int(args.checkpointing_steps		)
      else:
         raise ValueError(
             F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
   else:
      A_  :		Optional[Any]						=					None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_  :		str						=					config["""lr"""]
   A_  :		List[str]						=					int(config["""num_epochs"""]		)
   A_  :		Dict						=					int(config["""seed"""]		)
   A_  :		str						=					int(config["""batch_size"""]		)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      A_  :		str						=					os.path.split(a_		)[-1].split("""."""		)[0]
      accelerator.init_trackers(a_      ,		a_		)

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

   def tokenize_function(a_		):
      # max_length=None => use the model max length (it's actually the default)
      A_  :		Tuple						=					tokenizer(examples["""sentence1"""]      ,		examples["""sentence2"""]      ,		truncation=a_      ,		max_length=a_		)
      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():
      A_  :		Dict						=					datasets.map(
          a_      ,		batched=a_      ,		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
   A_  :		int						=					tokenized_datasets.rename_column("""label"""      ,		"""labels"""		)

   # If the batch size is too big we use gradient accumulation
   A_  :		Optional[Any]						=					1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_  :		List[str]						=					batch_size // MAX_GPU_BATCH_SIZE
      A_  :		List[str]						=					MAX_GPU_BATCH_SIZE

   def collate_fn(a_		):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_  :		List[Any]						=					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

      return tokenizer.pad(
          a_      ,		padding="""longest"""      ,		max_length=a_      ,		pad_to_multiple_of=a_      ,		return_tensors="""pt"""      ,		)

   # Instantiate dataloaders.
   A_  :		str						=					DataLoader(
       tokenized_datasets["""train"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)
   A_  :		Any						=					DataLoader(
       tokenized_datasets["""validation"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)

   set_seed(a_		)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_  :		List[Any]						=					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,		return_dict=a_		)

   # 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).
   A_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		num_training_steps=(len(a_		) * 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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					0

   # Potentially load in the weights and states from a previous save
   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		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		Optional[int]						=					resume_step // len(a_		)
         resume_step -= starting_epoch * len(a_		)

    # Now we train the model
   for epoch in range(a_      ,		a_		):
      model.train()
      if args.with_tracking:
         A_  :		List[str]						=					0
      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
         A_  :		List[Any]						=					accelerator.skip_first_batches(a_      ,		a_		)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         A_  :		Tuple						=					train_dataloader
      for step, batch in enumerate(a_		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(a_		)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(a_      ,		a_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
               accelerator.save_state(a_		)

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

      A_  :		Union[str, Any]						=					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"      ,		a_		)
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(a_		),
                 """epoch""": epoch,
             }      ,		step=a_      ,		)

      if checkpointing_steps == "epoch":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   A_  :		List[Any]						=					argparse.ArgumentParser(description="""Simple example of training script."""		)
   parser.add_argument(
       """--mixed_precision"""      ,		type=a_      ,		default=a_      ,		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=a_      ,		default=a_      ,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,		)
   parser.add_argument(
       """--resume_from_checkpoint"""      ,		type=a_      ,		default=a_      ,		help="""If the training should continue from a checkpoint folder."""      ,		)
   parser.add_argument(
       """--with_tracking"""      ,		action="""store_true"""      ,		help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,		)
   parser.add_argument(
       """--output_dir"""      ,		type=a_      ,		default="""."""      ,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,		)
   parser.add_argument(
       """--project_dir"""      ,		type=a_      ,		default="""logs"""      ,		help="""Location on where to store experiment tracking logs` and relevent project information"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
