





'''simple docstring'''






import argparse

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)
#
# 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
#
########################################################################


__lowerCamelCase						: Optional[int]		=   16
__lowerCamelCase						: Tuple		=   32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16   ):


  """simple docstring"""

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

  def tokenize_function(__SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase       			=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():
    _UpperCamelCase       			=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
  _UpperCamelCase       			=tokenized_datasets.rename_column('''label'''  ,      '''labels'''   )

  def collate_fn(__SCREAMING_SNAKE_CASE   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCamelCase       			=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":
      _UpperCamelCase       			=16
    elif accelerator.mixed_precision != "no":
      _UpperCamelCase       			=8
    else:
      _UpperCamelCase       			=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.
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      drop_last=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      drop_last=(accelerator.mixed_precision == '''fp8''')  ,      )

  return train_dataloader, eval_dataloader


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=Accelerator(cpu=args.cpu  ,      mixed_precision=args.mixed_precision   )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase       			=config['''lr''']
  _UpperCamelCase       			=int(config['''num_epochs''']   )
  _UpperCamelCase       			=int(config['''seed''']   )
  _UpperCamelCase       			=int(config['''batch_size''']   )

  _UpperCamelCase       			=evaluate.load('''glue'''  ,      '''mrpc'''   )

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

  set_seed(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase       			=get_dataloaders(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase       			=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).
  _UpperCamelCase       			=model.to(accelerator.device   )
  # Instantiate optimizer
  _UpperCamelCase       			=AdamW(params=model.parameters()  ,      lr=__SCREAMING_SNAKE_CASE   )

  # Instantiate scheduler
  _UpperCamelCase       			=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.

  _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=accelerator.prepare(
      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE   ):
    model.train()
    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   )
      _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.loss
      _UpperCamelCase       			=loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE   )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    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   )
      with torch.no_grad():
        _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.logits.argmax(dim=-1   )
      _UpperCamelCase			,     _UpperCamelCase       			=accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
      metric.add_batch(
          predictions=__SCREAMING_SNAKE_CASE  ,      references=__SCREAMING_SNAKE_CASE  ,      )

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


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=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.'''   )
  _UpperCamelCase       			=parser.parse_args()
  _UpperCamelCase       			={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       main()













'''simple docstring'''






import json
import os
from functools import lru_cache
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

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


__lowerCamelCase						: int		=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCamelCase						: Any		=   {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__lowerCamelCase						: Dict		=   {'facebook/blenderbot-3B': 128}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 					_a   ():


  """simple docstring"""

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


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]


  def __init__(				self    :					str							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[Any]="replace"							,	UpperCamelCase__    :					Dict="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Union[str, Any]="</s>"							,	UpperCamelCase__    :					Tuple="<s>"							,	UpperCamelCase__    :					int="<unk>"							,	UpperCamelCase__    :					int="<pad>"							,	UpperCamelCase__    :					Optional[int]="<mask>"							,	UpperCamelCase__    :					List[str]=False							,	**UpperCamelCase__    :					Tuple							,	)     ->						Optional[int]:
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else bos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else eos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else sep_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else cls_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else unk_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

    super().__init__(
        errors=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	add_prefix_space=UpperCamelCase__							,	**UpperCamelCase__							,	)

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

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    _UpperCamelCase       			=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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[Any]:
    return len(self.encoder  )


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


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[str]  )     ->						Dict:
    if token in self.cache:
      return self.cache[token]
    _UpperCamelCase       			=tuple(UpperCamelCase__  )
    _UpperCamelCase       			=get_pairs(UpperCamelCase__  )

    if not pairs:
      return token

    while True:
      _UpperCamelCase       			=min(UpperCamelCase__							,	key=lambda UpperCamelCase__  : self.bpe_ranks.get(UpperCamelCase__							,	float('''inf'''  )  )  )
      if bigram not in self.bpe_ranks:
        break
      _UpperCamelCase			,     _UpperCamelCase       			=bigram
      _UpperCamelCase       			=[]
      _UpperCamelCase       			=0
      while i < len(UpperCamelCase__  ):
        try:
          _UpperCamelCase       			=word.index(UpperCamelCase__							,	UpperCamelCase__  )
        except ValueError:
          new_word.extend(word[i:]  )
          break
        else:
          new_word.extend(word[i:j]  )
          _UpperCamelCase       			=j

        if word[i] == first and i < len(UpperCamelCase__  ) - 1 and word[i + 1] == second:
          new_word.append(first + second  )
          i += 2
        else:
          new_word.append(word[i]  )
          i += 1
      _UpperCamelCase       			=tuple(UpperCamelCase__  )
      _UpperCamelCase       			=new_word
      if len(UpperCamelCase__  ) == 1:
        break
      else:
        _UpperCamelCase       			=get_pairs(UpperCamelCase__  )
    _UpperCamelCase       			=''' '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=word
    return word


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=[]
    for token in re.findall(self.pat							,	UpperCamelCase__  ):
      _UpperCamelCase       			=''''''.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(UpperCamelCase__  ).split(''' '''  )  )
    return bpe_tokens


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Optional[int]  )     ->						int:
    return self.encoder.get(UpperCamelCase__							,	self.encoder.get(self.unk_token  )  )


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Optional[Any]  )     ->						List[str]:
    return self.decoder.get(UpperCamelCase__  )


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


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

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

    _UpperCamelCase       			=0
    with open(UpperCamelCase__							,	'''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 UpperCamelCase__  : 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!'''  )
          _UpperCamelCase       			=token_index
        writer.write(''' '''.join(UpperCamelCase__  ) + '''\n'''  )
        index += 1

    return vocab_file, merge_file


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

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


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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 + sep + token_ids_a + sep  ) * [0]


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str=False							,	**UpperCamelCase__    :					Union[str, Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=kwargs.pop('''add_prefix_space'''							,	self.add_prefix_space  )
    if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__  ) > 0 and not text[0].isspace()):
      _UpperCamelCase       			=''' ''' + text
    return (text, kwargs)


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						Any:
    return token_ids_a + [self.eos_token_id]




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					"Conversation"  )     ->						List[int]:
    _UpperCamelCase       			=[]
    for is_user, text in conversation.iter_texts():
      if is_user:
        # We need to space prefix as it's being done within blenderbot
        inputs.append(''' ''' + text  )
      else:
        # Generated responses should contain them already.
        inputs.append(UpperCamelCase__  )

    _UpperCamelCase       			='''  '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=self.encode(UpperCamelCase__  )
    if len(UpperCamelCase__  ) > self.model_max_length:
      _UpperCamelCase       			=input_ids[-self.model_max_length :]
      logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
    return input_ids






