

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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__  :      Tuple			=    16
UpperCamelCase__  :      List[str]			=    32


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Accelerator ,   _SCREAMING_SNAKE_CASE					:		int = 16       ):





    """simple docstring"""


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

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

    def collate_fn(_SCREAMING_SNAKE_CASE					:		str       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        SCREAMING_SNAKE_CASE_									=						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":
            SCREAMING_SNAKE_CASE_									=						16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_									=						8
        else:
            SCREAMING_SNAKE_CASE_									=						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.
    SCREAMING_SNAKE_CASE_									=						DataLoader(
        tokenized_datasets['train'] ,   shuffle=_SCREAMING_SNAKE_CASE ,   collate_fn=_SCREAMING_SNAKE_CASE ,   batch_size=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						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__  :      Tuple			=    mocked_dataloaders  # noqa: F811


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


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

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

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=_SCREAMING_SNAKE_CASE       )
    def inner_training_loop(_SCREAMING_SNAKE_CASE					:		Any       ):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(_SCREAMING_SNAKE_CASE       )

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        SCREAMING_SNAKE_CASE_									=						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).
        SCREAMING_SNAKE_CASE_									=						model.to(accelerator.device       )

        # Instantiate optimizer
        SCREAMING_SNAKE_CASE_									=						AdamW(params=model.parameters() ,   lr=_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						get_dataloaders(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        SCREAMING_SNAKE_CASE_									=						get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE ,   num_warmup_steps=100 ,   num_training_steps=(len(_SCREAMING_SNAKE_CASE       ) * num_epochs) ,   )

        # 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.
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						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       )
                SCREAMING_SNAKE_CASE_									=						model(**_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						outputs.loss
                accelerator.backward(_SCREAMING_SNAKE_CASE       )
                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():
                    SCREAMING_SNAKE_CASE_									=						model(**_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						outputs.logits.argmax(dim=-1       )
                SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						accelerator.gather_for_metrics((predictions, batch['labels'])       )
                metric.add_batch(
                    predictions=_SCREAMING_SNAKE_CASE ,   references=_SCREAMING_SNAKE_CASE ,   )

            SCREAMING_SNAKE_CASE_									=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:""" ,   _SCREAMING_SNAKE_CASE       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


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


if __name__ == "__main__":
       main()





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


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


UpperCamelCase__  :      Optional[Any]			=    {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__  :      List[str]			=    {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

UpperCamelCase__  :      str			=    {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}


@lru_cache()
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


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





class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	str						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	Any						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	Dict						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	List[Any]						 =  ['input_ids', 'attention_mask']

  def __init__(					self						,		_A						,		_A						,		_A="replace"						,		_A="<s>"						,		_A="</s>"						,		_A="</s>"						,		_A="<s>"						,		_A="<unk>"						,		_A="<pad>"						,		_A="<mask>"						,		_A=False						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else bos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else eos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else sep_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else cls_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else unk_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else pad_token

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

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

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

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

  @property
  def 						lowerCAmelCase__     (					self):
      return len(self.encoder)

  def 						lowerCAmelCase__     (					self):
      return dict(self.encoder						,		**self.added_tokens_encoder)

  def 						lowerCAmelCase__     (					self						,		_A):
      if token in self.cache:
          return self.cache[token]
      SCREAMING_SNAKE_CASE_									=						tuple(_A)
      SCREAMING_SNAKE_CASE_									=						get_pairs(_A)

      if not pairs:
          return token

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

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

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      for token in re.findall(self.pat						,		_A):
          SCREAMING_SNAKE_CASE_									=						''.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(_A).split(' '))
      return bpe_tokens

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.encoder.get(_A						,		self.encoder.get(self.unk_token))

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.decoder.get(_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''.join(_A)
      SCREAMING_SNAKE_CASE_									=						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'						,		errors=self.errors)
      return text

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

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

      return vocab_file, merge_file

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_A						,		token_ids_a=_A						,		already_has_special_tokens=_A)

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[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 						lowerCAmelCase__     (					self						,		_A						,		_A=False						,		**_A):
      SCREAMING_SNAKE_CASE_									=						kwargs.pop('add_prefix_space'						,		self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(_A) > 0 and not text[0].isspace()):
          SCREAMING_SNAKE_CASE_									=						' ' + text
      return (text, kwargs)


