





'''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,
# 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
#
########################################################################

snake_case_     			=						16
snake_case_     			=						32
def 				_lowerCamelCase(	UpperCamelCase__     :			Accelerator     ,							UpperCamelCase__     :			int = 16		)		->	Optional[int]:
    A							:							Any				=    AutoTokenizer.from_pretrained('''bert-base-cased'''		)
    A							:							Tuple				=    load_dataset('''glue'''     ,							'''mrpc'''		)

    def tokenize_function(UpperCamelCase__     :			str		):
        # max_length=None => use the model max length (it's actually the default)
        A							:							Optional[int]				=    tokenizer(examples['''sentence1''']     ,							examples['''sentence2''']     ,							truncation=UpperCamelCase__     ,							max_length=UpperCamelCase__		)
        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							:							Optional[int]				=    datasets.map(
            UpperCamelCase__     ,							batched=UpperCamelCase__     ,							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							:							Union[str, Any]				=    tokenized_datasets.rename_column('''label'''     ,							'''labels'''		)

    def collate_fn(UpperCamelCase__     :			str		):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A							:							Tuple				=    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":
            A							:							Dict				=    16
        elif accelerator.mixed_precision != "no":
            A							:							List[str]				=    8
        else:
            A							:							Optional[int]				=    None

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

    # Instantiate dataloaders.
    A							:							Tuple				=    DataLoader(
        tokenized_datasets['''train''']     ,							shuffle=UpperCamelCase__     ,							collate_fn=UpperCamelCase__     ,							batch_size=UpperCamelCase__		)
    A							:							Dict				=    DataLoader(
        tokenized_datasets['''validation''']     ,							shuffle=UpperCamelCase__     ,							collate_fn=UpperCamelCase__     ,							batch_size=UpperCamelCase__		)

    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

     snake_case_     			=						mocked_dataloaders  # noqa: F811
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			int		)		->	List[str]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,							UpperCamelCase__		) == "1":
        A							:							Optional[Any]				=    2
    # Initialize accelerator
    A							:							int				=    Accelerator(cpu=args.cpu     ,							mixed_precision=args.mixed_precision		)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A							:							List[Any]				=    config['''lr''']
    A							:							Dict				=    int(config['''num_epochs''']		)
    A							:							List[str]				=    int(config['''seed''']		)
    A							:							List[Any]				=    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":
            A							:							str				=    args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            A							:							Union[str, Any]				=    int(args.checkpointing_steps		)
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''		)
    else:
        A							:							str				=    None

    set_seed(UpperCamelCase__		)

    A,						A							:							Optional[int]				=    get_dataloaders(UpperCamelCase__     ,							UpperCamelCase__		)
    A							:							str				=    evaluate.load('''glue'''     ,							'''mrpc'''		)

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

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

    # 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							:							Optional[Any]				=    model.to(accelerator.device		)

    # Instantiate optimizer
    A							:							List[Any]				=    AdamW(params=model.parameters()     ,							lr=UpperCamelCase__		)

    # Instantiate scheduler
    A							:							Union[str, Any]				=    get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__     ,							num_warmup_steps=100     ,							num_training_steps=(len(UpperCamelCase__		) * 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							:							Optional[Any]				=    accelerator.prepare(
        UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)

    # New Code #
    # 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							:							List[Any]				=    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		)
            A							:							Optional[Any]				=    os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            A							:							str				=    [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            A							:							Dict				=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        A							:							Dict				=    os.path.splitext(UpperCamelCase__		)[0]

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

    # Now we train the model
    for epoch in range(UpperCamelCase__     ,							UpperCamelCase__		):
        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
            A							:							str				=    accelerator.skip_first_batches(UpperCamelCase__     ,							UpperCamelCase__		)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            A							:							Optional[Any]				=    train_dataloader
        for step, batch in enumerate(UpperCamelCase__		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            A							:							str				=    model(**UpperCamelCase__		)
            A							:							Optional[int]				=    outputs.loss
            A							:							Optional[Any]				=    loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase__		)
            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(UpperCamelCase__     ,							UpperCamelCase__		):
                A							:							Dict				=    F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        A							:							Optional[Any]				=    os.path.join(args.output_dir     ,							UpperCamelCase__		)
                    accelerator.save_state(UpperCamelCase__		)

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

        A							:							int				=    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''     ,							UpperCamelCase__		)

        # 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":
            A							:							Optional[int]				=    F'''epoch_{epoch}'''
            if args.output_dir is not None:
                A							:							Union[str, Any]				=    os.path.join(args.output_dir     ,							UpperCamelCase__		)
            accelerator.save_state(UpperCamelCase__		)
def 				_lowerCamelCase(	)		->	str:
    A							:							List[str]				=    argparse.ArgumentParser(description='''Simple example of training script.'''		)
    parser.add_argument(
        '''--mixed_precision'''     ,							type=UpperCamelCase__     ,							default=UpperCamelCase__     ,							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=UpperCamelCase__     ,							default=UpperCamelCase__     ,							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=UpperCamelCase__     ,							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=UpperCamelCase__     ,							default=UpperCamelCase__     ,							help='''If the training should continue from a checkpoint folder.'''     ,							)
    A							:							int				=    parser.parse_args()
    A							:							Tuple				=    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__     ,							UpperCamelCase__		)


if __name__ == "__main__":
     main()







'''simple docstring'''






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


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    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
       A							:							Optional[int]				=    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:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

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


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


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


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

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


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.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        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


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

       A							:							Tuple				=    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!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							Union[str, Any]				=    [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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    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()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
