


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      snake_case__				:	Optional[Any]					       =					None

snake_case__				:	int					       =					logging.get_logger(__name__)
snake_case__				:	Optional[Any]					       =					{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case__				:	List[Any]					       =					{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

snake_case__				:	str					       =					{
    'google/rembert': 256,
}

snake_case__				:	List[str]					       =					'▁'





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							VOCAB_FILES_NAMES
       _a   							=							PRETRAINED_VOCAB_FILES_MAP
       _a   							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a   							=							RemBertTokenizer



       def __init__(						self				:				Any		,				__a				:				Optional[Any]=None		,				__a				:				List[Any]=None		,				__a				:				Dict=True		,				__a				:				Dict=True		,				__a				:				Union[str, Any]=False		,				__a				:				Optional[Any]="[CLS]"		,				__a				:				int="[SEP]"		,				__a				:				List[str]="<unk>"		,				__a				:				Dict="[SEP]"		,				__a				:				Any="<pad>"		,				__a				:				Any="[CLS]"		,				__a				:				Optional[int]="[MASK]"		,				**__a				:				Dict		,				)					->List[Any]:
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase_						:   Optional[int]		= AddedToken(__a		,				lstrip=__a		,				rstrip=__a							) if isinstance(__a		,				__a							) else mask_token

          super().__init__(
              __a		,				tokenizer_file=__a		,				do_lower_case=__a		,				remove_space=__a		,				keep_accents=__a		,				bos_token=__a		,				eos_token=__a		,				unk_token=__a		,				sep_token=__a		,				pad_token=__a		,				cls_token=__a		,				mask_token=__a		,				**__a		,				)

          lowerCamelCase_						:   List[str]		= do_lower_case
          lowerCamelCase_						:   Dict		= remove_space
          lowerCamelCase_						:   Optional[Any]		= keep_accents
          lowerCamelCase_						:   int		= vocab_file
          lowerCamelCase_						:   List[str]		= False if not self.vocab_file else True



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				List[int]		,				__a				:				Optional[List[int]] = None							)					->List[int]:
          lowerCamelCase_						:   Dict		= [self.sep_token_id]
          lowerCamelCase_						:   List[Any]		= [self.cls_token_id]
          if token_ids_a is None:
             return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				List[int]		,				__a				:				Optional[List[int]] = None		,				__a				:				bool = False							)					->List[int]:

          if already_has_special_tokens:
             if token_ids_a is not None:
                raise ValueError(
                    """You should not supply a second sequence if the provided sequence of """
                    """ids is already formatted with special tokens for the model."""							)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
             return [1] + ([0] * len(__a							)) + [1] + ([0] * len(__a							)) + [1]
          return [1] + ([0] * len(__a							)) + [1]



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				List[int]		,				__a				:				Optional[List[int]] = None							)					->List[int]:
          lowerCamelCase_						:   Optional[Any]		= [self.sep_token_id]
          lowerCamelCase_						:   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 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				str		,				__a				:				Optional[str] = None							)					->Tuple[str]:
          if not os.path.isdir(__a							):
             logger.error("""Vocabulary path ({}) should be a directory""".format(__a							)							)
             return
          lowerCamelCase_						:   Dict		= os.path.join(
              __a		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							):
             copyfile(self.vocab_file		,				__a							)

          return (out_vocab_file,)









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__				:	str					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
       _a   							=							field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
       _a   							=							field(
           default=128						,       metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Optional[Any]		= self.task_name.lower()







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"train"
       _a   							=							"dev"
       _a   							=							"test"







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42
       _a   							=							42



       def __init__(						self				:				Any		,				__a				:				GlueDataTrainingArguments		,				__a				:				PreTrainedTokenizerBase		,				__a				:				Optional[int] = None		,				__a				:				Union[str, Split] = Split.train		,				__a				:				Optional[str] = None		,				)					->Optional[int]:
          warnings.warn(
              """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
              """library. You can have a look at this example script for pointers: """
              """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""		,				__a		,				)
          lowerCamelCase_						:   Optional[int]		= args
          lowerCamelCase_						:   Tuple		= glue_processors[args.task_name]()
          lowerCamelCase_						:   Optional[Any]		= glue_output_modes[args.task_name]
          if isinstance(__a		,				__a							):
             try:
                lowerCamelCase_						:   List[Any]		= Split[mode]
             except KeyError:
                raise KeyError("""mode is not a valid split name"""							)
        # Load data features from cache or dataset file
          lowerCamelCase_						:   List[Any]		= os.path.join(
              cache_dir if cache_dir is not None else args.data_dir		,				F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''		,				)
          lowerCamelCase_						:   Any		= self.processor.get_labels()
          if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
              "RobertaTokenizer",
              "RobertaTokenizerFast",
              "XLMRobertaTokenizer",
              "BartTokenizer",
              "BartTokenizerFast",
          ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase_,	lowerCamelCase_						:   int		= label_list[2], label_list[1]
          lowerCamelCase_						:   Any		= label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase_						:   List[Any]		= cached_features_file + """.lock"""
          with FileLock(__a							):
             if os.path.exists(__a							) and not args.overwrite_cache:
                lowerCamelCase_						:   str		= time.time()
                lowerCamelCase_						:   int		= torch.load(__a							)
                logger.info(
                    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''		,				time.time() - start							)
             else:
                logger.info(F'''Creating features from dataset file at {args.data_dir}'''							)

                if mode == Split.dev:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_dev_examples(args.data_dir							)
                elif mode == Split.test:
                   lowerCamelCase_						:   Tuple		= self.processor.get_test_examples(args.data_dir							)
                else:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_train_examples(args.data_dir							)
                if limit_length is not None:
                   lowerCamelCase_						:   Dict		= examples[:limit_length]
                lowerCamelCase_						:   Union[str, Any]		= glue_convert_examples_to_features(
                    __a		,				__a		,				max_length=args.max_seq_length		,				label_list=__a		,				output_mode=self.output_mode		,				)
                lowerCamelCase_						:   Optional[Any]		= time.time()
                torch.save(self.features		,				__a							)
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''							)



       def __len__(						self				:				Any							)					->Any:
          return len(self.features							)



       def __getitem__(						self				:				List[Any]		,				__a				:				Optional[int]							)					->InputFeatures:
          return self.features[i]




       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[int]:
          return self.label_list





