






"""simple docstring"""





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


UpperCamelCase_	:				Tuple										= logging.get_logger(__name__)


@dataclass
class      _lowercase :
      _a   :     str								=   field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()						)}						)
      _a   :     str								=   field(
          metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}						)
      _a   :     int								=   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   :     bool								=   field(
          default=lowerCAmelCase		,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    str  			=self.task_name.lower()



class      _lowercase (     lowerCAmelCase						):
      _a   :     Dict								=   '''train'''
      _a   :     List[str]								=   '''dev'''
      _a   :     Dict								=   '''test'''



class      _lowercase (     lowerCAmelCase						):
      _a   :     GlueDataTrainingArguments
      _a   :     str
      _a   :     List[InputFeatures]


      def __init__(    self				:	Optional[int]							,   a				:	GlueDataTrainingArguments							,   a				:	PreTrainedTokenizerBase							,   a				:	Optional[int] = None							,   a				:	Union[str, Split] = Split.train							,   a				:	Optional[str] = None							,   ):





            """simple docstring"""







            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							,   )
            __snake_case      :    Dict  			=args
            __snake_case      :    Union[str, Any]  			=glue_processors[args.task_name]()
            __snake_case      :    Union[str, Any]  			=glue_output_modes[args.task_name]
            if isinstance(a							,   a						):
                  try:
                        __snake_case      :    str  			=Split[mode]
                  except KeyError:
                        raise KeyError('''mode is not a valid split name'''						)
        # Load data features from cache or dataset file
            __snake_case      :    Dict  			=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}'''							,   )
            __snake_case      :    Dict  			=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)
                  __snake_case			,				__snake_case      :    List[str]  			=label_list[2], label_list[1]
            __snake_case      :    Tuple  			=label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            __snake_case      :    List[Any]  			=cached_features_file + '''.lock'''
            with FileLock(a						):
                  if os.path.exists(a						) and not args.overwrite_cache:
                        __snake_case      :    Optional[int]  			=time.time()
                        __snake_case      :    Dict  			=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:
                              __snake_case      :    List[str]  			=self.processor.get_dev_examples(args.data_dir						)
                        elif mode == Split.test:
                              __snake_case      :    List[str]  			=self.processor.get_test_examples(args.data_dir						)
                        else:
                              __snake_case      :    Optional[int]  			=self.processor.get_train_examples(args.data_dir						)
                        if limit_length is not None:
                              __snake_case      :    List[Any]  			=examples[:limit_length]
                        __snake_case      :    Union[str, Any]  			=glue_convert_examples_to_features(
                            a							,   a							,   max_length=args.max_seq_length							,   label_list=a							,   output_mode=self.output_mode							,   )
                        __snake_case      :    str  			=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				:	Dict						):





            """simple docstring"""







            return len(self.features						)


      def __getitem__(    self				:	Optional[int]							,   a				:	str						):





            """simple docstring"""







            return self.features[i]







      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            return self.label_list










"""simple docstring"""





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

import sentencepiece as spm

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


UpperCamelCase_	:				Optional[Any]										= logging.get_logger(__name__)

UpperCamelCase_	:				Union[str, Any]										= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_	:				Optional[Any]										= {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase_	:				int										= {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

UpperCamelCase_	:				Optional[int]										= """▁"""


class      _lowercase (     lowerCAmelCase						):
      _a   :     int								=   VOCAB_FILES_NAMES
      _a   :     int								=   PRETRAINED_VOCAB_FILES_MAP
      _a   :     Union[str, Any]								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a   :     Optional[int]								=   ['''input_ids''', '''attention_mask''']


      def __init__(    self				:	str							,   a				:	Optional[Any]							,   a				:	Optional[Any]="<s>"							,   a				:	Dict="</s>"							,   a				:	Any="</s>"							,   a				:	Optional[int]="<s>"							,   a				:	Optional[Any]="<unk>"							,   a				:	int="<pad>"							,   a				:	Tuple="<mask>"							,   a				:	Optional[Dict[str, Any]] = None							,   **a				:	List[Any]							,   ):





            """simple docstring"""







            __snake_case      :    Dict  			=AddedToken(a							,   lstrip=a							,   rstrip=a						) if isinstance(a							,   a						) else mask_token

            __snake_case      :    List[str]  			={} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=a							,   eos_token=a							,   unk_token=a							,   sep_token=a							,   cls_token=a							,   pad_token=a							,   mask_token=a							,   sp_model_kwargs=self.sp_model_kwargs							,   **a							,   )

            __snake_case      :    Optional[int]  			=vocab_file
            __snake_case      :    Dict  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(str(a						)						)

            __snake_case      :    Optional[Any]  			={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

            __snake_case      :    Optional[int]  			=len(self.sp_model						) - 1
            __snake_case      :    Union[str, Any]  			={v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case      :    Dict  			=[self.cls_token_id]
            __snake_case      :    List[str]  			=[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep


      def _UpperCamelCase						(    self				:	str							,   a				:	List[int]							,   a				:	Optional[List[int]] = None							,   a				:	bool = False						):





            """simple docstring"""







            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 _UpperCamelCase						(    self				:	Dict							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=[self.sep_token_id]
            __snake_case      :    int  			=[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]


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return len(self.sp_model						)


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			={self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
            vocab.update(self.added_tokens_encoder						)
            return vocab


      def _UpperCamelCase						(    self				:	str							,   a				:	str						):





            """simple docstring"""







            return self.sp_model.encode(a							,   out_type=a						)


      def _UpperCamelCase						(    self				:	int							,   a				:	Union[str, Any]						):





            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            __snake_case      :    List[Any]  			=self.sp_model.PieceToId(a						)

            return spm_id if spm_id else self.unk_token_id


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Dict						):





            """simple docstring"""







            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=[]
            __snake_case      :    Optional[int]  			=''''''
            __snake_case      :    Optional[int]  			=False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(a						) + token
                        __snake_case      :    str  			=True
                        __snake_case      :    int  			=[]
                  else:
                        current_sub_tokens.append(a						)
                        __snake_case      :    Tuple  			=False
            out_string += self.sp_model.decode(a						)
            return out_string.strip()


      def __getstate__(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.__dict__.copy()
            __snake_case      :    Optional[Any]  			=None
            return state


      def __setstate__(    self				:	Optional[int]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=d

            # for backward compatibility
            if not hasattr(self							,   '''sp_model_kwargs'''						):
                  __snake_case      :    Tuple  			={}

            __snake_case      :    int  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(self.vocab_file						)







      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	str							,   a				:	Optional[str] = None						):





            """simple docstring"""







            if not os.path.isdir(a						):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                  return
            __snake_case      :    int  			=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						) and os.path.isfile(self.vocab_file						):
                  copyfile(self.vocab_file							,   a						)
            elif not os.path.isfile(self.vocab_file						):
                  with open(a							,   '''wb'''						) as fi:
                        __snake_case      :    List[Any]  			=self.sp_model.serialized_model_proto()
                        fi.write(a						)

            return (out_vocab_file,)


