






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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__      :	str       	=      logging.get_logger(__name__)

snake_case__      :	Tuple       	=      """▁"""

snake_case__      :	List[str]       	=      {"""vocab_file""": """prophetnet.tokenizer"""}

snake_case__      :	str       	=      {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

snake_case__      :	Union[str, Any]       	=      {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

snake_case__      :	Any       	=      {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_1_2,
}






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     collections.OrderedDict()
   with open(__lowercase    ,      'r'    ,      encoding='utf-8') as reader:
      UpperCamelCase_									=     reader.readlines()
   for index, token in enumerate(__lowercase):
      UpperCamelCase_									=     token.rstrip('\n')
      UpperCamelCase_									=     index
   return vocab


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





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




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[UNK]"		,		_UpperCAmelCase="[PAD]"		,		_UpperCAmelCase="[CLS]"		,		_UpperCAmelCase="[MASK]"		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)			-> None:
     UpperCamelCase_									=     {} if sp_model_kwargs is None else sp_model_kwargs

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

     try:
        import sentencepiece as spm
     except ImportError:
        logger.warning(
            'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
            ' pip install sentencepiece' )
        raise

     UpperCamelCase_									=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(str(_UpperCAmelCase ) )
     UpperCamelCase_									=     vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # put special tokens and [unused] tokens into the vocab
     UpperCamelCase_									=     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

     for i in range(10 ):
        UpperCamelCase_									=     f"""[unused{i}]"""
        UpperCamelCase_									=     5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     UpperCamelCase_									=     12
     UpperCamelCase_									=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
        self.unique_no_split_tokens.append(_UpperCAmelCase )




  def __getstate__(     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.__dict__.copy()
     UpperCamelCase_									=     None
     return state




  def __setstate__(     self		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     d
     try:
        import sentencepiece as spm
     except ImportError:
        logger.warning(
            'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
            ' pip install sentencepiece' )
        raise

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

     UpperCamelCase_									=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(self.vocab_file )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 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 ([0] * len(_UpperCAmelCase )) + [1]
     return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.sep_token_id]

     if token_ids_a is None:
        return len(token_ids_a + sep ) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     return len(self.sp_model ) + self.fairseq_offset




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     return self.sp_model.encode(_UpperCAmelCase		,		out_type=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Union[str, Any]:
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     UpperCamelCase_									=     self.sp_model.PieceToId(_UpperCAmelCase )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     ''.join(_UpperCAmelCase ).replace(_UpperCAmelCase		,		' ' ).strip()
     return out_string




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = 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'] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
        copyfile(self.vocab_file		,		_UpperCAmelCase )
     elif not os.path.isfile(self.vocab_file ):
        with open(_UpperCAmelCase		,		'wb' ) as fi:
           UpperCamelCase_									=     self.sp_model.serialized_model_proto()
           fi.write(_UpperCAmelCase )

     return (out_vocab_file,)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:

     if token_ids_a is None:
        return token_ids_a + [self.sep_token_id]
     UpperCamelCase_									=     [self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep













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__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """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=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     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'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     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}"""		,		)
     UpperCamelCase_									=     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)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           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:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ 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 )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list





