




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


__a							     =					logging.get_logger(__name__)

@dataclass
class     __a:






    """simple docstring"""





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





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				int  =     self.task_name.lower()


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''train'''
    lowerCAmelCase         = '''dev'''
    lowerCAmelCase         = '''test'''


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = 42
    lowerCAmelCase         = 42
    lowerCAmelCase         = 42





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = Split.train  ,_SCREAMING_SNAKE_CASE = None  ,)					->						str:
         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'''  ,_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[str]  =     args
         UpperCAmelCase_     :				Optional[Any]  =     glue_processors[args.task_name]()
         UpperCAmelCase_     :				int  =     glue_output_modes[args.task_name]
         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              try:
                   UpperCAmelCase_     :				Any  =     Split[mode]
              except KeyError:
                   raise KeyError('''mode is not a valid split name'''    )
        # Load data features from cache or dataset file
         UpperCAmelCase_     :				List[str]  =     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_     :				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)
              UpperCAmelCase_,       UpperCAmelCase_     :				str  =     label_list[2], label_list[1]
         UpperCAmelCase_     :				str  =     label_list

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





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





    def 				a__ ( self    )					->						Any:
         return self.label_list





__a							     =					'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Union[str, Any]  =     f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(_lowercase )

     UpperCAmelCase_     :				Any  =     ''''''.join(bin(_lowercase )[2:].zfill(8 ) for byte in data )

     UpperCAmelCase_     :				Any  =     len(_lowercase ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          UpperCAmelCase_     :				Union[str, Any]  =     B'''=''' * ((6 - len(_lowercase ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(_lowercase ) % 6)
     else:
          UpperCAmelCase_     :				int  =     B''''''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]					,		2 )]
             for index in range(0					,		len(_lowercase )					,		6 ) ).encode()
         + padding
     )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ) and not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Tuple  =     (
              '''argument should be a bytes-like object or ASCII string, '''
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(_lowercase )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(_lowercase					,		_lowercase ):
          try:
               UpperCAmelCase_     :				Any  =     encoded_data.decode('''utf-8''' )
          except UnicodeDecodeError:
               raise ValueError('''base64 encoded data should only contain ASCII characters''' )

     UpperCAmelCase_     :				str  =     encoded_data.count('''=''' )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(_lowercase ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          UpperCAmelCase_     :				List[Any]  =     encoded_data[:-padding]

          UpperCAmelCase_     :				List[Any]  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
     else:
          UpperCAmelCase_     :				Tuple  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )

     UpperCAmelCase_     :				str  =     [
         int(binary_stream[index : index + 8]					,		2 )
         for index in range(0					,		len(_lowercase )					,		8 )
     ]

     return bytes(_lowercase )


if __name__ == "__main__":
     import doctest

     doctest.testmod()