import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowercase__ 	=     logging.getLogger(__name__)




@dataclass(frozen=__lowerCAmelCase    )
class        __snake_case	:
      a__				=		42
      a__				=		42
      a__				=		None
      a__				=		None
      a__				=		None







@dataclass(frozen=__lowerCAmelCase    )
class        __snake_case	:
      a__				=		42
      a__				=		None
      a__				=		None
      a__				=		None
      a__				=		None


if is_torch_available():
 import torch
 from torch.utils.data import Dataset




 class        __snake_case	(					__lowerCAmelCase    ):
       a__				=		42



       def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase=False   ,      lowercase = False   ,      )     ->      str:





         '''simple docstring'''





         a__:					Tuple      							=     hans_processors[task]()

         a__:					Optional[int]      							=     os.path.join(
             lowercase   ,      'cached_{}_{}_{}_{}'.format(
                 'dev' if evaluate else 'train'   ,      tokenizer.__class__.__name__   ,      str(lowercase)   ,      lowercase   ,      )   ,      )
         a__:					Optional[int]      							=     processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           a__:					int      							=     label_list[2], label_list[1]
         a__:					Optional[Any]      							=     label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         a__:					List[str]      							=     cached_features_file + '.lock'
         with FileLock(lowercase):
           if os.path.exists(lowercase) and not overwrite_cache:
             logger.info(f'Loading features from cached file {cached_features_file}')
             a__:					Union[str, Any]      							=     torch.load(lowercase)
           else:
             logger.info(f'Creating features from dataset file at {data_dir}')

             a__:					Dict      							=     (
                 processor.get_dev_examples(lowercase) if evaluate else processor.get_train_examples(lowercase)
             )

             logger.info('Training examples: %s'   ,      len(lowercase))
             a__:					int      							=     hans_convert_examples_to_features(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)
             logger.info('Saving features into cached file %s'   ,      lowercase)
             torch.save(self.features   ,      lowercase)



       def __len__(						self)     ->      int:





         '''simple docstring'''





         return len(self.features)



       def __getitem__(						self   ,      lowercase)     ->      InputFeatures:





         '''simple docstring'''





         return self.features[i]



       def   lowerCamelCase_       (						self)     ->      int:





         '''simple docstring'''





         return self.label_list


if is_tf_available():
 import tensorflow as tf




 class        __snake_case	:
       a__				=		42



       def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = 1_28   ,      lowercase=False   ,      lowercase = False   ,      )     ->      Dict:





         '''simple docstring'''





         a__:					Union[str, Any]      							=     hans_processors[task]()
         a__:					str      							=     processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           a__:					int      							=     label_list[2], label_list[1]
         a__:					Optional[int]      							=     label_list

         a__:					str      							=     processor.get_dev_examples(lowercase) if evaluate else processor.get_train_examples(lowercase)
         a__:					Tuple      							=     hans_convert_examples_to_features(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)

         def gen():
           for ex_index, ex in tqdm.tqdm(enumerate(self.features)   ,      desc='convert examples to features'):
             if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d of %d' % (ex_index, len(lowercase)))

             yield (
                 {
                     "example_id": 0,
                     "input_ids": ex.input_ids,
                     "attention_mask": ex.attention_mask,
                     "token_type_ids": ex.token_type_ids,
                 },
                 ex.label,
             )

         a__:					Union[str, Any]      							=     tf.data.Dataset.from_generator(
             lowercase   ,      (
                 {
                     'example_id': tf.intaa,
                     'input_ids': tf.intaa,
                     'attention_mask': tf.intaa,
                     'token_type_ids': tf.intaa,
                 },
                 tf.intaa,
             )   ,      (
                 {
                     'example_id': tf.TensorShape([]),
                     'input_ids': tf.TensorShape([None, None]),
                     'attention_mask': tf.TensorShape([None, None]),
                     'token_type_ids': tf.TensorShape([None, None]),
                 },
                 tf.TensorShape([]),
             )   ,      )



       def   lowerCamelCase_       (						self)     ->      int:





         '''simple docstring'''





         return self.dataset



       def __len__(						self)     ->      int:





         '''simple docstring'''





         return len(self.features)



       def __getitem__(						self   ,      lowercase)     ->      InputFeatures:





         '''simple docstring'''





         return self.features[i]



       def   lowerCamelCase_       (						self)     ->      Tuple:





         '''simple docstring'''





         return self.label_list




class        __snake_case	(					__lowerCAmelCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        return self._create_examples(self._read_tsv(os.path.join(lowercase   ,      'heuristics_train_set.txt'))   ,      'train')



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        return self._create_examples(self._read_tsv(os.path.join(lowercase   ,      'heuristics_evaluation_set.txt'))   ,      'dev')



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        return ["contradiction", "entailment", "neutral"]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     []
        for i, line in enumerate(lowercase):
          if i == 0:
            continue
          a__:					Union[str, Any]      							=     '%s-%s' % (set_type, line[0])
          a__:					Union[str, Any]      							=     line[5]
          a__:					Optional[int]      							=     line[6]
          a__:					int      							=     line[7][2:] if line[7].startswith('ex') else line[7]
          a__:					int      							=     line[0]
          examples.append(InputExample(guid=lowercase   ,      text_a=lowercase   ,      text_b=lowercase   ,      label=lowercase   ,      pairID=lowercase))
        return examples







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					)     ->Optional[int]:
  a__:					Optional[Any]      							=     {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE       )}

  a__:					Any      							=     []
  for ex_index, example in tqdm.tqdm(enumerate(_SCREAMING_SNAKE_CASE       )		,					desc='convert examples to features'       ):
    if ex_index % 10000 == 0:
      logger.info('Writing example %d' % (ex_index)       )

    a__:					Any      							=     tokenizer(
        example.text_a		,					example.text_b		,					add_special_tokens=_SCREAMING_SNAKE_CASE		,					max_length=_SCREAMING_SNAKE_CASE		,					padding='max_length'		,					truncation=_SCREAMING_SNAKE_CASE		,					return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,					)

    a__:					Union[str, Any]      							=     label_map[example.label] if example.label in label_map else 0

    a__:					int      							=     int(example.pairID       )

    features.append(InputFeatures(**_SCREAMING_SNAKE_CASE		,					label=_SCREAMING_SNAKE_CASE		,					pairID=_SCREAMING_SNAKE_CASE       )       )

  for i, example in enumerate(examples[:5]       ):
    logger.info('*** Example ***'       )
    logger.info(F'guid: {example}'       )
    logger.info(F'features: {features[i]}'       )

  return features


lowercase__ 	=     {
    'hans': 3,
}


lowercase__ 	=     {
    'hans': HansProcessor,
}



"""simple docstring"""
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		DebertaTokenizer
      a__				=		True
      a__				=		DebertaTokenizerFast



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        a__:					List[str]      							=     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        a__:					List[str]      							=     dict(zip(lowercase   ,      range(len(lowercase))))
        a__:					Any      							=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:					Optional[Any]      							=     {'unk_token': '[UNK]'}

        a__:					Any      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file'])
        a__:					Dict      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file'])
        with open(self.vocab_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write(json.dumps(lowercase) + '\n')
        with open(self.merges_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     'lower newer'
        a__:					int      							=     'lower newer'
        return input_text, output_text



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.get_tokenizer()
        a__:					List[Any]      							=     'lower newer'
        a__:					Optional[int]      							=     ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        a__:					Optional[Any]      							=     tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase   ,      lowercase)

        a__:					int      							=     tokens + [tokenizer.unk_token]
        a__:					Optional[int]      							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					int      							=     self.get_tokenizer()
        a__:					Any      							=     tokenizer('Hello'   ,      'World')
        a__:					Optional[Any]      							=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids']   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.tokenizer_class.from_pretrained('microsoft/deberta-base')

        a__:					Union[str, Any]      							=     tokenizer.encode('sequence builders'   ,      add_special_tokens=lowercase)
        a__:					Optional[Any]      							=     tokenizer.encode('multi-sequence build'   ,      add_special_tokens=lowercase)

        a__:					List[str]      							=     tokenizer.encode(
            'sequence builders'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)
        a__:					Any      							=     tokenizer.encode(
            'sequence builders'   ,      'multi-sequence build'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)

        a__:					Optional[int]      							=     tokenizer.build_inputs_with_special_tokens(lowercase)
        a__:					Union[str, Any]      							=     tokenizer.build_inputs_with_special_tokens(lowercase   ,      lowercase)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.tokenizer_class]
        if self.test_rust_tokenizer:
          tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
          a__:					int      							=     tokenizer_class.from_pretrained('microsoft/deberta-base')

          a__:					List[str]      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          a__:					Dict      							=     tokenizer(lowercase   ,      padding=lowercase)
          a__:					Union[str, Any]      							=     [tokenizer.decode(lowercase   ,      skip_special_tokens=lowercase) for seq in encoding['input_ids']]

          # fmt: off
          a__:					Any      							=     {
              'input_ids': [
                  [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
              ],
              'token_type_ids': [
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
              ],
              'attention_mask': [
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
              ]
          }
          # fmt: on

          a__:					str      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          self.assertDictEqual(encoding.data   ,      lowercase)

          for expected, decoded in zip(lowercase   ,      lowercase):
            self.assertEqual(lowercase   ,      lowercase)


