




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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   DebertaTokenizer
 lowercase_       =   True
 lowercase_       =   DebertaTokenizerFast



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       super().setUp()

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

       SCREAMING_SNAKE_CASE_     						=      os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES['vocab_file']						)
       SCREAMING_SNAKE_CASE_     						=      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(_lowerCAmelCase						) + '\n'						)
       with open(self.merges_file    ,    'w'    ,    encoding='utf-8'						) as fp:
             fp.write('\n'.join(_lowerCAmelCase						)						)



 def 				lowerCAmelCase_  (							self  :		Any    ,    **_lowerCAmelCase  :		int						):
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      'lower newer'
       SCREAMING_SNAKE_CASE_     						=      'lower newer'
       return input_text, output_text



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
       SCREAMING_SNAKE_CASE_     						=      'lower newer'
       SCREAMING_SNAKE_CASE_     						=      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       SCREAMING_SNAKE_CASE_     						=      tokenizer.tokenize(_lowerCAmelCase						)
       self.assertListEqual(_lowerCAmelCase    ,    _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      tokens + [tokenizer.unk_token]
       SCREAMING_SNAKE_CASE_     						=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
       SCREAMING_SNAKE_CASE_     						=      tokenizer('Hello'    ,    'World'						)
       SCREAMING_SNAKE_CASE_     						=      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']    ,    _lowerCAmelCase						)



 @slow
 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      self.tokenizer_class.from_pretrained('microsoft/deberta-base'						)

       SCREAMING_SNAKE_CASE_     						=      tokenizer.encode('sequence builders'    ,    add_special_tokens=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      tokenizer.encode('multi-sequence build'    ,    add_special_tokens=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      tokenizer.encode(
           'sequence builders'    ,    add_special_tokens=_lowerCAmelCase    ,    add_prefix_space=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      tokenizer.encode(
           'sequence builders'    ,    'multi-sequence build'    ,    add_special_tokens=_lowerCAmelCase    ,    add_prefix_space=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase    ,    _lowerCAmelCase						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




 @slow
 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      [self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class						)

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

             SCREAMING_SNAKE_CASE_     						=      [
                 '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.',
             ]

             SCREAMING_SNAKE_CASE_     						=      tokenizer(_lowerCAmelCase    ,    padding=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      [tokenizer.decode(_lowerCAmelCase    ,    skip_special_tokens=_lowerCAmelCase						) for seq in encoding['input_ids']]

             # fmt: off
             SCREAMING_SNAKE_CASE_     						=      {
                 'input_ids': [
                     [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 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

             SCREAMING_SNAKE_CASE_     						=      [
                 '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    ,    _lowerCAmelCase						)

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





from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str = "isbn/0140328726"							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      olid.strip().strip('/'							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count('/'							) != 1:
            SCREAMING_SNAKE_CASE_     						=      f"{olid} is not a valid Open Library olid"
            raise ValueError(__UpperCAmelCase							)
      return requests.get(f"https://openlibrary.org/{new_olid}.json"							).json()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          'title': 'Title',
          'publish_date': 'Publish date',
          'authors': 'Authors',
          'number_of_pages': 'Number of pages:',
          'first_sentence': 'First sentence',
          'isbn_10': 'ISBN (10)',
          'isbn_13': 'ISBN (13)',
      }
      SCREAMING_SNAKE_CASE_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[Any]         =							input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
      if isbn.lower() in ("", "q", "quit", "exit", "stop"):
         break

      if len(isbn) not in (10, 13) or not isbn.isdigit():
         print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
         continue

      print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

      try:
         lowerCamelCase__			:       Union[str, Any]         =							summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
         print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
      except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
         print(f'''Sorry, there are no results for ISBN: {isbn}.''')