


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)							,	lowerCamelCase__			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

 for i in range(len(SCREAMING_SNAKE_CASE     )     ):
  curr_string += data_bits[i]
  if curr_string not in lexicon:
   continue

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

 while curr_string != "" and curr_string not in lexicon:
  curr_string += "0"

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   os.path.getsize(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   bin(SCREAMING_SNAKE_CASE     )[2:]
 UpperCamelCase__					:    Tuple          =   len(SCREAMING_SNAKE_CASE     )

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_SNAKE_CASE     )
   ]

   if len(result_byte_array[-1]     ) % byte_length == 0:
    result_byte_array.append('''10000000'''     )
   else:
    result_byte_array[-1] += "1" + "0" * (
        byte_length - len(result_byte_array[-1]     ) - 1
    )

   for elem in result_byte_array:
    opened_file.write(int(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])


