import copy
import random

from transformers import CLIPTokenizer





class 		_lowercase						(					snake_case_       ):

 def __init__( self	:							List[Any]   ,      *snake_case	:							int   ,      **snake_case	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  super().__init__(*snake_case   ,      **snake_case			)
  UpperCamelCase_					:						str =					{}

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Union[str, Any]   ,      *snake_case	:							Optional[int]   ,      **snake_case	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					super().add_tokens(snake_case   ,      *snake_case   ,      **snake_case			)
  if num_added_tokens == 0:
   raise ValueError(
       f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
       ' `placeholder_token` that is not already in the tokenizer.'			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							List[Any]   ,      *snake_case	:							int   ,      snake_case	:							Optional[Any]=1   ,      **snake_case	:							List[Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					[]
  if num_vec_per_token == 1:
   self.try_adding_tokens(snake_case   ,      *snake_case   ,      **snake_case			)
   output.append(snake_case			)
  else:
   UpperCamelCase_					:						int =					[]
   for i in range(snake_case			):
    UpperCamelCase_					:						Tuple =					placeholder_token + f"_{i}"
    self.try_adding_tokens(snake_case   ,      *snake_case   ,      **snake_case			)
    output.append(snake_case			)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
  for token in self.token_map:
   if token in placeholder_token:
    raise ValueError(
        f"The tokenizer already has placeholder token {token} that can get confused with"
        f" {placeholder_token}keep placeholder tokens independent"			)
  UpperCamelCase_					:						Union[str, Any] =					output

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							int   ,      snake_case	:							List[Any]=False   ,      snake_case	:							int=1.0			)   ->						List[str]:

  """simple docstring"""





  if isinstance(snake_case   ,      snake_case			):
   UpperCamelCase_					:						Union[str, Any] =					[]
   for i in range(len(snake_case			)			):
    output.append(self.replace_placeholder_tokens_in_text(text[i]   ,      vector_shuffle=snake_case			)			)
   return output
  for placeholder_token in self.token_map:
   if placeholder_token in text:
    UpperCamelCase_					:						Dict =					self.token_map[placeholder_token]
    UpperCamelCase_					:						Any =					tokens[: 1 + int(len(snake_case			) * prop_tokens_to_load			)]
    if vector_shuffle:
     UpperCamelCase_					:						str =					copy.copy(snake_case			)
     random.shuffle(snake_case			)
    UpperCamelCase_					:						Any =					text.replace(snake_case   ,      ' '.join(snake_case			)			)
  return text

 def __call__( self	:							Union[str, Any]   ,      snake_case	:							Optional[Any]   ,      *snake_case	:							Optional[Any]   ,      snake_case	:							List[Any]=False   ,      snake_case	:							Optional[Any]=1.0   ,      **snake_case	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  return super().__call__(
      self.replace_placeholder_tokens_in_text(
          snake_case   ,      vector_shuffle=snake_case   ,      prop_tokens_to_load=snake_case			)   ,      *snake_case   ,      **snake_case   ,      )


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      *snake_case	:							List[str]   ,      snake_case	:							str=False   ,      snake_case	:							int=1.0   ,      **snake_case	:							str			)   ->						List[str]:

  """simple docstring"""





  return super().encode(
      self.replace_placeholder_tokens_in_text(
          snake_case   ,      vector_shuffle=snake_case   ,      prop_tokens_to_load=snake_case			)   ,      *snake_case   ,      **snake_case   ,      )




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

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

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



