
'''simple docstring'''







import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Any   =					FunnelTokenizer
  __UpperCAmelCase				:				List[str]   =					FunnelTokenizerFast
  __UpperCAmelCase				:				Optional[Any]   =					True
  __UpperCAmelCase				:				Tuple   =					True


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     super().setUp()

     _a				:	Any			=   [
         '<unk>',
         '<cls>',
         '<sep>',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     _a				:	Optional[Any]			=   os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES['vocab_file']       )
     with open(self.vocab_file							,'w'							,encoding='utf-8'       ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )


  def   __lowercase		(		self      :     int							,**_a      :     Dict       ):


     '''simple docstring'''






     return FunnelTokenizer.from_pretrained(self.tmpdirname							,**_a       )


  def   __lowercase		(		self      :     List[str]							,**_a      :     List[Any]       ):


     '''simple docstring'''






     return FunnelTokenizerFast.from_pretrained(self.tmpdirname							,**_a       )


  def   __lowercase		(		self      :     Dict							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   'UNwant\u00E9d,running'
     _a				:	int			=   'unwanted, running'
     return input_text, output_text


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.tokenizer_class(self.vocab_file       )

     _a				:	str			=   tokenizer.tokenize('UNwant\u00E9d,running'       )
     self.assertListEqual(_a							,['un', '##want', '##ed', ',', 'runn', '##ing']       )
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )							,[7, 4, 5, 10, 8, 9]       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.get_tokenizers(do_lower_case=_a       )
     for tokenizer in tokenizers:
        _a				:	str			=   tokenizer('UNwant\u00E9d,running'       )
        _a				:	Optional[int]			=   len(inputs['input_ids']       ) - 1
        self.assertListEqual(inputs['token_type_ids']							,[2] + [0] * sentence_len       )

        _a				:	Any			=   tokenizer('UNwant\u00E9d,running'							,'UNwant\u00E9d,running'       )
        self.assertListEqual(inputs['token_type_ids']							,[2] + [0] * sentence_len + [1] * sentence_len       )





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



