





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

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  BioGptTokenizer
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  False




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             __SCREAMING_SNAKE_CASE       :int		    =  [
                 '''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>''',
             ]
             __SCREAMING_SNAKE_CASE       :str		    =  dict(zip(SCREAMING_SNAKE_CASE__		,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE       :str		    =  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  '''lower newer'''
             __SCREAMING_SNAKE_CASE       :Tuple		    =  '''lower newer'''
             return input_text, output_text




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  BioGptTokenizer(self.vocab_file		,self.merges_file							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  '''lower'''
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ['''low''', '''er</w>''']
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
             self.assertListEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokens + ['''<unk>''']
             __SCREAMING_SNAKE_CASE       :str		    =  [14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)		,SCREAMING_SNAKE_CASE__							)




       @slow
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''							)

             __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer.encode('''sequence builders'''		,add_special_tokens=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  tokenizer.encode('''multi-sequence build'''		,add_special_tokens=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :str		    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             self.assertTrue(encoded_sentence == [2] + text							)
             self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)






"""simple docstring"""
from __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

      if depth < 0:
            raise ValueError('''Depth cannot be less than 0'''							)

      if len(a_							) == 0:
            raise ValueError('''Scores cannot be empty'''							)

      if depth == height:
            return scores[node_index]

      if is_max:
            return max(
                minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

      return min(
          minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()