

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



from __future__ import annotations

import math




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






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

    if len(SCREAMING_SNAKE_CASE_			) == 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			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						minimax(depth + 1			,						node_index * 2 + 1			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						)

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




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    UpperCamelCase_			  =						math.log(len(SCREAMING_SNAKE_CASE_			)			,						2			)
    print("Optimal value : "			,						end=""			)
    print(minimax(0			,						0			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()
