





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
 def __init__(							self :Tuple		,			lowercase_ :Optional[int]=None		,			lowercase_ :Optional[int]=None		,			**lowercase_ :Dict				)->				int:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :List[Any]		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :List[Any]				)->				Optional[Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			*lowercase_ :Optional[Any]		,			**lowercase_ :List[Any]				)->				Any:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :str		,			*lowercase_ :int		,			**lowercase_ :Dict				)->				int:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)



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










'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

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

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

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





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



