




from __future__ import annotations

import math




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


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

    if not scores:
        raise ValueError('''Scores cannot be empty'''      )

    if depth == height:
        return scores[node_index]

    return (
        max(
            minimax(depth + 1					,     node_index * 2					,     lowercase_					,     lowercase_					,     lowercase_      )					,     minimax(depth + 1					,     node_index * 2 + 1					,     lowercase_					,     lowercase_					,     lowercase_      )					,     )
        if is_max
        else min(
            minimax(depth + 1					,     node_index * 2					,     lowercase_					,     lowercase_					,     lowercase_      )					,     minimax(depth + 1					,     node_index * 2 + 1					,     lowercase_					,     lowercase_					,     lowercase_      )					,     )
    )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	None:

    """simple docstring"""


    __UpperCamelCase  		=						[90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    __UpperCamelCase  		=						math.log(len(lowercase_      )					,     2      )
    print(F"Optimal value : {minimax(0					,     0					,     lowercase_					,     lowercase_					,     lowercase_      )}"      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()












import warnings

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

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						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__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






