




from __future__ import annotations




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	list[tuple[int, int]]:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						position
    __UpperCamelCase  		=						[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    __UpperCamelCase  		=						[]

    for position in positions:
        __UpperCamelCase		,      __UpperCamelCase  		=						position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(lowercase_      )

    return permissible_positions




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	bool:

    """simple docstring"""


    return not any(elem == 0 for row in board for elem in row      )




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

    """simple docstring"""


    if is_complete(lowercase_      ):
        return True

    for position in get_valid_pos(lowercase_					,     len(lowercase_      )      ):
        __UpperCamelCase		,      __UpperCamelCase  		=						position

        if board[y][x] == 0:
            __UpperCamelCase  		=						curr + 1
            if open_knight_tour_helper(lowercase_					,     lowercase_					,     curr + 1      ):
                return True
            __UpperCamelCase  		=						0

    return False




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	list[list[int]]:

    """simple docstring"""


    __UpperCamelCase  		=						[[0 for i in range(lowercase_      )] for j in range(lowercase_      )]

    for i in range(lowercase_      ):
        for j in range(lowercase_      ):
            __UpperCamelCase  		=						1
            if open_knight_tour_helper(lowercase_					,     (i, j)					,     1      ):
                return board
            __UpperCamelCase  		=						0

    __UpperCamelCase  		=						F"Open Kight Tour cannot be performed on a board of size {n}"
    raise ValueError(lowercase_      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()












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






