



"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

lowerCAmelCase_   						=   logging.get_logger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[List[ImageInput]]:
		if isinstance(__lowerCamelCase						,     (list, tuple)	) and isinstance(videos[0]						,     (list, tuple)	) and is_valid_image(videos[0][0]	):
				return videos

		elif isinstance(__lowerCamelCase						,     (list, tuple)	) and is_valid_image(videos[0]	):
				return [videos]

		elif is_valid_image(__lowerCamelCase	):
				return [[videos]]

		raise ValueError(f"""Could not make batched video from {videos}"""	)





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				["pixel_values"]







		def __init__(	self						:  List[Any]			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = PILImageResampling.BILINEAR			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  bool = True			,_snake_case						:  Union[int, float] = 1 / 255			,_snake_case						:  bool = True			,_snake_case						:  bool = True			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,**_snake_case						:  List[str]			,)					->		None:






				"""simple docstring"""



				super().__init__(**_snake_case						)
				lowercase__   :    int				=			size if size is not None else {'''shortest_edge''': 256}
				lowercase__   :    Union[str, Any]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				lowercase__   :    List[str]				=			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				lowercase__   :    Optional[Any]				=			get_size_dict(_snake_case			,param_name='''crop_size'''						)

				lowercase__   :    List[Any]				=			do_resize
				lowercase__   :    Optional[int]				=			size
				lowercase__   :    Union[str, Any]				=			do_center_crop
				lowercase__   :    int				=			crop_size
				lowercase__   :    List[str]				=			resample
				lowercase__   :    int				=			do_rescale
				lowercase__   :    Tuple				=			rescale_factor
				lowercase__   :    List[Any]				=			offset
				lowercase__   :    Optional[int]				=			do_normalize
				lowercase__   :    List[Any]				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				lowercase__   :    Union[str, Any]				=			image_std if image_std is not None else IMAGENET_STANDARD_STD







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  PILImageResampling = PILImageResampling.BILINEAR			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  List[str]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				if "shortest_edge" in size:
						lowercase__   :    Optional[int]				=			get_resize_output_image_size(_snake_case			,size['''shortest_edge''']			,default_to_square=_snake_case						)
				elif "height" in size and "width" in size:
						lowercase__   :    Optional[Any]				=			(size['''height'''], size['''width'''])
				else:
						raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""						)
				return resize(_snake_case			,size=_snake_case			,resample=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Union[str, Any]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    Dict				=			get_size_dict(_snake_case						)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""						)
				return center_crop(_snake_case			,size=(size['''height'''], size['''width'''])			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[int, float]			,_snake_case						:  bool = True			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Tuple			,)					->		Any:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			image.astype(np.floataa						)
				if offset:
						lowercase__   :    List[str]				=			image - (scale / 2)
				return rescale(_snake_case			,scale=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Tuple			,)					->		np.ndarray:






				"""simple docstring"""



				return normalize(_snake_case			,mean=_snake_case			,std=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  ImageInput			,_snake_case						:  bool = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  bool = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  bool = None			,_snake_case						:  float = None			,_snake_case						:  bool = None			,_snake_case						:  bool = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[ChannelDimension] = ChannelDimension.FIRST			,)					->		np.ndarray:






				"""simple docstring"""



				if do_resize and size is None or resample is None:
						raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

				if do_center_crop and crop_size is None:
						raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

				if do_rescale and rescale_factor is None:
						raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

				if offset and not do_rescale:
						raise ValueError('''For offset, do_rescale must also be set to True.'''						)

				# All transformations expect numpy arrays.
				lowercase__   :    Dict				=			to_numpy_array(_snake_case						)

				if do_resize:
						lowercase__   :    Union[str, Any]				=			self.resize(image=_snake_case			,size=_snake_case			,resample=_snake_case						)

				if do_center_crop:
						lowercase__   :    Optional[Any]				=			self.center_crop(_snake_case			,size=_snake_case						)

				if do_rescale:
						lowercase__   :    List[Any]				=			self.rescale(image=_snake_case			,scale=_snake_case			,offset=_snake_case						)

				if do_normalize:
						lowercase__   :    List[Any]				=			self.normalize(image=_snake_case			,mean=_snake_case			,std=_snake_case						)

				lowercase__   :    List[str]				=			to_channel_dimension_format(_snake_case			,_snake_case						)
				return image







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  ImageInput			,_snake_case						:  bool = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  bool = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  bool = None			,_snake_case						:  float = None			,_snake_case						:  bool = None			,_snake_case						:  bool = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[str, TensorType]] = None			,_snake_case						:  ChannelDimension = ChannelDimension.FIRST			,**_snake_case						:  Dict			,)					->		PIL.Image.Image:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			do_resize if do_resize is not None else self.do_resize
				lowercase__   :    int				=			resample if resample is not None else self.resample
				lowercase__   :    List[Any]				=			do_center_crop if do_center_crop is not None else self.do_center_crop
				lowercase__   :    str				=			do_rescale if do_rescale is not None else self.do_rescale
				lowercase__   :    int				=			rescale_factor if rescale_factor is not None else self.rescale_factor
				lowercase__   :    Optional[Any]				=			offset if offset is not None else self.offset
				lowercase__   :    Optional[Any]				=			do_normalize if do_normalize is not None else self.do_normalize
				lowercase__   :    Any				=			image_mean if image_mean is not None else self.image_mean
				lowercase__   :    List[str]				=			image_std if image_std is not None else self.image_std

				lowercase__   :    Union[str, Any]				=			size if size is not None else self.size
				lowercase__   :    Union[str, Any]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				lowercase__   :    Tuple				=			crop_size if crop_size is not None else self.crop_size
				lowercase__   :    Union[str, Any]				=			get_size_dict(_snake_case			,param_name='''crop_size'''						)

				if not valid_images(_snake_case						):
						raise ValueError(
						    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
						    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

				lowercase__   :    Any				=			make_batched(_snake_case						)

				lowercase__   :    Optional[int]				=			[
				    [
				        self._preprocess_image(
				            image=_snake_case			,do_resize=_snake_case			,size=_snake_case			,resample=_snake_case			,do_center_crop=_snake_case			,crop_size=_snake_case			,do_rescale=_snake_case			,rescale_factor=_snake_case			,offset=_snake_case			,do_normalize=_snake_case			,image_mean=_snake_case			,image_std=_snake_case			,data_format=_snake_case			,)
				        for img in video
				    ]
				    for video in videos
				]

				lowercase__   :    Dict				=			{'''pixel_values''': videos}
				return BatchFeature(data=_snake_case			,tensor_type=_snake_case						)








from __future__ import annotations




def      lowercase__      (     __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
					UpperCAmelCase_    :      str            =						[
					    (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_    :      Optional[Any]            =						[]

					for position in positions:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
										if 0 <= y_test < n and 0 <= x_test < n:
															permissible_positions.append(__snake_case  )

					return permissible_positions




def      lowercase__      (     __snake_case    :							list[list[int]]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							list[list[int]]     ,       __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if is_complete(__snake_case  ):
										return True

					for position in get_valid_pos(__snake_case     ,       len(__snake_case  )  ):
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						position

										if board[y][x] == 0:
															UpperCAmelCase_    :      Optional[Any]            =						curr + 1
															if open_knight_tour_helper(__snake_case     ,       __snake_case     ,       curr + 1  ):
																				return True
															UpperCAmelCase_    :      List[Any]            =						0

					return False




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						[[0 for i in range(__snake_case  )] for j in range(__snake_case  )]

					for i in range(__snake_case  ):
										for j in range(__snake_case  ):
															UpperCAmelCase_    :      Optional[Any]            =						1
															if open_knight_tour_helper(__snake_case     ,       (i, j)     ,       1  ):
																				return board
															UpperCAmelCase_    :      List[Any]            =						0

					UpperCAmelCase_    :      List[str]            =						F"Open Kight Tour cannot be performed on a board of size {n}"
					raise ValueError(__snake_case  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






