






from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__UpperCAmelCase							:   Tuple							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Dict      )     -> List[List[ImageInput]]:





				'''simple docstring'''


				if isinstance(__snake_case	,	(list, tuple)      ) and isinstance(videos[0]	,	(list, tuple)      ) and is_valid_image(videos[0][0]      ):
								return videos

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

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

				raise ValueError(F'Could not make batched video from {videos}'      )


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = 1 / 255					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = None					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   size if size is not None else {"shortest_edge": 224}
					snake_case__      :Any         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )
					snake_case__      :Tuple         =   crop_size if crop_size is not None else {"height": 224, "width": 224}
					snake_case__      :Optional[Any]         =   get_size_dict(UpperCamelCase					,param_name="crop_size"       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :Any         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )
					if "shortest_edge" in size:
									snake_case__      :Any         =   get_resize_output_image_size(UpperCamelCase					,size["shortest_edge"]					,default_to_square=UpperCamelCase       )
					elif "height" in size and "width" in size:
									snake_case__      :int         =   (size["height"], size["width"])
					else:
									raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
					return resize(UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :Optional[Any]         =   get_size_dict(UpperCamelCase       )
					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(UpperCamelCase					,size=(size["height"], size["width"])					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    Tuple:
					return rescale(UpperCamelCase					,scale=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					return normalize(UpperCamelCase					,mean=UpperCamelCase					,std=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,)						->    np.ndarray:
					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."       )

					# All transformations expect numpy arrays.
					snake_case__      :Any         =   to_numpy_array(UpperCamelCase       )

					if do_resize:
									snake_case__      :str         =   self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       )

					if do_center_crop:
									snake_case__      :Optional[int]         =   self.center_crop(UpperCamelCase					,size=UpperCamelCase       )

					if do_rescale:
									snake_case__      :int         =   self.rescale(image=UpperCamelCase					,scale=UpperCamelCase       )

					if do_normalize:
									snake_case__      :Any         =   self.normalize(image=UpperCamelCase					,mean=UpperCamelCase					,std=UpperCamelCase       )

					snake_case__      :Optional[Any]         =   to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       )
					return image






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Any         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :Dict         =   resample if resample is not None else self.resample
					snake_case__      :Dict         =   do_center_crop if do_center_crop is not None else self.do_center_crop
					snake_case__      :List[Any]         =   do_rescale if do_rescale is not None else self.do_rescale
					snake_case__      :Optional[int]         =   rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case__      :Union[str, Any]         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :Dict         =   image_mean if image_mean is not None else self.image_mean
					snake_case__      :Optional[int]         =   image_std if image_std is not None else self.image_std

					snake_case__      :str         =   size if size is not None else self.size
					snake_case__      :Union[str, Any]         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )
					snake_case__      :Dict         =   crop_size if crop_size is not None else self.crop_size
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase					,param_name="crop_size"       )

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

					snake_case__      :int         =   make_batched(UpperCamelCase       )

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

					snake_case__      :List[str]         =   {"pixel_values": videos}
					return BatchFeature(data=UpperCamelCase					,tensor_type=UpperCamelCase       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


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

				if len(__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	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

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





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()