




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


__lowerCamelCase   :						List[Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Dict				):





					'''simple docstring'''






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

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

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

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


class    lowerCamelCase (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=['''pixel_values''']

			def __init__( self			:   Optional[Any]       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = PILImageResampling.BILINEAR       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Union[int, float] = 1 / 255       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						**lowerCamelCase_			:   Tuple       ,						)						->   None:
								super().__init__(**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							size if size is not None else {'''shortest_edge''': 224}
								__magic_name__							:	List[str]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__magic_name__							:	str    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

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

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   PILImageResampling = PILImageResampling.BILINEAR       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Union[str, Any]       ,						)						->   np.ndarray:
								__magic_name__							:	Optional[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								if "shortest_edge" in size:
													__magic_name__							:	List[Any]    							=							get_resize_output_image_size(lowerCamelCase_       ,						size['''shortest_edge''']       ,						default_to_square=lowerCamelCase_  )
								elif "height" in size and "width" in size:
													__magic_name__							:	str    							=							(size['''height'''], size['''width'''])
								else:
													raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
								return resize(lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Optional[int]       ,						)						->   np.ndarray:
								__magic_name__							:	int    							=							get_size_dict(lowerCamelCase_  )
								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(lowerCamelCase_       ,						size=(size['''height'''], size['''width'''])       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Union[int, float]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Any       ,						)						->   Union[str, Any]:
								return rescale(lowerCamelCase_       ,						scale=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Union[float, List[float]]       ,						lowerCamelCase_			:   Union[float, List[float]]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Optional[int]       ,						)						->   np.ndarray:
								return normalize(lowerCamelCase_       ,						mean=lowerCamelCase_       ,						std=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   ImageInput       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   float = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						lowerCamelCase_			:   Optional[ChannelDimension] = 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.
								__magic_name__							:	int    							=							to_numpy_array(lowerCamelCase_  )

								if do_resize:
													__magic_name__							:	Any    							=							self.resize(image=lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_  )

								if do_center_crop:
													__magic_name__							:	List[Any]    							=							self.center_crop(lowerCamelCase_       ,						size=lowerCamelCase_  )

								if do_rescale:
													__magic_name__							:	List[str]    							=							self.rescale(image=lowerCamelCase_       ,						scale=lowerCamelCase_  )

								if do_normalize:
													__magic_name__							:	int    							=							self.normalize(image=lowerCamelCase_       ,						mean=lowerCamelCase_       ,						std=lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							to_channel_dimension_format(lowerCamelCase_       ,						lowerCamelCase_  )
								return image


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   ImageInput       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   float = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						lowerCamelCase_			:   Optional[Union[float, List[float]]] = None       ,						lowerCamelCase_			:   Optional[Union[str, TensorType]] = None       ,						lowerCamelCase_			:   ChannelDimension = ChannelDimension.FIRST       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   PIL.Image.Image:
								__magic_name__							:	Tuple    							=							do_resize if do_resize is not None else self.do_resize
								__magic_name__							:	str    							=							resample if resample is not None else self.resample
								__magic_name__							:	List[str]    							=							do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__							:	str    							=							do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__							:	List[str]    							=							rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__							:	Any    							=							do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__							:	Dict    							=							image_mean if image_mean is not None else self.image_mean
								__magic_name__							:	List[str]    							=							image_std if image_std is not None else self.image_std

								__magic_name__							:	Optional[Any]    							=							size if size is not None else self.size
								__magic_name__							:	Dict    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							crop_size if crop_size is not None else self.crop_size
								__magic_name__							:	List[str]    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

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

								__magic_name__							:	Union[str, Any]    							=							make_batched(lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							[
								    [
								        self._preprocess_image(
								            image=lowerCamelCase_       ,						do_resize=lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_       ,						do_center_crop=lowerCamelCase_       ,						crop_size=lowerCamelCase_       ,						do_rescale=lowerCamelCase_       ,						rescale_factor=lowerCamelCase_       ,						do_normalize=lowerCamelCase_       ,						image_mean=lowerCamelCase_       ,						image_std=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						)
								        for img in video
								    ]
								    for video in videos
								]

								__magic_name__							:	List[str]    							=							{'''pixel_values''': videos}
								return BatchFeature(data=lowerCamelCase_       ,						tensor_type=lowerCamelCase_  )









import functools







def 		lowercase__   (		__A:	list[int]							,__A:	list[int]				):





					'''simple docstring'''






					if not isinstance(__A							,__A				) or not all(isinstance(__A							,__A				) for day in days				):
										raise ValueError('''The parameter days should be a list of integers'''				)

					if len(__A				) != 3 or not all(isinstance(__A							,__A				) for cost in costs				):
										raise ValueError('''The parameter costs should be a list of three integers'''				)

					if len(__A				) == 0:
										return 0

					if min(__A				) <= 0:
										raise ValueError('''All days elements should be greater than 0'''				)

					if max(__A				) >= 3_6_6:
										raise ValueError('''All days elements should be less than 366'''				)

					__magic_name__							:	Tuple    							=							set(__A				)

					@functools.cache
					def dynamic_programming(__A:	int				) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1				)

										return min(
										    costs[0] + dynamic_programming(index + 1				)							,costs[1] + dynamic_programming(index + 7				)							,costs[2] + dynamic_programming(index + 3_0				)							,)

					return dynamic_programming(1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



