




"""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

a_				:    Any										=     logging.get_logger(__name__)





def 						UpperCAmelCase				(			A__:    int     )  ->     Dict:
			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 __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= ['pixel_values']
					def __init__(    self						,	__a = True						,	__a = None						,	__a = PILImageResampling.BILINEAR						,	__a = True						,	__a = None						,	__a = True						,	__a = 1 / 255						,	__a = True						,	__a = True						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(**_lowerCAmelCase     )
								__lowerCamelCase					:	Optional[Any]	       =				size if size is not None else {'shortest_edge': 256}
								__lowerCamelCase					:	Optional[int]	       =				get_size_dict(_lowerCAmelCase						,	default_to_square=_lowerCAmelCase     )
								__lowerCamelCase					:	List[Any]	       =				crop_size if crop_size is not None else {'height': 224, 'width': 224}
								__lowerCamelCase					:	Tuple	       =				get_size_dict(_lowerCAmelCase						,	param_name='crop_size'     )

								__lowerCamelCase					:	Dict	       =				do_resize
								__lowerCamelCase					:	Union[str, Any]	       =				size
								__lowerCamelCase					:	str	       =				do_center_crop
								__lowerCamelCase					:	int	       =				crop_size
								__lowerCamelCase					:	List[str]	       =				resample
								__lowerCamelCase					:	Optional[Any]	       =				do_rescale
								__lowerCamelCase					:	List[str]	       =				rescale_factor
								__lowerCamelCase					:	str	       =				offset
								__lowerCamelCase					:	List[str]	       =				do_normalize
								__lowerCamelCase					:	Any	       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowerCamelCase					:	Dict	       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
					def     snake_case_				(    self						,	__a						,	__a						,	__a = PILImageResampling.BILINEAR						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	Tuple	       =				get_size_dict(_lowerCAmelCase						,	default_to_square=_lowerCAmelCase     )
								if "shortest_edge" in size:
											__lowerCamelCase					:	List[Any]	       =				get_resize_output_image_size(_lowerCAmelCase						,	size['shortest_edge']						,	default_to_square=_lowerCAmelCase     )
								elif "height" in size and "width" in size:
											__lowerCamelCase					:	Dict	       =				(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     snake_case_				(    self						,	__a						,	__a						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	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     snake_case_				(    self						,	__a						,	__a						,	__a = True						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	int	       =				image.astype(np.floataa     )
								if offset:
											__lowerCamelCase					:	int	       =				image - (scale / 2)
								return rescale(_lowerCAmelCase						,	scale=_lowerCAmelCase						,	data_format=_lowerCAmelCase						,	**_lowerCAmelCase     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a = None						,	**__a						,	):
								return normalize(_lowerCAmelCase						,	mean=_lowerCAmelCase						,	std=_lowerCAmelCase						,	data_format=_lowerCAmelCase						,	**_lowerCAmelCase     )
					def     snake_case_				(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = ChannelDimension.FIRST						,	):
								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.
								__lowerCamelCase					:	Union[str, Any]	       =				to_numpy_array(_lowerCAmelCase     )

								if do_resize:
											__lowerCamelCase					:	Optional[int]	       =				self.resize(image=_lowerCAmelCase						,	size=_lowerCAmelCase						,	resample=_lowerCAmelCase     )

								if do_center_crop:
											__lowerCamelCase					:	List[Any]	       =				self.center_crop(_lowerCAmelCase						,	size=_lowerCAmelCase     )

								if do_rescale:
											__lowerCamelCase					:	List[str]	       =				self.rescale(image=_lowerCAmelCase						,	scale=_lowerCAmelCase						,	offset=_lowerCAmelCase     )

								if do_normalize:
											__lowerCamelCase					:	str	       =				self.normalize(image=_lowerCAmelCase						,	mean=_lowerCAmelCase						,	std=_lowerCAmelCase     )

								__lowerCamelCase					:	List[Any]	       =				to_channel_dimension_format(_lowerCAmelCase						,	_lowerCAmelCase     )
								return image





					def     snake_case_				(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = ChannelDimension.FIRST						,	**__a						,	):
								__lowerCamelCase					:	Tuple	       =				do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase					:	List[str]	       =				resample if resample is not None else self.resample
								__lowerCamelCase					:	Union[str, Any]	       =				do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase					:	Optional[int]	       =				do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase					:	Tuple	       =				rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase					:	Union[str, Any]	       =				offset if offset is not None else self.offset
								__lowerCamelCase					:	List[str]	       =				do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase					:	Optional[Any]	       =				image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase					:	int	       =				image_std if image_std is not None else self.image_std

								__lowerCamelCase					:	Optional[int]	       =				size if size is not None else self.size
								__lowerCamelCase					:	Tuple	       =				get_size_dict(_lowerCAmelCase						,	default_to_square=_lowerCAmelCase     )
								__lowerCamelCase					:	Union[str, Any]	       =				crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase					:	Union[str, Any]	       =				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.'     )

								__lowerCamelCase					:	Tuple	       =				make_batched(_lowerCAmelCase     )

								__lowerCamelCase					:	Optional[int]	       =				[
								    [
								        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						,	offset=_lowerCAmelCase						,	do_normalize=_lowerCAmelCase						,	image_mean=_lowerCAmelCase						,	image_std=_lowerCAmelCase						,	data_format=_lowerCAmelCase						,	)
								        for img in video
								    ]
								    for video in videos
								]

								__lowerCamelCase					:	Optional[int]	       =				{'pixel_values': videos}
								return BatchFeature(data=_lowerCAmelCase						,	tensor_type=_lowerCAmelCase     )









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



