




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       int						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	Any					):

							'''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		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   ["pixel_values"]




			def __init__( self    ,							a__ = True    ,							a__ = None    ,							a__ = PILImageResampling.BILINEAR    ,							a__ = True    ,							a__ = None    ,							a__ = True    ,							a__ = 1 / 255    ,							a__ = True    ,							a__ = None    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										super().__init__(**a__							)
										snake_case_      =				size if size is not None else {"shortest_edge": 224}
										snake_case_      =				get_size_dict(a__    ,							default_to_square=a__							)
										snake_case_      =				crop_size if crop_size is not None else {"height": 224, "width": 224}
										snake_case_      =				get_size_dict(a__    ,							param_name="crop_size"							)

										snake_case_      =				do_resize
										snake_case_      =				size
										snake_case_      =				do_center_crop
										snake_case_      =				crop_size
										snake_case_      =				resample
										snake_case_      =				do_rescale
										snake_case_      =				rescale_factor
										snake_case_      =				do_normalize
										snake_case_      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case_      =				image_std if image_std is not None else IMAGENET_STANDARD_STD




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__ = PILImageResampling.BILINEAR    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




										snake_case_      =				get_size_dict(a__    ,							default_to_square=a__							)
										if "shortest_edge" in size:
																	snake_case_      =				get_resize_output_image_size(a__    ,							size["shortest_edge"]    ,							default_to_square=a__							)
										elif "height" in size and "width" in size:
																	snake_case_      =				(size["height"], size["width"])
										else:
																	raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'							)
										return resize(a__    ,							size=a__    ,							resample=a__    ,							data_format=a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




										snake_case_      =				get_size_dict(a__							)
										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(a__    ,							size=(size["height"], size["width"])    ,							data_format=a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__ = None    ,							**a__    ,							)    ->    List[str]:

										'''simple docstring'''




										return rescale(a__    ,							scale=a__    ,							data_format=a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = 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."							)

										# All transformations expect numpy arrays.
										snake_case_      =				to_numpy_array(a__							)

										if do_resize:
																	snake_case_      =				self.resize(image=a__    ,							size=a__    ,							resample=a__							)

										if do_center_crop:
																	snake_case_      =				self.center_crop(a__    ,							size=a__							)

										if do_rescale:
																	snake_case_      =				self.rescale(image=a__    ,							scale=a__							)

										if do_normalize:
																	snake_case_      =				self.normalize(image=a__    ,							mean=a__    ,							std=a__							)

										snake_case_      =				to_channel_dimension_format(a__    ,							a__							)
										return image




			def        lowerCAmelCase__    ( 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    ,							**a__    ,							)    ->    PIL.Image.Image:

										'''simple docstring'''




										snake_case_      =				do_resize if do_resize is not None else self.do_resize
										snake_case_      =				resample if resample is not None else self.resample
										snake_case_      =				do_center_crop if do_center_crop is not None else self.do_center_crop
										snake_case_      =				do_rescale if do_rescale is not None else self.do_rescale
										snake_case_      =				rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case_      =				do_normalize if do_normalize is not None else self.do_normalize
										snake_case_      =				image_mean if image_mean is not None else self.image_mean
										snake_case_      =				image_std if image_std is not None else self.image_std

										snake_case_      =				size if size is not None else self.size
										snake_case_      =				get_size_dict(a__    ,							default_to_square=a__							)
										snake_case_      =				crop_size if crop_size is not None else self.crop_size
										snake_case_      =				get_size_dict(a__    ,							param_name="crop_size"							)

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

										snake_case_      =				make_batched(a__							)

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

										snake_case_      =				{"pixel_values": videos}
										return BatchFeature(data=a__    ,							tensor_type=a__							)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	list[int]							,    snake_case							:	int					):

							'''simple docstring'''






							snake_case_      =				len(snake_case					)
							snake_case_      =				[[False] * (required_sum + 1) for _ in range(arr_len + 1					)]

							# for each arr value, a sum of zero(0) can be formed by not taking any element
							# hence True/1
							for i in range(arr_len + 1					):
														snake_case_      =				True

							# sum is not zero and set is empty then false
							for i in range(1							,    required_sum + 1					):
														snake_case_      =				False

							for i in range(1							,    arr_len + 1					):
														for j in range(1							,    required_sum + 1					):
																					if arr[i - 1] > j:
																												snake_case_      =				subset[i - 1][j]
																					if arr[i - 1] <= j:
																												snake_case_      =				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

							return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




