


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    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				_A   :List[Any]  )->		List[List[ImageInput]]:
			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__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[int]    =		['pixel_values']




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

						'''simple docstring'''


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

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = PILImageResampling.BILINEAR      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						if "shortest_edge" in size:
									UpperCamelCase__					=       get_resize_output_image_size(snake_case      ,						size["shortest_edge"]      ,						default_to_square=snake_case     )
						elif "height" in size and "width" in size:
									UpperCamelCase__					=       (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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = ChannelDimension.FIRST      ,						):

						'''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.
						UpperCamelCase__					=       to_numpy_array(snake_case     )

						if do_resize:
									UpperCamelCase__					=       self.resize(image=snake_case      ,						size=snake_case      ,						resample=snake_case     )

						if do_center_crop:
									UpperCamelCase__					=       self.center_crop(snake_case      ,						size=snake_case     )

						if do_rescale:
									UpperCamelCase__					=       self.rescale(image=snake_case      ,						scale=snake_case     )

						if do_normalize:
									UpperCamelCase__					=       self.normalize(image=snake_case      ,						mean=snake_case      ,						std=snake_case     )

						UpperCamelCase__					=       to_channel_dimension_format(snake_case      ,						snake_case     )
						return image







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = ChannelDimension.FIRST      ,						**snake_case      ,						):

						'''simple docstring'''


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

						UpperCamelCase__					=       size if size is not None else self.size
						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						UpperCamelCase__					=       crop_size if crop_size is not None else self.crop_size
						UpperCamelCase__					=       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."     )

						UpperCamelCase__					=       make_batched(snake_case     )

						UpperCamelCase__					=       [
						    [
						        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      ,						do_normalize=snake_case      ,						image_mean=snake_case      ,						image_std=snake_case      ,						data_format=snake_case      ,						)
						        for img in video
						    ]
						    for video in videos
						]

						UpperCamelCase__					=       {"pixel_values": videos}
						return BatchFeature(data=snake_case      ,						tensor_type=snake_case     )






def        UpperCamelCase_(				_A   :List[str]				,     _A   :Tuple				,     _A   :Any				,     _A   :Tuple				,     _A   :Optional[int]				,     _A   :str  )->		List[str]:
			if index == r:
						for j in range(_A  ):
									print(data[j]				,     end=" "  )
						print(" "  )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			UpperCamelCase__					=       arr[i]
			combination_util(_A				,     _A				,     _A				,     index + 1				,     _A				,     i + 1  )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(_A				,     _A				,     _A				,     _A				,     _A				,     i + 1  )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()
def        UpperCamelCase_(				_A   :Dict				,     _A   :Optional[Any]				,     _A   :Union[str, Any]  )->		Optional[int]:
			# A temporary array to store all combination one by one
			UpperCamelCase__					=       [0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(_A				,     _A				,     _A				,     0				,     _A				,     0  )


if __name__ == "__main__":
						# Driver code to check the function above
						__UpperCamelCase    		=       [1_0, 2_0, 3_0, 4_0, 5_0]
						print_combination(arr, len(arr), 3)
						# This code is contributed by Ambuj sahu


