
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


lowercase__	:Tuple										=					logging.get_logger(__name__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					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	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =['''pixel_values''']




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PILImageResampling.BILINEAR     ,A__ = True     ,A__ = None     ,A__ = True     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = None     ,A__ = None     ,**A__     ,):
												super().__init__(**A__)
												lowercase					=		size if size is not None else {'''shortest_edge''': 2_2_4}
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												lowercase					=		crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
												lowercase					=		get_size_dict(A__     ,param_name='''crop_size''')

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




							def 				A__   (       self     ,A__     ,A__     ,A__ = PILImageResampling.BILINEAR     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												if "shortest_edge" in size:
																	lowercase					=		get_resize_output_image_size(A__     ,size['''shortest_edge''']     ,default_to_square=A__)
												elif "height" in size and "width" in size:
																	lowercase					=		(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 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return rescale(A__     ,scale=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return normalize(A__     ,mean=A__     ,std=A__     ,data_format=A__     ,**A__)




							def 				A__   (       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     ,):
												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.
												lowercase					=		to_numpy_array(A__)

												if do_resize:
																	lowercase					=		self.resize(image=A__     ,size=A__     ,resample=A__)

												if do_center_crop:
																	lowercase					=		self.center_crop(A__     ,size=A__)

												if do_rescale:
																	lowercase					=		self.rescale(image=A__     ,scale=A__)

												if do_normalize:
																	lowercase					=		self.normalize(image=A__     ,mean=A__     ,std=A__)

												lowercase					=		to_channel_dimension_format(A__     ,A__)
												return image





							def 				A__   (       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__     ,):
												lowercase					=		do_resize if do_resize is not None else self.do_resize
												lowercase					=		resample if resample is not None else self.resample
												lowercase					=		do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
												lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase					=		do_normalize if do_normalize is not None else self.do_normalize
												lowercase					=		image_mean if image_mean is not None else self.image_mean
												lowercase					=		image_std if image_std is not None else self.image_std

												lowercase					=		size if size is not None else self.size
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												lowercase					=		crop_size if crop_size is not None else self.crop_size
												lowercase					=		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.''')

												lowercase					=		make_batched(A__)

												lowercase					=		[
												    [
												        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
												]

												lowercase					=		{'''pixel_values''': videos}
												return BatchFeature(data=A__     ,tensor_type=A__)






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




