






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					:		Tuple	  =						logging.get_logger(__name__)
def      a_ (      _A  )			->			List[List[ImageInput]]:







				"""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   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       ["pixel_values"]
							def __init__(     self:      Any       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      PILImageResampling = PILImageResampling.BILINEAR       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      Union[int, float] = 1 / 2_55       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	**UpperCamelCase:      str       ,	)		->		None:
											super().__init__(**UpperCamelCase						)
											snake_case__									=     size if size is not None else {'shortest_edge': 2_24}
											snake_case__									=     get_size_dict(UpperCamelCase       ,	default_to_square=UpperCamelCase						)
											snake_case__									=     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
											snake_case__									=     get_size_dict(UpperCamelCase       ,	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:      Dict       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      Dict[str, int]       ,	UpperCamelCase:      PILImageResampling = PILImageResampling.BILINEAR       ,	UpperCamelCase:      Optional[Union[str, ChannelDimension]] = None       ,	**UpperCamelCase:      Any       ,	)		->		np.ndarray:
											snake_case__									=     get_size_dict(UpperCamelCase       ,	default_to_square=UpperCamelCase						)
											if "shortest_edge" in size:
															snake_case__									=     get_resize_output_image_size(UpperCamelCase       ,	size['shortest_edge']       ,	default_to_square=UpperCamelCase						)
											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(UpperCamelCase       ,	size=UpperCamelCase       ,	resample=UpperCamelCase       ,	data_format=UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      Dict[str, int]       ,	UpperCamelCase:      Optional[Union[str, ChannelDimension]] = None       ,	**UpperCamelCase:      str       ,	)		->		np.ndarray:
											snake_case__									=     get_size_dict(UpperCamelCase						)
											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(UpperCamelCase       ,	size=(size['height'], size['width'])       ,	data_format=UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      Union[int, float]       ,	UpperCamelCase:      Optional[Union[str, ChannelDimension]] = None       ,	**UpperCamelCase:      Union[str, Any]       ,	)		->		List[str]:
											return rescale(UpperCamelCase       ,	scale=UpperCamelCase       ,	data_format=UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      Union[float, List[float]]       ,	UpperCamelCase:      Union[float, List[float]]       ,	UpperCamelCase:      Optional[Union[str, ChannelDimension]] = None       ,	**UpperCamelCase:      Union[str, Any]       ,	)		->		np.ndarray:
											return normalize(UpperCamelCase       ,	mean=UpperCamelCase       ,	std=UpperCamelCase       ,	data_format=UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      ImageInput       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      PILImageResampling = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      float = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	UpperCamelCase:      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.
											snake_case__									=     to_numpy_array(UpperCamelCase						)

											if do_resize:
															snake_case__									=     self.resize(image=UpperCamelCase       ,	size=UpperCamelCase       ,	resample=UpperCamelCase						)

											if do_center_crop:
															snake_case__									=     self.center_crop(UpperCamelCase       ,	size=UpperCamelCase						)

											if do_rescale:
															snake_case__									=     self.rescale(image=UpperCamelCase       ,	scale=UpperCamelCase						)

											if do_normalize:
															snake_case__									=     self.normalize(image=UpperCamelCase       ,	mean=UpperCamelCase       ,	std=UpperCamelCase						)

											snake_case__									=     to_channel_dimension_format(UpperCamelCase       ,	UpperCamelCase						)
											return image


							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      ImageInput       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      PILImageResampling = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Dict[str, int] = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      float = None       ,	UpperCamelCase:      bool = None       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	UpperCamelCase:      Optional[Union[float, List[float]]] = None       ,	UpperCamelCase:      Optional[Union[str, TensorType]] = None       ,	UpperCamelCase:      ChannelDimension = ChannelDimension.FIRST       ,	**UpperCamelCase:      Union[str, Any]       ,	)		->		PIL.Image.Image:
											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(UpperCamelCase       ,	default_to_square=UpperCamelCase						)
											snake_case__									=     crop_size if crop_size is not None else self.crop_size
											snake_case__									=     get_size_dict(UpperCamelCase       ,	param_name='crop_size'						)

											if not valid_images(UpperCamelCase						):
															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(UpperCamelCase						)

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

											snake_case__									=     {'pixel_values': videos}
											return BatchFeature(data=UpperCamelCase       ,	tensor_type=UpperCamelCase						)












def      a_ (      _A = 4000000  )			->			int:







				"""simple docstring"""
				snake_case__									=     [0, 1]
				snake_case__									=     0
				while fib[i] <= n:
								fib.append(fib[i] + fib[i + 1]  )
								if fib[i + 2] > n:
												break
								i += 1
				snake_case__									=     0
				for j in range(len(_A  ) - 1  ):
								if fib[j] % 2 == 0:
												total += fib[j]

				return total


if __name__ == "__main__":
							print(f'''{solution() = }''')




