



'''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

UpperCamelCase__       :	Optional[Any]    = logging.get_logger(__name__)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]					):
		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 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Tuple			    =		['''pixel_values''']

			def __init__(							self     : str							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : PILImageResampling = PILImageResampling.BILINEAR							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Union[int, float] = 1 / 2_5_5							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     **lowerCAmelCase__     : Dict							,     ):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		size if size is not None else {"""shortest_edge""": 2_5_6}
					__SCREAMING_SNAKE_CASE       :		str           =		get_size_dict(lowerCAmelCase__							,     default_to_square=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_size_dict(lowerCAmelCase__							,     param_name="""crop_size"""						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		do_resize
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		do_center_crop
					__SCREAMING_SNAKE_CASE       :		List[str]           =		crop_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		resample
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		do_rescale
					__SCREAMING_SNAKE_CASE       :		Any           =		rescale_factor
					__SCREAMING_SNAKE_CASE       :		Any           =		offset
					__SCREAMING_SNAKE_CASE       :		int           =		do_normalize
					__SCREAMING_SNAKE_CASE       :		Any           =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__SCREAMING_SNAKE_CASE       :		Any           =		image_std if image_std is not None else IMAGENET_STANDARD_STD

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Dict[str, int]							,     lowerCAmelCase__     : PILImageResampling = PILImageResampling.BILINEAR							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Any							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		get_size_dict(lowerCAmelCase__							,     default_to_square=lowerCAmelCase__						)
					if "shortest_edge" in size:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		get_resize_output_image_size(lowerCAmelCase__							,     size["""shortest_edge"""]							,     default_to_square=lowerCAmelCase__						)
					elif "height" in size and "width" in size:
							__SCREAMING_SNAKE_CASE       :		str           =		(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 UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Dict[str, int]							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Dict							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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 UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Union[int, float]							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : List[Any]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		image.astype(np.floataa						)
					if offset:
							__SCREAMING_SNAKE_CASE       :		int           =		image - (scale / 2)
					return rescale(lowerCAmelCase__							,     scale=lowerCAmelCase__							,     data_format=lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Union[float, List[float]]							,     lowerCAmelCase__     : Union[float, List[float]]							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Union[str, Any]							,     ):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : ImageInput							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : PILImageResampling = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : float = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[ChannelDimension] = 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."""						)

					if offset and not do_rescale:
							raise ValueError("""For offset, do_rescale must also be set to True."""						)

					# All transformations expect numpy arrays.
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		to_numpy_array(lowerCAmelCase__						)

					if do_resize:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		self.resize(image=lowerCAmelCase__							,     size=lowerCAmelCase__							,     resample=lowerCAmelCase__						)

					if do_center_crop:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.center_crop(lowerCAmelCase__							,     size=lowerCAmelCase__						)

					if do_rescale:
							__SCREAMING_SNAKE_CASE       :		str           =		self.rescale(image=lowerCAmelCase__							,     scale=lowerCAmelCase__							,     offset=lowerCAmelCase__						)

					if do_normalize:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.normalize(image=lowerCAmelCase__							,     mean=lowerCAmelCase__							,     std=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Any           =		to_channel_dimension_format(lowerCAmelCase__							,     lowerCAmelCase__						)
					return image



			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : ImageInput							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : PILImageResampling = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : float = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[str, TensorType]] = None							,     lowerCAmelCase__     : ChannelDimension = ChannelDimension.FIRST							,     **lowerCAmelCase__     : Tuple							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		do_resize if do_resize is not None else self.do_resize
					__SCREAMING_SNAKE_CASE       :		Dict           =		resample if resample is not None else self.resample
					__SCREAMING_SNAKE_CASE       :		Dict           =		do_center_crop if do_center_crop is not None else self.do_center_crop
					__SCREAMING_SNAKE_CASE       :		Dict           =		do_rescale if do_rescale is not None else self.do_rescale
					__SCREAMING_SNAKE_CASE       :		Dict           =		rescale_factor if rescale_factor is not None else self.rescale_factor
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		offset if offset is not None else self.offset
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		do_normalize if do_normalize is not None else self.do_normalize
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		image_mean if image_mean is not None else self.image_mean
					__SCREAMING_SNAKE_CASE       :		Any           =		image_std if image_std is not None else self.image_std

					__SCREAMING_SNAKE_CASE       :		Dict           =		size if size is not None else self.size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		get_size_dict(lowerCAmelCase__							,     default_to_square=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		crop_size if crop_size is not None else self.crop_size
					__SCREAMING_SNAKE_CASE       :		Optional[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."""						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		make_batched(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[
					    [
					        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
					]

					__SCREAMING_SNAKE_CASE       :		Dict           =		{"""pixel_values""": videos}
					return BatchFeature(data=lowerCAmelCase__							,     tensor_type=lowerCAmelCase__						)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		assert (
		    isinstance(_lowerCamelCase       ,      _lowerCamelCase					) and number_of_steps > 0
		), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
		if number_of_steps == 1:
				return 1
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		1, 1
		for _ in range(number_of_steps - 1					):
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		current + previous, current
		return current


if __name__ == "__main__":
						import doctest

						doctest.testmod()