


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__snake_case            =   logging.get_logger(__name__)


if is_vision_available():
			import PIL




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""pixel_values"""]




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







						"""simple docstring"""







						super().__init__(**__magic_name__						)
						UpperCamelCase         =					size if size is not None else {"""shortest_edge""": 2_2_4}
						UpperCamelCase         =					get_size_dict(__magic_name__       ,       default_to_square=__magic_name__						)
						UpperCamelCase         =					crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
						UpperCamelCase         =					get_size_dict(__magic_name__       ,       default_to_square=__magic_name__       ,       param_name="""crop_size"""						)

						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 if image_mean is not None else OPENAI_CLIP_MEAN
						UpperCamelCase         =					image_std if image_std is not None else OPENAI_CLIP_STD
						UpperCamelCase         =					do_convert_rgb




			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		Dict[str, int]       ,       __magic_name__							:		PILImageResampling = PILImageResampling.BICUBIC       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		Optional[int]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					get_size_dict(__magic_name__       ,       default_to_square=__magic_name__						)
						if "shortest_edge" not in size:
									raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
						UpperCamelCase         =					get_resize_output_image_size(__magic_name__       ,       size=size["""shortest_edge"""]       ,       default_to_square=__magic_name__						)
						return resize(__magic_name__       ,       size=__magic_name__       ,       resample=__magic_name__       ,       data_format=__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		Dict[str, int]       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					get_size_dict(__magic_name__						)
						if "height" not in size or "width" not in size:
									raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'						)
						return center_crop(__magic_name__       ,       size=(size["""height"""], size["""width"""])       ,       data_format=__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		Union[int, float]       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		Optional[Any]       ,       ):







						"""simple docstring"""







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




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







						"""simple docstring"""







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





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







						"""simple docstring"""







						UpperCamelCase         =					do_resize if do_resize is not None else self.do_resize
						UpperCamelCase         =					size if size is not None else self.size
						UpperCamelCase         =					get_size_dict(__magic_name__       ,       param_name="""size"""       ,       default_to_square=__magic_name__						)
						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         =					crop_size if crop_size is not None else self.crop_size
						UpperCamelCase         =					get_size_dict(__magic_name__       ,       param_name="""crop_size"""       ,       default_to_square=__magic_name__						)
						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         =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCamelCase         =					make_list_of_images(__magic_name__						)

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

						if do_resize and size is None:
									raise ValueError("""Size 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."""						)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
									UpperCamelCase         =					[convert_to_rgb(__magic_name__						) for image in images]

						# All transformations expect numpy arrays.
						UpperCamelCase         =					[to_numpy_array(__magic_name__						) for image in images]

						if do_resize:
									UpperCamelCase         =					[self.resize(image=__magic_name__       ,       size=__magic_name__       ,       resample=__magic_name__						) for image in images]

						if do_center_crop:
									UpperCamelCase         =					[self.center_crop(image=__magic_name__       ,       size=__magic_name__						) for image in images]

						if do_rescale:
									UpperCamelCase         =					[self.rescale(image=__magic_name__       ,       scale=__magic_name__						) for image in images]

						if do_normalize:
									UpperCamelCase         =					[self.normalize(image=__magic_name__       ,       mean=__magic_name__       ,       std=__magic_name__						) for image in images]

						UpperCamelCase         =					[to_channel_dimension_format(__magic_name__       ,       __magic_name__						) for image in images]

						UpperCamelCase         =					{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__       ,       tensor_type=__magic_name__						)








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			assert (
			    isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) 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
			UpperCamelCase  ,  UpperCamelCase         =					1, 1
			for _ in range(number_of_steps - 1	):
						UpperCamelCase  ,  UpperCamelCase         =					current + previous, current
			return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()




