

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_      =     logging.get_logger(__name__)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =['pixel_values']
			def __init__(      self				:			Tuple   ,   a				:			bool = True   ,   a				:			Optional[Dict[str, int]] = None   ,   a				:			PILImageResampling = PILImageResampling.BILINEAR   ,   a				:			bool = True   ,   a				:			Dict[str, int] = None   ,   a				:			bool = True   ,   a				:			Union[int, float] = 1 / 255   ,   a				:			bool = True   ,   a				:			Optional[Union[float, List[float]]] = None   ,   a				:			Optional[Union[float, List[float]]] = None   ,   **a				:			List[str]   ,   )				->							None:

				"""simple docstring"""

				super().__init__(**a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							size if size is not None else {"shortest_edge": 256}
				SCREAMING_SNAKE_CASE :   Tuple					  =							get_size_dict(a   ,   default_to_square=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							crop_size if crop_size is not None else {"height": 224, "width": 224}
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							get_size_dict(a						)
				SCREAMING_SNAKE_CASE :   str					  =							do_resize
				SCREAMING_SNAKE_CASE :   List[str]					  =							size
				SCREAMING_SNAKE_CASE :   List[str]					  =							resample
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							do_center_crop
				SCREAMING_SNAKE_CASE :   int					  =							crop_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							do_rescale
				SCREAMING_SNAKE_CASE :   str					  =							rescale_factor
				SCREAMING_SNAKE_CASE :   List[str]					  =							do_normalize
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				SCREAMING_SNAKE_CASE :   int					  =							image_std if image_std is not None else IMAGENET_STANDARD_STD
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			np.ndarray   ,   a				:			Dict[str, int]   ,   a				:			PILImageResampling = PILImageResampling.BICUBIC   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			str   ,   )				->							np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							get_size_dict(a   ,   default_to_square=a						)
				if "shortest_edge" not in size:
					raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
				SCREAMING_SNAKE_CASE :   str					  =							get_resize_output_image_size(a   ,   size=size["shortest_edge"]   ,   default_to_square=a						)
				return resize(a   ,   size=a   ,   resample=a   ,   data_format=a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			np.ndarray   ,   a				:			Dict[str, int]   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			str   ,   )				->							np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							get_size_dict(a						)
				return center_crop(a   ,   size=(size["height"], size["width"])   ,   data_format=a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			np.ndarray   ,   a				:			float   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			List[str]						)				->							np.ndarray:

				"""simple docstring"""

				return rescale(a   ,   scale=a   ,   data_format=a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			np.ndarray   ,   a				:			Union[float, List[float]]   ,   a				:			Union[float, List[float]]   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			int   ,   )				->							np.ndarray:

				"""simple docstring"""

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






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE :   Any					  =							size if size is not None else self.size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							get_size_dict(a   ,   default_to_square=a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							do_center_crop if do_center_crop is not None else self.do_center_crop
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							crop_size if crop_size is not None else self.crop_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							get_size_dict(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							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]					  =							do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_std if image_std is not None else self.image_std

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							make_list_of_images(a						)

				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."						)

				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."						)

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE :   Dict					  =							[to_numpy_array(a						) for image in images]

				if do_resize:
					SCREAMING_SNAKE_CASE :   Dict					  =							[self.resize(image=a   ,   size=a   ,   resample=a						) for image in images]

				if do_center_crop:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							[self.center_crop(image=a   ,   size=a						) for image in images]

				if do_rescale:
					SCREAMING_SNAKE_CASE :   List[str]					  =							[self.rescale(image=a   ,   scale=a						) for image in images]

				if do_normalize:
					SCREAMING_SNAKE_CASE :   str					  =							[self.normalize(image=a   ,   mean=a   ,   std=a						) for image in images]

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[to_channel_dimension_format(a   ,   a						) for image in images]

				SCREAMING_SNAKE_CASE :   Tuple					  =							{"pixel_values": images}
				return BatchFeature(data=a   ,   tensor_type=a						)


def 			lowerCamelCase__			(       _a):
	if not isinstance(_a				,      _a):
		SCREAMING_SNAKE_CASE :   Tuple					  =							f"Input value of [number={number}] must be an integer"
		raise TypeError(_a)
	if number < 0:
		return False
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()