

"""simple docstring"""




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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__A  :					str												=	logging.get_logger(__name__)


if is_vision_available():
	import PIL





class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					["pixel_values"]


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

							A    =      size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
							if isinstance(__SCREAMING_SNAKE_CASE  ,  (tuple, list)):
												# The previous feature extractor size parameter was in (width, height) format
												A    =      size[::-1]
							A    =      get_size_dict(__SCREAMING_SNAKE_CASE)

							A    =      do_resize
							A    =      size
							A    =      resample
							A    =      do_thumbnail
							A    =      do_align_long_axis
							A    =      do_pad
							A    =      do_rescale
							A    =      rescale_factor
							A    =      do_normalize
							A    =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A    =      image_std if image_std is not None else IMAGENET_STANDARD_STD


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, int]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None):
							A    ,					A    =      get_image_size(__SCREAMING_SNAKE_CASE)
							A    ,					A    =      size["height"], size["width"]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
												A    =      np.rotaa(__SCREAMING_SNAKE_CASE  ,  3)

							if data_format is not None:
												A    =      to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)

							return image


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, int]  ,  __SCREAMING_SNAKE_CASE				:	bool = False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None  ,  ):
							A    ,					A    =      size["height"], size["width"]
							A    ,					A    =      get_image_size(__SCREAMING_SNAKE_CASE)

							A    =      output_width - input_width
							A    =      output_height - input_height

							if random_padding:
												A    =      np.random.randint(low=0  ,  high=delta_height + 1)
												A    =      np.random.randint(low=0  ,  high=delta_width + 1)
							else:
												A    =      delta_height // 2
												A    =      delta_width // 2

							A    =      delta_height - pad_top
							A    =      delta_width - pad_left

							A    =      ((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  *__SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  **__SCREAMING_SNAKE_CASE				:	int):
							logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
							return self.pad_image(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, int]  ,  __SCREAMING_SNAKE_CASE				:	PILImageResampling = PILImageResampling.BICUBIC  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
							A    ,					A    =      get_image_size(__SCREAMING_SNAKE_CASE)
							A    ,					A    =      size["height"], size["width"]

							# We always resize to the smallest of either the input or output size.
							A    =      min(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							A    =      min(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)

							if height == input_height and width == input_width:
												return image

							if input_height > input_width:
												A    =      int(input_width * height / input_height)
							elif input_width > input_height:
												A    =      int(input_height * width / input_width)

							return resize(
							    __SCREAMING_SNAKE_CASE  ,  size=(height, width)  ,  resample=__SCREAMING_SNAKE_CASE  ,  reducing_gap=2.0  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, int]  ,  __SCREAMING_SNAKE_CASE				:	PILImageResampling = PILImageResampling.BICUBIC  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None  ,  **__SCREAMING_SNAKE_CASE				:	Any  ,  ):
							A    =      get_size_dict(__SCREAMING_SNAKE_CASE)
							A    =      min(size["height"]  ,  size["width"])
							A    =      get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE  ,  default_to_square=__SCREAMING_SNAKE_CASE)
							A    =      resize(__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE  ,  resample=__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
							return resized_image


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Union[int, float]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None  ,  **__SCREAMING_SNAKE_CASE				:	Tuple  ,  ):
							return rescale(__SCREAMING_SNAKE_CASE  ,  scale=__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	np.ndarray  ,  __SCREAMING_SNAKE_CASE				:	Union[float, List[float]]  ,  __SCREAMING_SNAKE_CASE				:	Union[float, List[float]]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, ChannelDimension]] = None  ,  **__SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  ):
							return normalize(__SCREAMING_SNAKE_CASE  ,  mean=__SCREAMING_SNAKE_CASE  ,  std=__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	ImageInput  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, int] = None  ,  __SCREAMING_SNAKE_CASE				:	PILImageResampling = None  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	bool = False  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  __SCREAMING_SNAKE_CASE				:	bool = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[float, List[float]]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[float, List[float]]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, TensorType]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[ChannelDimension] = ChannelDimension.FIRST  ,  **__SCREAMING_SNAKE_CASE				:	Any  ,  ):
							A    =      do_resize if do_resize is not None else self.do_resize
							A    =      size if size is not None else self.size
							if isinstance(__SCREAMING_SNAKE_CASE  ,  (tuple, list)):
												# Previous feature extractor had size in (width, height) format
												A    =      size[::-1]
							A    =      get_size_dict(__SCREAMING_SNAKE_CASE)
							A    =      resample if resample is not None else self.resample
							A    =      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							A    =      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							A    =      do_pad if do_pad is not None else self.do_pad
							A    =      do_rescale if do_rescale is not None else self.do_rescale
							A    =      rescale_factor if rescale_factor is not None else self.rescale_factor
							A    =      do_normalize if do_normalize is not None else self.do_normalize
							A    =      image_mean if image_mean is not None else self.image_mean
							A    =      image_std if image_std is not None else self.image_std

							A    =      make_list_of_images(__SCREAMING_SNAKE_CASE)

							if not valid_images(__SCREAMING_SNAKE_CASE):
												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_rescale and rescale_factor is None:
												raise ValueError("Rescale factor must be specified if do_rescale is True.")

							if do_pad and size is None:
												raise ValueError("Size must be specified if do_pad 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.
							A    =      [to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]

							if do_align_long_axis:
												A    =      [self.align_long_axis(__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE) for image in images]

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

							if do_thumbnail:
												A    =      [self.thumbnail(image=__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE) for image in images]

							if do_pad:
												A    =      [self.pad_image(image=__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE  ,  random_padding=__SCREAMING_SNAKE_CASE) for image in images]

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

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

							A    =      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) for image in images]

							A    =      {"pixel_values": images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,  tensor_type=__SCREAMING_SNAKE_CASE)








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if not isinstance(lowercase__    ,							lowercase__   ):
										raise ValueError("double_factorial() only accepts integral values"   )
					if n < 0:
										raise ValueError("double_factorial() not defined for negative values"   )
					return 1 if n <= 1 else n * double_factorial(n - 2   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





