


"""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 (
    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, is_vision_available, logging


if is_vision_available():
		import PIL


a__				:							List[Any]						=     logging.get_logger(__name__)




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[str]    					=			["pixel_values"]




	def __init__( self			:				List[Any]			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				float = None			,			UpperCAmelCase__			:				PILImageResampling = PILImageResampling.BILINEAR			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Union[int, float] = 1 / 2_5_5			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	None:
						super().__init__(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				size if size is not None else {"shortest_edge": 3_8_4}
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				do_resize
						__SCREAMING_SNAKE_CASE						   =				size
						# Default value set here for backwards compatibility where the value in config is None
						__SCREAMING_SNAKE_CASE						   =				crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
						__SCREAMING_SNAKE_CASE						   =				resample
						__SCREAMING_SNAKE_CASE						   =				do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_normalize
						__SCREAMING_SNAKE_CASE						   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE						   =				image_std if image_std is not None else IMAGENET_STANDARD_STD




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Dict[str, int]			,			UpperCAmelCase__			:				float			,			UpperCAmelCase__			:				PILImageResampling = PILImageResampling.BICUBIC			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Tuple			,			)       ->	np.ndarray:
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
						if "shortest_edge" not in size:
											raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""   )
						__SCREAMING_SNAKE_CASE						   =				size["shortest_edge"]

						if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__SCREAMING_SNAKE_CASE						   =				int(shortest_edge / crop_pct   )
											__SCREAMING_SNAKE_CASE						   =				get_resize_output_image_size(UpperCAmelCase__			,			size=UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				resize(image=UpperCAmelCase__			,			size=UpperCAmelCase__			,			resample=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=UpperCAmelCase__			,			size=(shortest_edge, shortest_edge)			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )
						else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    UpperCAmelCase__			,			size=(shortest_edge, shortest_edge)			,			resample=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




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




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Union[float, List[float]]			,			UpperCAmelCase__			:				Union[float, List[float]]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Dict			,			)       ->	np.ndarray:
						return normalize(UpperCAmelCase__			,			mean=UpperCAmelCase__			,			std=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				ImageInput			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				float = None			,			UpperCAmelCase__			:				PILImageResampling = 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__			:				Tuple			,			)       ->	PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						   =				do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						   =				crop_pct if crop_pct is not None else self.crop_pct
						__SCREAMING_SNAKE_CASE						   =				resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						   =				do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE						   =				image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE						   =				image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE						   =				size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				make_list_of_images(UpperCAmelCase__   )

						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."   )

						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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError("crop_pct must be specified if size < 384."   )

						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						   =				[to_numpy_array(UpperCAmelCase__   ) for image in images]

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

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

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

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

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




"""simple docstring"""







from __future__ import annotations




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None   ):




					'''simple docstring'''


					if start is None:
										__SCREAMING_SNAKE_CASE						   =				0

					if end is None:
										__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   ) - 1

					if start >= end:
										return

					__SCREAMING_SNAKE_CASE						   =				(start + end) // 2

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					slowsort(lowerCAmelCase_  ,						mid + 1  ,						lowerCAmelCase_   )

					if sequence[end] < sequence[mid]:
										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				sequence[mid], sequence[end]

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						end - 1   )


if __name__ == "__main__":
		from doctest import testmod

		testmod()
