




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


lowercase__							:	str											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''pixel_values''']






						def __init__(		self				:	List[str]			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	float = None			,      __lowercase				:	PILImageResampling = PILImageResampling.BILINEAR			,      __lowercase				:	bool = True			,      __lowercase				:	Union[int, float] = 1 / 2_55			,      __lowercase				:	bool = True			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      **__lowercase				:	int			,      ):






							"""simple docstring"""






							super().__init__(**__lowercase		)
							snake_case_						       =	size if size is not None else {"shortest_edge": 3_84}
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)

							snake_case_						       =	do_resize
							snake_case_						       =	size
							# Default value set here for backwards compatibility where the value in config is None
							snake_case_						       =	crop_pct if crop_pct is not None else 2_24 / 2_56
							snake_case_						       =	resample
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							snake_case_						       =	image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	np.ndarray			,      __lowercase				:	Dict[str, int]			,      __lowercase				:	float			,      __lowercase				:	PILImageResampling = PILImageResampling.BICUBIC			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)
							if "shortest_edge" not in size:
								raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
							snake_case_						       =	size["shortest_edge"]

							if shortest_edge < 3_84:
								# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
								snake_case_						       =	int(shortest_edge / crop_pct		)
								snake_case_						       =	get_resize_output_image_size(__lowercase			,      size=__lowercase			,      default_to_square=__lowercase		)
								snake_case_						       =	resize(image=__lowercase			,      size=__lowercase			,      resample=__lowercase			,      data_format=__lowercase			,      **__lowercase		)
								# then crop to (shortest_edge, shortest_edge)
								return center_crop(image=__lowercase			,      size=(shortest_edge, shortest_edge)			,      data_format=__lowercase			,      **__lowercase		)
							else:
								# warping (no cropping) when evaluated at 384 or larger
								return resize(
								    __lowercase			,      size=(shortest_edge, shortest_edge)			,      resample=__lowercase			,      data_format=__lowercase			,      **__lowercase		)






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






							"""simple docstring"""






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






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






							"""simple docstring"""






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






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






							"""simple docstring"""






							snake_case_						       =	do_resize if do_resize is not None else self.do_resize
							snake_case_						       =	crop_pct if crop_pct is not None else self.crop_pct
							snake_case_						       =	resample if resample is not None else self.resample
							snake_case_						       =	do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						       =	rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						       =	do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						       =	image_mean if image_mean is not None else self.image_mean
							snake_case_						       =	image_std if image_std is not None else self.image_std

							snake_case_						       =	size if size is not None else self.size
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)

							snake_case_						       =	make_list_of_images(__lowercase		)

							if not valid_images(__lowercase		):
								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_84 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.
							snake_case_						       =	[to_numpy_array(__lowercase		) for image in images]

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

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

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

							snake_case_						       =	[to_channel_dimension_format(__lowercase			,      __lowercase		) for image in images]

							snake_case_						       =	{"pixel_values": images}
							return BatchFeature(data=__lowercase			,      tensor_type=__lowercase		)






def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
