




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


lowercase__							:	int											=		logging.get_logger(__name__)


if is_vision_available():
		import PIL
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''pixel_values''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	PILImageResampling = PILImageResampling.BICUBIC			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __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				:	bool = True			,      **__lowercase				:	Optional[int]			,      ):






							"""simple docstring"""






							super().__init__(**__lowercase		)
							snake_case_						       =	size if size is not None else {"shortest_edge": 2_24}
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase		)
							snake_case_						       =	crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
							snake_case_						       =	get_size_dict(__lowercase			,      default_to_square=__lowercase			,      param_name="crop_size"		)

							snake_case_						       =	do_resize
							snake_case_						       =	size
							snake_case_						       =	resample
							snake_case_						       =	do_center_crop
							snake_case_						       =	crop_size
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							snake_case_						       =	image_std if image_std is not None else OPENAI_CLIP_STD
							snake_case_						       =	do_convert_rgb






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






							"""simple docstring"""






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






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






							"""simple docstring"""






							snake_case_						       =	get_size_dict(__lowercase		)
							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(__lowercase			,      size=(size["height"], size["width"])			,      data_format=__lowercase			,      **__lowercase		)






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






							"""simple docstring"""






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






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






							"""simple docstring"""






							snake_case_						       =	do_resize if do_resize is not None else self.do_resize
							snake_case_						       =	size if size is not None else self.size
							snake_case_						       =	get_size_dict(__lowercase			,      param_name="size"			,      default_to_square=__lowercase		)
							snake_case_						       =	resample if resample is not None else self.resample
							snake_case_						       =	do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_						       =	crop_size if crop_size is not None else self.crop_size
							snake_case_						       =	get_size_dict(__lowercase			,      param_name="crop_size"			,      default_to_square=__lowercase		)
							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_						       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							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:
								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:
								snake_case_						       =	[convert_to_rgb(__lowercase		) for image in images]

							# 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			,      resample=__lowercase		) for image in images]

							if do_center_crop:
								snake_case_						       =	[self.center_crop(image=__lowercase			,      size=__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		)






import numpy as np


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
		import doctest

		doctest.testmod()
