



from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     lowercase__				):
	SCREAMING_SNAKE_CASE       =       ["pixel_values"]



	def __init__(						self							:    Dict				,	snake_case_							:    bool = True				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    PILImageResampling = PILImageResampling.BICUBIC				,	snake_case_							:    bool = True				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    bool = True				,	snake_case_							:    Union[int, float] = 1 / 2_55				,	snake_case_							:    bool = True				,	snake_case_							:    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN				,	snake_case_							:    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD				,	**snake_case_							:    int				,	)->							None:
						super().__init__(**snake_case_)
						__lowerCAmelCase	   =size if size is not None else {"shortest_edge": 2_24}
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						__lowerCAmelCase	   =crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	param_name="""crop_size""")

						__lowerCAmelCase	   =do_resize
						__lowerCAmelCase	   =size
						__lowerCAmelCase	   =resample
						__lowerCAmelCase	   =do_center_crop
						__lowerCAmelCase	   =crop_size
						__lowerCAmelCase	   =do_rescale
						__lowerCAmelCase	   =rescale_factor
						__lowerCAmelCase	   =do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__lowerCAmelCase	   =image_std if image_std is not None else IMAGENET_DEFAULT_STD



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    np.ndarray				,	snake_case_							:    Dict[str, int]				,	snake_case_							:    PILImageResampling = PILImageResampling.BICUBIC				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Any				,	)->							np.ndarray:
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
											__lowerCAmelCase	   =int((2_56 / 2_24) * size["""shortest_edge"""])
											__lowerCAmelCase	   =get_resize_output_image_size(snake_case_				,	size=snake_case_				,	default_to_square=snake_case_)
											__lowerCAmelCase	   ={"height": output_size[0], "width": output_size[1]}
						if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""")
						return resize(
						    snake_case_				,	size=(size_dict["""height"""], size_dict["""width"""])				,	resample=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    np.ndarray				,	snake_case_							:    Dict[str, int]				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Optional[Any]				,	)->							np.ndarray:
						__lowerCAmelCase	   =get_size_dict(snake_case_)
						if "height" not in size or "width" not in size:
											raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""")
						return center_crop(snake_case_				,	size=(size["""height"""], size["""width"""])				,	data_format=snake_case_				,	**snake_case_)



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



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



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    ImageInput				,	snake_case_							:    Optional[bool] = None				,	snake_case_							:    Optional[Dict[str, int]] = None				,	snake_case_							:    PILImageResampling = None				,	snake_case_							:    Optional[bool] = None				,	snake_case_							:    Optional[Dict[str, int]] = None				,	snake_case_							:    Optional[bool] = None				,	snake_case_							:    Optional[float] = None				,	snake_case_							:    Optional[bool] = None				,	snake_case_							:    Optional[Union[float, Iterable[float]]] = None				,	snake_case_							:    Optional[Union[float, Iterable[float]]] = None				,	snake_case_							:    Optional[TensorType] = None				,	snake_case_							:    ChannelDimension = ChannelDimension.FIRST				,	**snake_case_							:    Tuple				,	)->							BatchFeature:
						__lowerCAmelCase	   =do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase	   =resample if resample is not None else self.resample
						__lowerCAmelCase	   =do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase	   =do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase	   =rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase	   =do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase	   =image_std if image_std is not None else self.image_std

						__lowerCAmelCase	   =size if size is not None else self.size
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						__lowerCAmelCase	   =crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	param_name="""crop_size""")

						__lowerCAmelCase	   =make_list_of_images(snake_case_)

						if not valid_images(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_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.
						__lowerCAmelCase	   =[to_numpy_array(snake_case_) for image in images]

						if do_resize:
											__lowerCAmelCase	   =[self.resize(snake_case_				,	snake_case_				,	snake_case_) for image in images]

						if do_center_crop:
											__lowerCAmelCase	   =[self.center_crop(snake_case_				,	snake_case_) for image in images]

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

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

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

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









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


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

				testmod()




