




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


SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							logging.get_logger(__name__)


if is_vision_available():
 import PIL







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



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

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






         """simple docstring"""
         super().__init__(**A     )
         a__  			=							size if size is not None else {"""shortest_edge""": 2_24}
         a__  			=							get_size_dict(A    ,					default_to_square=A     )
         a__  			=							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
         a__  			=							get_size_dict(A     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							np.ndarray    ,					A			:							Dict[str, int]    ,					A			:							PILImageResampling = PILImageResampling.BICUBIC    ,					A			:							Optional[Union[str, ChannelDimension]] = None    ,					**A			:							str    ,					)      ->      np.ndarray:






         """simple docstring"""
         a__  			=							get_size_dict(A    ,					default_to_square=A     )
         a__  			=							get_resize_output_image_size(
             A    ,					size=(size["""height"""], size["""width"""])    ,					default_to_square=A     )
         return resize(A    ,					size=A    ,					resample=A    ,					data_format=A    ,					**A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							np.ndarray    ,					A			:							Dict[str, int]    ,					A			:							Optional[Union[str, ChannelDimension]] = None    ,					**A			:							int    ,					)      ->      np.ndarray:






         """simple docstring"""
         a__  			=							get_size_dict(A     )
         return center_crop(A    ,					size=(size["""height"""], size["""width"""])    ,					data_format=A    ,					**A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							np.ndarray    ,					A			:							Union[int, float]    ,					A			:							Optional[Union[str, ChannelDimension]] = None    ,					**A			:							Tuple    ,					)      ->      Optional[int]:






         """simple docstring"""
         return rescale(A    ,					scale=A    ,					data_format=A    ,					**A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							np.ndarray    ,					A			:							Union[float, List[float]]    ,					A			:							Union[float, List[float]]    ,					A			:							Optional[Union[str, ChannelDimension]] = None    ,					**A			:							Tuple    ,					)      ->      np.ndarray:






         """simple docstring"""
         return normalize(A    ,					mean=A    ,					std=A    ,					data_format=A    ,					**A     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							ImageInput    ,					A			:							bool = None    ,					A			:							Dict[str, int] = None    ,					A			:							PILImageResampling = None    ,					A			:							bool = None    ,					A			:							int = None    ,					A			:							bool = None    ,					A			:							float = None    ,					A			:							bool = None    ,					A			:							Optional[Union[float, List[float]]] = None    ,					A			:							Optional[Union[float, List[float]]] = None    ,					A			:							bool = None    ,					A			:							Optional[Union[str, TensorType]] = None    ,					A			:							Optional[ChannelDimension] = ChannelDimension.FIRST    ,					**A			:							List[Any]    ,					)      ->      PIL.Image.Image:






         """simple docstring"""
         a__  			=							do_resize if do_resize is not None else self.do_resize
         a__  			=							size if size is not None else self.size
         a__  			=							get_size_dict(A    ,					default_to_square=A     )
         a__  			=							resample if resample is not None else self.resample
         a__  			=							do_center_crop if do_center_crop is not None else self.do_center_crop
         a__  			=							crop_size if crop_size is not None else self.crop_size
         a__  			=							get_size_dict(A     )
         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__  			=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         a__  			=							make_list_of_images(A     )

         if not valid_images(A     ):
             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:
             a__  			=							[convert_to_rgb(A     ) for image in images]

         # All transformations expect numpy arrays.
         a__  			=							[to_numpy_array(A     ) for image in images]

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

         if do_center_crop:
             a__  			=							[self.center_crop(image=A    ,					size=A     ) for image in images]

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

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

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

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












from itertools import count
from math import asin, pi, sqrt
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return (
        (1 - 2 * point) * sqrt(point - point**2		) + 2 * point + asin(sqrt(1 - point		)		)
    ) / 4
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(circles_number + 1 - sqrt(2 * circles_number		)) / (
        2 * (circles_number**2 + 1)
    )
    a__  			=							circles_number * intersection_y

    a__  			=							intersection_x * intersection_y / 2
    a__  			=							circle_bottom_arc_integral(
        1 / 2		) - circle_bottom_arc_integral(UpperCAmelCase_		)

    return triangle_area + concave_region_area
def 							__lowerCAmelCase			(UpperCAmelCase_ = 1 / 1_000		):
    a__  			=							(1 - pi / 4) / 4

    for n in count(1		):
        if concave_triangle_area(UpperCAmelCase_		) / l_section_area < fraction:
            return n

    return -1


if __name__ == "__main__":
 print(F"{solution() = }")






