


"""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 convert_to_rgb, 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


if is_vision_available():
	import PIL


_lowerCamelCase     =  logging.get_logger(__name__)


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					["pixel_values"]



				def __init__(						self       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = PILImageResampling.BICUBIC       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = 1 / 255       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = True       ,		**UpperCAmelCase__       ,		):
								super().__init__(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				size if size is not None else {"height": 384, "width": 384}
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__       ,		default_to_square=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				do_resize
								SCREAMING_SNAKE_CASE__     							=				size
								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 OPENAI_CLIP_MEAN
								SCREAMING_SNAKE_CASE__     							=				image_std if image_std is not None else OPENAI_CLIP_STD
								SCREAMING_SNAKE_CASE__     							=				do_convert_rgb



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = PILImageResampling.BICUBIC       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__       ,		default_to_square=UpperCAmelCase__     )
								if "height" not in size or "width" not in size:
												raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''     )
								SCREAMING_SNAKE_CASE__     							=				(size["height"], size["width"])
								return resize(UpperCAmelCase__       ,		size=UpperCAmelCase__       ,		resample=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								return rescale(UpperCAmelCase__       ,		scale=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								return normalize(UpperCAmelCase__       ,		mean=UpperCAmelCase__       ,		std=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = ChannelDimension.FIRST       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				do_resize if do_resize is not None else self.do_resize
								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__     							=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								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_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:
												SCREAMING_SNAKE_CASE__     							=				[convert_to_rgb(UpperCAmelCase__     ) for image in images]

								# 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__       ,		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__     							=				BatchFeature(data={"pixel_values": images}       ,		tensor_type=UpperCAmelCase__     )

								return encoded_outputs




"""simple docstring"""






from math import pi


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
	print(arc_length(90, 10))
