





'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[int]    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			b.T
    _SCREAMING_SNAKE_CASE									=			np.sum(np.square(__lowerCamelCase    )					,       axis=1    )
    _SCREAMING_SNAKE_CASE									=			np.sum(np.square(__lowerCamelCase    )					,       axis=0    )
    _SCREAMING_SNAKE_CASE									=			np.matmul(__lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			aa[:, None] - 2 * ab + ba[None, :]
    return d





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Dict    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			x.reshape(-1					,       3    )
    _SCREAMING_SNAKE_CASE									=			squared_euclidean_distance(__lowerCamelCase					,       __lowerCamelCase    )
    return np.argmin(__lowerCamelCase					,       axis=1    )






class 					a_     (				A__       ):

    '''simple docstring'''
    UpperCamelCase     = ['''pixel_values''']


    def __init__(		self ,		A = None ,		A = True ,		A = None ,		A = PILImageResampling.BILINEAR ,		A = True ,		A = True ,		**A ,		)		->   List[Any]:
        super().__init__(**_A       )
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"height": 256, "width": 256}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(_A       )
        _SCREAMING_SNAKE_CASE									=			np.array(_A       ) if clusters is not None else None
        _SCREAMING_SNAKE_CASE									=			do_resize
        _SCREAMING_SNAKE_CASE									=			size
        _SCREAMING_SNAKE_CASE									=			resample
        _SCREAMING_SNAKE_CASE									=			do_normalize
        _SCREAMING_SNAKE_CASE									=			do_color_quantize


    def 			snake_case_(		self ,		A ,		A ,		A = PILImageResampling.BILINEAR ,		A = None ,		**A ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			get_size_dict(_A       )
        if "height" not in size or "width" not in size:
            raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'       )
        return resize(
            _A ,		size=(size["""height"""], size["""width"""]) ,		resample=_A ,		data_format=_A ,		**_A       )


    def 			snake_case_(		self ,		A ,		A = None ,		)		->   Any:
        _SCREAMING_SNAKE_CASE									=			rescale(image=_A ,		scale=1 / 127.5 ,		data_format=_A       )
        _SCREAMING_SNAKE_CASE									=			image - 1
        return image





    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = ChannelDimension.FIRST ,		**A ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			do_resize if do_resize is not None else self.do_resize
        _SCREAMING_SNAKE_CASE									=			size if size is not None else self.size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(_A       )
        _SCREAMING_SNAKE_CASE									=			resample if resample is not None else self.resample
        _SCREAMING_SNAKE_CASE									=			do_normalize if do_normalize is not None else self.do_normalize
        _SCREAMING_SNAKE_CASE									=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        _SCREAMING_SNAKE_CASE									=			clusters if clusters is not None else self.clusters
        _SCREAMING_SNAKE_CASE									=			np.array(_A       )

        _SCREAMING_SNAKE_CASE									=			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 or resample is None:
            raise ValueError("""Size and resample must be specified if do_resize is True."""       )

        if do_color_quantize and clusters is None:
            raise ValueError("""Clusters must be specified if do_color_quantize is True."""       )

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

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

        if do_normalize:
            _SCREAMING_SNAKE_CASE									=			[self.normalize(image=_A       ) for image in images]

        if do_color_quantize:
            _SCREAMING_SNAKE_CASE									=			[to_channel_dimension_format(_A ,		ChannelDimension.LAST       ) for image in images]
            # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
            _SCREAMING_SNAKE_CASE									=			np.array(_A       )
            _SCREAMING_SNAKE_CASE									=			color_quantize(_A ,		_A       ).reshape(images.shape[:-1]       )

            # flatten to (batch_size, height*width)
            _SCREAMING_SNAKE_CASE									=			images.shape[0]
            _SCREAMING_SNAKE_CASE									=			images.reshape(_A ,		-1       )

            # We need to convert back to a list of images to keep consistent behaviour across processors.
            _SCREAMING_SNAKE_CASE									=			list(_A       )
        else:
            _SCREAMING_SNAKE_CASE									=			[to_channel_dimension_format(_A ,		_A       ) for image in images]

        _SCREAMING_SNAKE_CASE									=			{"input_ids": images}
        return BatchFeature(data=_A ,		tensor_type=_A       )




from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
