


"""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


A  :     List[str]    					=    logging.get_logger(__name__)


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    b.T
			__lowerCAmelCase       					=    np.sum(np.square(_UpperCamelCase   )					,				axis=1   )
			__lowerCAmelCase       					=    np.sum(np.square(_UpperCamelCase   )					,				axis=0   )
			__lowerCAmelCase       					=    np.matmul(_UpperCamelCase					,				_UpperCamelCase   )
			__lowerCAmelCase       					=    aa[:, None] - 2 * ab + ba[None, :]
			return d


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.reshape(-1					,				3   )
			__lowerCAmelCase       					=    squared_euclidean_distance(_UpperCamelCase					,				_UpperCamelCase   )
			return np.argmin(_UpperCamelCase					,				axis=1   )


class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







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

							def __init__(  self					,     __a = None					,     __a = True					,     __a = None					,     __a = PILImageResampling.BILINEAR					,     __a = True					,     __a = True					,     **__a					,     ):
										super().__init__(**__a			)
										__lowerCAmelCase       					=    size if size is not None else {"height": 2_56, "width": 2_56}
										__lowerCAmelCase       					=    get_size_dict(__a			)
										__lowerCAmelCase       					=    np.array(__a			) if clusters is not None else None
										__lowerCAmelCase       					=    do_resize
										__lowerCAmelCase       					=    size
										__lowerCAmelCase       					=    resample
										__lowerCAmelCase       					=    do_normalize
										__lowerCAmelCase       					=    do_color_quantize

							def     snake_case       (  self					,     __a					,     __a					,     __a = PILImageResampling.BILINEAR					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    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					,     ):
										__lowerCAmelCase       					=    rescale(image=__a					,     scale=1 / 1_2_7.5					,     data_format=__a			)
										__lowerCAmelCase       					=    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					,     ):
										__lowerCAmelCase       					=    do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase       					=    size if size is not None else self.size
										__lowerCAmelCase       					=    get_size_dict(__a			)
										__lowerCAmelCase       					=    resample if resample is not None else self.resample
										__lowerCAmelCase       					=    do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase       					=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										__lowerCAmelCase       					=    clusters if clusters is not None else self.clusters
										__lowerCAmelCase       					=    np.array(__a			)

										__lowerCAmelCase       					=    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.
										__lowerCAmelCase       					=    [to_numpy_array(__a			) for image in images]

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

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

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

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

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

										__lowerCAmelCase       					=    {"input_ids": images}
										return BatchFeature(data=__a					,     tensor_type=__a			)








"""simple docstring"""


import cmath
import math


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    math.radians(_UpperCamelCase   )
			__lowerCAmelCase       					=    math.radians(_UpperCamelCase   )

			# Convert voltage and current to rectangular form
			__lowerCAmelCase       					=    cmath.rect(_UpperCamelCase					,				_UpperCamelCase   )
			__lowerCAmelCase       					=    cmath.rect(_UpperCamelCase					,				_UpperCamelCase   )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
	import doctest

	doctest.testmod()




