




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__     						=						logging.get_logger(__name__)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""pixel_values"""]
			def __init__(  self  ,      lowercase = True  ,      lowercase = 32  ,      lowercase=PILImageResampling.BILINEAR  ,      lowercase = True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			List[Any]    =    do_resize
							_lowerCamelCase     :			Dict    =    do_rescale
							_lowerCamelCase     :			Tuple    =    size_divisor
							_lowerCamelCase     :			List[str]    =    resample
							super().__init__(**lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      **lowercase  ):
							_lowerCamelCase, _lowerCamelCase     :			Dict    =    get_image_size(lowercase  )
							# Rounds the height and width down to the closest multiple of size_divisor
							_lowerCamelCase     :			List[str]    =    height // size_divisor * size_divisor
							_lowerCamelCase     :			Tuple    =    width // size_divisor * size_divisor
							_lowerCamelCase     :			int    =    resize(lowercase  ,      (new_h, new_w)  ,      resample=lowercase  ,      data_format=lowercase  ,      **lowercase  )
							return image
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      **lowercase  ):
							return rescale(image=lowercase  ,      scale=lowercase  ,      data_format=lowercase  ,      **lowercase  )






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      lowercase = None  ,      lowercase=None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = ChannelDimension.FIRST  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Optional[int]    =    do_resize if do_resize is not None else self.do_resize
							_lowerCamelCase     :			Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
							_lowerCamelCase     :			Optional[Any]    =    size_divisor if size_divisor is not None else self.size_divisor
							_lowerCamelCase     :			Optional[Any]    =    resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'  )

							_lowerCamelCase     :			Tuple    =    make_list_of_images(lowercase  )

							if not valid_images(lowercase  ):
											raise ValueError('Invalid image(s)'  )

							# All transformations expect numpy arrays.
							_lowerCamelCase     :			Any    =    [to_numpy_array(lowercase  ) for img in images]

							if do_resize:
											_lowerCamelCase     :			Optional[int]    =    [self.resize(lowercase  ,      size_divisor=lowercase  ,      resample=lowercase  ) for image in images]

							if do_rescale:
											_lowerCamelCase     :			List[str]    =    [self.rescale(lowercase  ,      scale=1 / 255  ) for image in images]

							_lowerCamelCase     :			Union[str, Any]    =    [to_channel_dimension_format(lowercase  ,      lowercase  ) for image in images]

							_lowerCamelCase     :			str    =    {'pixel_values': images}
							return BatchFeature(data=lowercase  ,      tensor_type=lowercase  )





"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()