





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case								=							logging.get_logger(__name__)





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Dict  			=	['''pixel_values''']







       def __init__(     self						:						Union[str, Any]    ,__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 / 255    ,__A						:						bool = True    ,__A						:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN    ,__A						:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD    ,**__A						:						Optional[int]    ,)    ->       None:
         super().__init__(**__A	)
         _lowercase						    =			size if size is not None else {'shortest_edge': 224}
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A	)
         _lowercase						    =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
         _lowercase						    =			get_size_dict(__A    ,param_name='crop_size'	)

         _lowercase						    =			do_resize
         _lowercase						    =			size
         _lowercase						    =			resample
         _lowercase						    =			do_center_crop
         _lowercase						    =			crop_size
         _lowercase						    =			do_rescale
         _lowercase						    =			rescale_factor
         _lowercase						    =			do_normalize
         _lowercase						    =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowercase						    =			image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						np.ndarray    ,__A						:						Dict[str, int]    ,__A						:						PILImageResampling = PILImageResampling.BICUBIC    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						List[str]    ,)    ->       np.ndarray:
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A	)
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
           _lowercase						    =			int((256 / 224) * size['shortest_edge']	)
           _lowercase						    =			get_resize_output_image_size(__A    ,size=__A    ,default_to_square=__A	)
           _lowercase						    =			{'height': output_size[0], 'width': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
           raise ValueError(
               F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""	)
         return resize(
             __A    ,size=(size_dict['height'], size_dict['width'])    ,resample=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						np.ndarray    ,__A						:						Dict[str, int]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						Optional[int]    ,)    ->       np.ndarray:
         _lowercase						    =			get_size_dict(__A	)
         if "height" not in size or "width" not in size:
           raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""	)
         return center_crop(__A    ,size=(size['height'], size['width'])    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Optional[int]    ,__A						:						np.ndarray    ,__A						:						Union[int, float]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						List[str]    ,)    ->       np.ndarray:
         return rescale(__A    ,scale=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						np.ndarray    ,__A						:						Union[float, List[float]]    ,__A						:						Union[float, List[float]]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						int    ,)    ->       np.ndarray:
         return normalize(__A    ,mean=__A    ,std=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						ImageInput    ,__A						:						Optional[bool] = None    ,__A						:						Optional[Dict[str, int]] = None    ,__A						:						PILImageResampling = None    ,__A						:						Optional[bool] = None    ,__A						:						Optional[Dict[str, int]] = None    ,__A						:						Optional[bool] = None    ,__A						:						Optional[float] = None    ,__A						:						Optional[bool] = None    ,__A						:						Optional[Union[float, Iterable[float]]] = None    ,__A						:						Optional[Union[float, Iterable[float]]] = None    ,__A						:						Optional[TensorType] = None    ,__A						:						ChannelDimension = ChannelDimension.FIRST    ,**__A						:						Optional[Any]    ,)    ->       BatchFeature:
         _lowercase						    =			do_resize if do_resize is not None else self.do_resize
         _lowercase						    =			resample if resample is not None else self.resample
         _lowercase						    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
         _lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
         _lowercase						    =			image_mean if image_mean is not None else self.image_mean
         _lowercase						    =			image_std if image_std is not None else self.image_std

         _lowercase						    =			size if size is not None else self.size
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A	)
         _lowercase						    =			crop_size if crop_size is not None else self.crop_size
         _lowercase						    =			get_size_dict(__A    ,param_name='crop_size'	)

         _lowercase						    =			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.'	)

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

         if do_resize:
           _lowercase						    =			[self.resize(__A    ,__A    ,__A	) for image in images]

         if do_center_crop:
           _lowercase						    =			[self.center_crop(__A    ,__A	) for image in images]

         if do_rescale:
           _lowercase						    =			[self.rescale(__A    ,__A	) for image in images]

         if do_normalize:
           _lowercase						    =			[self.normalize(__A    ,__A    ,__A	) for image in images]

         _lowercase						    =			[to_channel_dimension_format(__A    ,__A	) for image in images]

         _lowercase						    =			{'pixel_values': images}
         return BatchFeature(data=__A    ,tensor_type=__A	)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

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

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
