
def      _A				(UpperCamelCase		:      list[list[float]] )  ->list[list[float]]:



			'''simple docstring'''







			lowerCamelCase__ :      list[list[float]]								=		[]
			for data in source_data:
						for i, el in enumerate(UpperCamelCase ):
									if len(UpperCamelCase ) < i + 1:
												data_lists.append([] )
									data_lists[i].append(float(UpperCamelCase ) )
			return data_lists






def      _A				(UpperCamelCase		:      list[list[float]]      ,			UpperCamelCase		:      list[int] )  ->list[list[float]]:



			'''simple docstring'''







			lowerCamelCase__ :      list[list[float]]								=		[]
			for dlist, weight in zip(UpperCamelCase      ,			UpperCamelCase ):
						lowerCamelCase__ :      Tuple								=		min(UpperCamelCase )
						lowerCamelCase__ :      Union[str, Any]								=		max(UpperCamelCase )

						lowerCamelCase__ :      list[float]								=		[]
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind)) )
												except ZeroDivisionError:
															score.append(1 )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind) )
												except ZeroDivisionError:
															score.append(0 )

        # weight not 0 or 1
						else:
									lowerCamelCase__ :      Optional[Any]								=		f"Invalid weight of {weight:f} provided"
									raise ValueError(UpperCamelCase )

						score_lists.append(UpperCamelCase )

			return score_lists






def      _A				(UpperCamelCase		:      list[list[float]] )  ->list[float]:



			'''simple docstring'''







			lowerCamelCase__ :      list[float]								=		[0 for i in range(len(score_lists[0] ) )]

			for slist in score_lists:
						for j, ele in enumerate(UpperCamelCase ):
									lowerCamelCase__ :      str								=		final_scores[j] + ele

			return final_scores






def      _A				(UpperCamelCase		:      list[list[float]]      ,			UpperCamelCase		:      list[int] )  ->list[list[float]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		get_data(UpperCamelCase )
			lowerCamelCase__ :      Union[str, Any]								=		calculate_each_score(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		generate_final_scores(UpperCamelCase )

			# append scores to source data
			for i, ele in enumerate(UpperCamelCase ):
						source_data[i].append(UpperCamelCase )

			return source_data








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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return resize(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return center_crop(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									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_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.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

						if do_rescale:
									lowerCamelCase__ :      Dict								=		[self.rescale(image=__magic_name__							,    scale=__magic_name__				) for image in images]

						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		[self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				) for image in images]

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



