



'''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 (
    center_crop,
    get_resize_output_image_size,
    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, logging


lowerCAmelCase_     :       List[str]						=						logging.get_logger(__name__)
class 	lowerCamelCase_							(					a__			):
						_lowerCAmelCase     :   str	     =				["""pixel_values"""]







						def __init__(  self   :      str				,    lowerCAmelCase__   :      str = True				,    lowerCAmelCase__   :      Union[str, Any] = None				,    lowerCAmelCase__   :      Dict = PILImageResampling.BILINEAR				,    lowerCAmelCase__   :      Optional[int] = True				,    lowerCAmelCase__   :      Tuple = None				,    lowerCAmelCase__   :      Optional[int] = True				,    lowerCAmelCase__   :      Any = 1 / 2_55				,    lowerCAmelCase__   :      Optional[Any] = True				,    lowerCAmelCase__   :      str = None				,    lowerCAmelCase__   :      Any = None				,    **lowerCAmelCase__   :      Optional[int]				,    ):

													"""simple docstring"""
													super().__init__(**lowercase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			size if size is not None else {'''shortest_edge''': 2_56}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			get_size_dict(lowercase__				,    default_to_square=lowercase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			get_size_dict(lowercase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			do_resize
													SCREAMING_SNAKE_CASE					:      Dict     					=			size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			resample
													SCREAMING_SNAKE_CASE					:      Dict     					=			do_center_crop
													SCREAMING_SNAKE_CASE					:      Any     					=			crop_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			do_rescale
													SCREAMING_SNAKE_CASE					:      List[str]     					=			rescale_factor
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			do_normalize
													SCREAMING_SNAKE_CASE					:      Any     					=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE					:      Any     					=			image_std if image_std is not None else IMAGENET_STANDARD_STD







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Any] = PILImageResampling.BICUBIC				,    lowerCAmelCase__   :      Optional[int] = None				,    **lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			get_size_dict(lowercase__				,    default_to_square=lowercase__				)
													if "shortest_edge" not in size:
																				raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_resize_output_image_size(lowercase__				,    size=size['''shortest_edge''']				,    default_to_square=lowercase__				)
													return resize(lowercase__				,    size=lowercase__				,    resample=lowercase__				,    data_format=lowercase__				,    **lowercase__				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[Any] = None				,    **lowerCAmelCase__   :      Optional[int]				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_size_dict(lowercase__				)
													return center_crop(lowercase__				,    size=(size['''height'''], size['''width'''])				,    data_format=lowercase__				,    **lowercase__				)







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple = None				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return rescale(lowercase__				,    scale=lowercase__				,    data_format=lowercase__				,    **lowercase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Dict = None				,    **lowerCAmelCase__   :      List[Any]				,    ):

													"""simple docstring"""
													return normalize(lowercase__				,    mean=lowercase__				,    std=lowercase__				,    data_format=lowercase__				,    **lowercase__				)







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Any = None				,    lowerCAmelCase__   :      List[Any] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Any = None				,    lowerCAmelCase__   :      Any = None				,    lowerCAmelCase__   :      Union[str, Any] = None				,    lowerCAmelCase__   :      Tuple = None				,    lowerCAmelCase__   :      List[str] = None				,    lowerCAmelCase__   :      str = None				,    lowerCAmelCase__   :      List[str] = None				,    lowerCAmelCase__   :      str = None				,    lowerCAmelCase__   :      str = ChannelDimension.FIRST				,    **lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE					:      int     					=			size if size is not None else self.size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_size_dict(lowercase__				,    default_to_square=lowercase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE					:      Tuple     					=			do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE					:      Dict     					=			get_size_dict(lowercase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE					:      Dict     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			make_list_of_images(lowercase__				)

													if not valid_images(lowercase__				):
																				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.
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[to_numpy_array(lowercase__				) for image in images]

													if do_resize:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			[self.resize(image=lowercase__				,    size=lowercase__				,    resample=lowercase__				) for image in images]

													if do_center_crop:
																				SCREAMING_SNAKE_CASE					:      Any     					=			[self.center_crop(image=lowercase__				,    size=lowercase__				) for image in images]

													if do_rescale:
																				SCREAMING_SNAKE_CASE					:      int     					=			[self.rescale(image=lowercase__				,    scale=lowercase__				) for image in images]

													if do_normalize:
																				SCREAMING_SNAKE_CASE					:      Any     					=			[self.normalize(image=lowercase__				,    mean=lowercase__				,    std=lowercase__				) for image in images]

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			[to_channel_dimension_format(lowercase__				,    lowercase__				) for image in images]

													SCREAMING_SNAKE_CASE					:      int     					=			{'''pixel_values''': images}
													return BatchFeature(data=lowercase__				,    tensor_type=lowercase__				)









'''simple docstring'''



from bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




