





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


__A							:			Dict         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['pixel_values']




     def __init__(    self				,     snake_case_ = True				,     snake_case_ = 32				,     snake_case_=PILImageResampling.BILINEAR				,     snake_case_ = True				,     **snake_case_				,     ):
         _A     =							do_resize
         _A     =							do_rescale
         _A     =							size_divisor
         _A     =							resample
         super().__init__(**snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_							):
         _A,			_A     =							get_image_size(snake_case_							)
         # Rounds the height and width down to the closest multiple of size_divisor
         _A     =							height // size_divisor * size_divisor
         _A     =							width // size_divisor * size_divisor
         _A     =							resize(snake_case_				,     (new_h, new_w)				,     resample=snake_case_				,     data_format=snake_case_				,     **snake_case_							)
         return image




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_							):
         return rescale(image=snake_case_				,     scale=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_=None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = ChannelDimension.FIRST				,     **snake_case_				,     ):
         _A     =							do_resize if do_resize is not None else self.do_resize
         _A     =							do_rescale if do_rescale is not None else self.do_rescale
         _A     =							size_divisor if size_divisor is not None else self.size_divisor
         _A     =							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'							)

         _A     =							make_list_of_images(snake_case_							)

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

         # All transformations expect numpy arrays.
         _A     =							[to_numpy_array(snake_case_							) for img in images]

         if do_resize:
             _A     =							[self.resize(snake_case_				,     size_divisor=snake_case_				,     resample=snake_case_							) for image in images]

         if do_rescale:
             _A     =							[self.rescale(snake_case_				,     scale=1 / 255							) for image in images]

         _A     =							[to_channel_dimension_format(snake_case_				,     snake_case_							) for image in images]

         _A     =							{'pixel_values': images}
         return BatchFeature(data=snake_case_				,     tensor_type=snake_case_							)






"""simple docstring"""



from collections.abc import Sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase							)							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			0.0
							for coeff in reversed(lowerCAmelCase							):
														A__       :     Optional[int]								=			result * x + coeff
							return result


if __name__ == "__main__":
				_UpperCamelCase								=		(0.0, 0.0, 5.0, 9.3, 7.0)
				_UpperCamelCase								=		10.0
				print(evaluate_poly(poly, x))
				print(horner(poly, x))





