
'''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, 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


__snake_case:	List[Any]										=      logging.get_logger(__name__)


class 			_UpperCAmelCase			(			__A     ):

     """simple docstring"""

     a_     						=       ['pixel_values']


     def __init__(   self				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = PIL.Image.BICUBIC				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = 1 / 2_55				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       super().__init__(**snake_case_			)
       a_   :						Any   =				size if size is not None else {"""height""": 2_56, """width""": 2_56}
       a_   :						Optional[Any]   =				get_size_dict(snake_case_			)
       a_   :						Optional[Any]   =				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
       a_   :						int   =				get_size_dict(snake_case_				,		param_name="""crop_size"""			)

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


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = PIL.Image.BICUBIC				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						str   =				get_size_dict(snake_case_			)
       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(
           snake_case_				,		size=(size["""height"""], size["""width"""])				,		resample=snake_case_				,		data_format=snake_case_				,		**snake_case_			)


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						Optional[Any]   =				get_size_dict(snake_case_			)
       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(snake_case_				,		size=(size["""height"""], size["""width"""])				,		data_format=snake_case_				,		**snake_case_			)


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       return rescale(snake_case_				,		scale=snake_case_				,		data_format=snake_case_				,		**snake_case_			)


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       return normalize(snake_case_				,		mean=snake_case_				,		std=snake_case_				,		data_format=snake_case_				,		**snake_case_			)






     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_=None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = ChannelDimension.FIRST				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						Tuple   =				do_resize if do_resize is not None else self.do_resize
       a_   :						Optional[Any]   =				resample if resample is not None else self.resample
       a_   :						List[str]   =				do_center_crop if do_center_crop is not None else self.do_center_crop
       a_   :						Tuple   =				do_rescale if do_rescale is not None else self.do_rescale
       a_   :						Optional[int]   =				rescale_factor if rescale_factor is not None else self.rescale_factor
       a_   :						str   =				do_normalize if do_normalize is not None else self.do_normalize
       a_   :						Dict   =				image_mean if image_mean is not None else self.image_mean
       a_   :						Union[str, Any]   =				image_std if image_std is not None else self.image_std

       a_   :						List[Any]   =				size if size is not None else self.size
       a_   :						Union[str, Any]   =				get_size_dict(snake_case_			)
       a_   :						List[str]   =				crop_size if crop_size is not None else self.crop_size
       a_   :						List[str]   =				get_size_dict(snake_case_				,		param_name="""crop_size"""			)

       a_   :						Optional[Any]   =				make_list_of_images(snake_case_			)

       if not valid_images(snake_case_			):
         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.
       a_   :						Optional[Any]   =				[to_numpy_array(snake_case_			) for image in images]

       if do_resize:
         a_   :						str   =				[self.resize(image=snake_case_				,		size=snake_case_				,		resample=snake_case_			) for image in images]

       if do_center_crop:
         a_   :						Union[str, Any]   =				[self.center_crop(image=snake_case_				,		size=snake_case_			) for image in images]

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

       if do_normalize:
         a_   :						Dict   =				[self.normalize(image=snake_case_				,		mean=snake_case_				,		std=snake_case_			) for image in images]

       a_   :						Tuple   =				[to_channel_dimension_format(snake_case_				,		snake_case_			) for image in images]

       a_   :						int   =				{"""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))





