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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				__snake_case			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['pixel_values']
		def __init__(       self     :     Tuple     ,  snake_case__     :     bool = True     ,  snake_case__     :     Dict[str, int] = None     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[int, float] = 1 / 2_5_5     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     bool = True     ,  **snake_case__     :     List[Any]     ,  )      ->	Optional[int]:
				super().__init__(**_SCREAMING_SNAKE_CASE		)
				_lowerCamelCase				   =  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
				_lowerCamelCase				   =  get_size_dict(_SCREAMING_SNAKE_CASE     ,  default_to_square=_SCREAMING_SNAKE_CASE		)

				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  resample
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				_lowerCamelCase				   =  image_std if image_std is not None else OPENAI_CLIP_STD
				_lowerCamelCase				   =  do_convert_rgb
		def   _snake_case       (       self     :     int     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Dict[str, int]     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     List[Any]     ,  )      ->	Optional[Any]:
				_lowerCamelCase				   =  get_size_dict(_SCREAMING_SNAKE_CASE     ,  default_to_square=_SCREAMING_SNAKE_CASE		)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				_lowerCamelCase				   =  (size['height'], size['width'])
				return resize(_SCREAMING_SNAKE_CASE     ,  size=_SCREAMING_SNAKE_CASE     ,  resample=_SCREAMING_SNAKE_CASE     ,  data_format=_SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE		)
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[int, float]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     str     ,  )      ->	List[str]:
				return rescale(_SCREAMING_SNAKE_CASE     ,  scale=_SCREAMING_SNAKE_CASE     ,  data_format=_SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE		)
		def   _snake_case       (       self     :     int     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Union[str, Any]     ,  )      ->	Any:
				return normalize(_SCREAMING_SNAKE_CASE     ,  mean=_SCREAMING_SNAKE_CASE     ,  std=_SCREAMING_SNAKE_CASE     ,  data_format=_SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE		)



		def   _snake_case       (       self     :     str     ,  snake_case__     :     ImageInput     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Dict[str, int]] = None     ,  snake_case__     :     PILImageResampling = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[float] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     bool = None     ,  snake_case__     :     ChannelDimension = ChannelDimension.FIRST     ,  **snake_case__     :     Tuple     ,  )      ->	Tuple:
				_lowerCamelCase				   =  do_resize if do_resize is not None else self.do_resize
				_lowerCamelCase				   =  resample if resample is not None else self.resample
				_lowerCamelCase				   =  do_rescale if do_rescale is not None else self.do_rescale
				_lowerCamelCase				   =  rescale_factor if rescale_factor is not None else self.rescale_factor
				_lowerCamelCase				   =  do_normalize if do_normalize is not None else self.do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else self.image_mean
				_lowerCamelCase				   =  image_std if image_std is not None else self.image_std
				_lowerCamelCase				   =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				_lowerCamelCase				   =  size if size is not None else self.size
				_lowerCamelCase				   =  get_size_dict(_SCREAMING_SNAKE_CASE     ,  default_to_square=_SCREAMING_SNAKE_CASE		)

				_lowerCamelCase				   =  make_list_of_images(_SCREAMING_SNAKE_CASE		)

				if not valid_images(_SCREAMING_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_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.'		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						_lowerCamelCase				   =  [convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

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

				if do_resize:
						_lowerCamelCase				   =  [self.resize(image=_SCREAMING_SNAKE_CASE     ,  size=_SCREAMING_SNAKE_CASE     ,  resample=_SCREAMING_SNAKE_CASE		) for image in images]

				if do_rescale:
						_lowerCamelCase				   =  [self.rescale(image=_SCREAMING_SNAKE_CASE     ,  scale=_SCREAMING_SNAKE_CASE		) for image in images]

				if do_normalize:
						_lowerCamelCase				   =  [self.normalize(image=_SCREAMING_SNAKE_CASE     ,  mean=_SCREAMING_SNAKE_CASE     ,  std=_SCREAMING_SNAKE_CASE		) for image in images]

				_lowerCamelCase				   =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE		) for image in images]

				_lowerCamelCase				   =  BatchFeature(data={'pixel_values': images}     ,  tensor_type=_SCREAMING_SNAKE_CASE		)

				return encoded_outputs







from collections.abc import Sequence
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      UpperCamelCase					       =  0.0
      for coeff in reversed(_UpperCamelCase):
            UpperCamelCase					       =  result * x + coeff
      return result


if __name__ == "__main__":
       __magic_name__   :  Union[str, Any]        =							(0.0, 0.0, 5.0, 9.3, 7.0)
       __magic_name__   :  List[str]        =							10.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))



