


'''simple docstring'''
from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_ :     List[str]								=      logging.get_logger(__name__)

class 						_lowerCamelCase					(    _UpperCAmelCase				):




	'''simple docstring'''
	__lowercase				:		List[str]    							=						['''pixel_values''']


	def __init__(      self      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = True      ,      __lowercase = 1 / 255      ,      __lowercase = True      ,      __lowercase = IMAGENET_DEFAULT_MEAN      ,      __lowercase = IMAGENET_DEFAULT_STD      ,      **__lowercase      ,      ):

		"""simple docstring"""







		super().__init__(**lowerCamelCase_						)
		__A  :		str       =			size if size is not None else {'''shortest_edge''': 224}
		__A  :		Optional[int]       =			get_size_dict(lowerCamelCase_      ,      default_to_square=lowerCamelCase_						)
		__A  :		Union[str, Any]       =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
		__A  :		Dict       =			get_size_dict(lowerCamelCase_      ,      param_name='crop_size'						)

		__A  :		List[str]       =			do_resize
		__A  :		Tuple       =			size
		__A  :		Optional[Any]       =			resample
		__A  :		Any       =			do_center_crop
		__A  :		List[str]       =			crop_size
		__A  :		int       =			do_rescale
		__A  :		Any       =			rescale_factor
		__A  :		str       =			do_normalize
		__A  :		List[str]       =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		__A  :		Optional[Any]       =			image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = None      ,      **__lowercase      ,      ):

		"""simple docstring"""







		__A  :		List[Any]       =			get_size_dict(lowerCamelCase_      ,      default_to_square=lowerCamelCase_						)
		# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
		if "shortest_edge" in size:
			__A  :		str       =			int((256 / 224) * size['shortest_edge']						)
			__A  :		Any       =			get_resize_output_image_size(lowerCamelCase_      ,      size=lowerCamelCase_      ,      default_to_square=lowerCamelCase_						)
			__A  :		Optional[int]       =			{'''height''': output_size[0], '''width''': output_size[1]}
		if "height" not in size_dict or "width" not in size_dict:
			raise ValueError(
			    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""						)
		return resize(
		    lowerCamelCase_      ,      size=(size_dict['height'], size_dict['width'])      ,      resample=lowerCamelCase_      ,      data_format=lowerCamelCase_      ,      **lowerCamelCase_						)


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

		"""simple docstring"""







		__A  :		Union[str, Any]       =			get_size_dict(lowerCamelCase_						)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""						)
		return center_crop(lowerCamelCase_      ,      size=(size['height'], size['width'])      ,      data_format=lowerCamelCase_      ,      **lowerCamelCase_						)


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

		"""simple docstring"""







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


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

		"""simple docstring"""







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







	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      **__lowercase      ,      ):

		"""simple docstring"""







		__A  :		Optional[Any]       =			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[Any]       =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A  :		Dict       =			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  :		Optional[int]       =			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  :		List[str]       =			image_std if image_std is not None else self.image_std

		__A  :		Union[str, Any]       =			size if size is not None else self.size
		__A  :		Union[str, Any]       =			get_size_dict(lowerCamelCase_      ,      default_to_square=lowerCamelCase_						)
		__A  :		str       =			crop_size if crop_size is not None else self.crop_size
		__A  :		List[Any]       =			get_size_dict(lowerCamelCase_      ,      param_name='crop_size'						)

		__A  :		str       =			make_list_of_images(lowerCamelCase_						)

		if not valid_images(lowerCamelCase_						):
			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.
		__A  :		List[Any]       =			[to_numpy_array(lowerCamelCase_						) for image in images]

		if do_resize:
			__A  :		Union[str, Any]       =			[self.resize(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_						) for image in images]

		if do_center_crop:
			__A  :		Union[str, Any]       =			[self.center_crop(lowerCamelCase_      ,      lowerCamelCase_						) for image in images]

		if do_rescale:
			__A  :		Tuple       =			[self.rescale(lowerCamelCase_      ,      lowerCamelCase_						) for image in images]

		if do_normalize:
			__A  :		Dict       =			[self.normalize(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_						) for image in images]

		__A  :		List[str]       =			[to_channel_dimension_format(lowerCamelCase_      ,      lowerCamelCase_						) for image in images]

		__A  :		Any       =			{'''pixel_values''': images}
		return BatchFeature(data=lowerCamelCase_      ,      tensor_type=lowerCamelCase_						)










'''simple docstring'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


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






