





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


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


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  str			   =   ["pixel_values"]







       def __init__(				self					:     Optional[int]		, A					:     bool = True		, A					:     Union[int, float] = 1 / 2_5_5		, A					:     bool = True		, A					:     int = 8		, **A					:     Any		, )       ->None:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[int]        =							do_rescale
          lowerCamelCase__       :						int        =							rescale_factor
          lowerCamelCase__       :						str        =							do_pad
          lowerCamelCase__       :						int        =							pad_size







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     np.ndarray		, A					:     float		, A					:     Optional[Union[str, ChannelDimension]] = None		, **A					:     Optional[int]      )       ->np.ndarray:
          return rescale(A		, scale=A		, data_format=A		, **A      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     np.ndarray		, A					:     int		, A					:     Optional[Union[str, ChannelDimension]] = None      )       ->str:
          lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							get_image_size(A      )
          lowerCamelCase__       :						Union[str, Any]        =							(old_height // size + 1) * size - old_height
          lowerCamelCase__       :						int        =							(old_width // size + 1) * size - old_width

          return pad(A		, ((0, pad_height), (0, pad_width))		, mode='''symmetric'''		, data_format=A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     ImageInput		, A					:     Optional[bool] = None		, A					:     Optional[float] = None		, A					:     Optional[bool] = None		, A					:     Optional[int] = None		, A					:     Optional[Union[str, TensorType]] = None		, A					:     Union[str, ChannelDimension] = ChannelDimension.FIRST		, **A					:     Optional[Any]		, )       ->Optional[int]:
          lowerCamelCase__       :						Optional[int]        =							do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase__       :						Optional[Any]        =							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase__       :						Tuple        =							do_pad if do_pad is not None else self.do_pad
          lowerCamelCase__       :						Any        =							pad_size if pad_size is not None else self.pad_size

          lowerCamelCase__       :						str        =							make_list_of_images(A      )

          if not valid_images(A      ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

          if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

          # All transformations expect numpy arrays.
          lowerCamelCase__       :						List[Any]        =							[to_numpy_array(A      ) for image in images]

          if do_rescale:
             lowerCamelCase__       :						Union[str, Any]        =							[self.rescale(image=A		, scale=A      ) for image in images]

          if do_pad:
             lowerCamelCase__       :						List[Any]        =							[self.pad(A		, size=A      ) for image in images]

          lowerCamelCase__       :						Dict        =							[to_channel_dimension_format(A		, A      ) for image in images]

          lowerCamelCase__       :						Optional[int]        =							{'''pixel_values''': images}
          return BatchFeature(data=A		, tensor_type=A      )










from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







       def __add__(				self					:     Union[str, Any]		, A					:     int      )       ->str:
          if not isinstance(A		, A      ):
             return Dual(self.real + other		, self.duals      )
          lowerCamelCase__       :						int        =							self.duals.copy()
          lowerCamelCase__       :						int        =							other.duals.copy()
          if len(A      ) > len(A      ):
             o_dual.extend([1] * (len(A      ) - len(A      ))      )
          elif len(A      ) < len(A      ):
             s_dual.extend([1] * (len(A      ) - len(A      ))      )
          lowerCamelCase__       :						Optional[Any]        =							[]
          for i in range(len(A      )      ):
             new_duals.append(s_dual[i] + o_dual[i]      )
          return Dual(self.real + other.real		, A      )

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[0] * (len(self.duals      ) + len(other.duals      ) + 1)
          for i, item in enumerate(self.duals      ):
             for j, jtem in enumerate(other.duals      ):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals      )      ):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals      )      ):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







       def __floordiv__(				self					:     Dict		, A					:     Union[str, Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i // other      )
             return Dual(self.real // other		, A      )
          raise ValueError







       def __pow__(				self					:     Any		, A					:     List[Any]      )       ->Tuple:
          if n < 0 or isinstance(A		, A      ):
             raise ValueError('''power must be a positive integer'''      )
          if n == 0:
             return 1
          if n == 1:
             return self
          lowerCamelCase__       :						Union[str, Any]        =							self
          for _ in range(n - 1      ):
             x *= self
          return x



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if not callable(UpperCAmelCase					):
      raise ValueError('''differentiate() requires a function as input for func'''					)
   if not isinstance(UpperCAmelCase      ,  (float, int)					):
      raise ValueError('''differentiate() requires a float as input for position'''					)
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise ValueError('''differentiate() requires an int as input for order'''					)
   lowerCamelCase__       :						List[str]        =							Dual(UpperCAmelCase      ,  1					)
   lowerCamelCase__       :						Any        =							func(UpperCAmelCase					)
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(UpperCAmelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



