





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  :   str						   =  logging.get_logger(__name__)



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""pixel_values"""]






  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		PILImageResampling = PIL.Image.BICUBIC ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		Union[int, float] = 1 / 2_5_5 ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  **lowerCAmelCase_			:		Optional[int] ,  )							->						None:
     super().__init__(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_ ,  param_name='crop_size'					)

     __lowerCAmelCase							     =     do_resize
     __lowerCAmelCase							     =     size
     __lowerCAmelCase							     =     resample
     __lowerCAmelCase							     =     do_center_crop
     __lowerCAmelCase							     =     crop_size
     __lowerCAmelCase							     =     do_rescale
     __lowerCAmelCase							     =     rescale_factor
     __lowerCAmelCase							     =     do_normalize
     __lowerCAmelCase							     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __lowerCAmelCase							     =     image_std if image_std is not None else IMAGENET_STANDARD_STD






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Dict[str, int] ,  lowerCAmelCase_			:		PILImageResampling = PIL.Image.BICUBIC ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		Optional[int] ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     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(
         lowerCAmelCase_ ,  size=(size['height'], size['width']) ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Dict[str, int] ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		Any ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     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(lowerCAmelCase_ ,  size=(size['height'], size['width']) ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






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






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Union[float, List[float]] ,  lowerCAmelCase_			:		Union[float, List[float]] ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		List[str] ,  )							->						np.ndarray:
     return normalize(lowerCAmelCase_ ,  mean=lowerCAmelCase_ ,  std=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		ImageInput ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		str=None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		float = None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  lowerCAmelCase_			:		ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						PIL.Image.Image:
     __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_center_crop if do_center_crop is not None else self.do_center_crop
     __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							     =     size if size is not None else self.size
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     crop_size if crop_size is not None else self.crop_size
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_ ,  param_name='crop_size'					)

     __lowerCAmelCase							     =     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 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.
     __lowerCAmelCase							     =     [to_numpy_array(lowerCAmelCase_					) for image in images]

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

     if do_center_crop:
        __lowerCAmelCase							     =     [self.center_crop(image=lowerCAmelCase_ ,  size=lowerCAmelCase_					) for image in images]

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

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

     __lowerCAmelCase							     =     [to_channel_dimension_format(lowerCAmelCase_ ,  lowerCAmelCase_					) for image in images]

     __lowerCAmelCase							     =     {'pixel_values': images}
     return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_					)








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






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

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [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 ,  lowerCAmelCase_					)

  a_      =			__mul__






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






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






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





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


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

