






from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[Any]        = ['''pixel_values''']







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Optional[Dict[str, int]] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BILINEAR							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  size if size is not None else {"shortest_edge": 2_56}
      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
      UpperCamelCase__			:   int       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)
      UpperCamelCase__			:   str       				=  do_resize
      UpperCamelCase__			:   Optional[Any]       				=  size
      UpperCamelCase__			:   str       				=  resample
      UpperCamelCase__			:   Union[str, Any]       				=  do_center_crop
      UpperCamelCase__			:   Optional[int]       				=  crop_size
      UpperCamelCase__			:   List[str]       				=  do_rescale
      UpperCamelCase__			:   Optional[int]       				=  rescale_factor
      UpperCamelCase__			:   Any       				=  do_normalize
      UpperCamelCase__			:   List[Any]       				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase__			:   Optional[Any]       				=  image_std if image_std is not None else IMAGENET_STANDARD_STD







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BICUBIC							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      if "shortest_edge" not in size:
           raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
      UpperCamelCase__			:   Union[str, Any]       				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE							,   size=size["shortest_edge"]							,   default_to_square=SCREAMING_SNAKE_CASE						)
      return resize(SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Any							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  get_size_dict(SCREAMING_SNAKE_CASE						)
      if "height" not in size or "width" not in size:
           raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'						)
      return center_crop(SCREAMING_SNAKE_CASE							,   size=(size["height"], size["width"])							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   float							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   List[str]							,   ):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[float] = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, TensorType]] = None							,   SCREAMING_SNAKE_CASE					:   Union[str, ChannelDimension] = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   Union[str, Any]       				=  size if size is not None else self.size
      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   Dict       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   Any       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Dict       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)
      UpperCamelCase__			:   Any       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   str       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   Any       				=  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__			:   Dict       				=  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__			:   List[Any]       				=  image_std if image_std is not None else self.image_std

      UpperCamelCase__			:   Dict       				=  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:
           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.
      UpperCamelCase__			:   Any       				=  [to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

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

      if do_center_crop:
           UpperCamelCase__			:   List[str]       				=  [self.center_crop(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE						) for image in images]

      if do_rescale:
           UpperCamelCase__			:   Tuple       				=  [self.rescale(image=SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE						) for image in images]

      if do_normalize:
           UpperCamelCase__			:   Optional[Any]       				=  [self.normalize(image=SCREAMING_SNAKE_CASE							,   mean=SCREAMING_SNAKE_CASE							,   std=SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Any       				=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Any       				=  {"pixel_values": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE							,   tensor_type=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Tuple] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"						)

           if is_torch_tensor(SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Tuple       				=  target_sizes.numpy()

           UpperCamelCase__			:   str       				=  []

           for idx in range(len(SCREAMING_SNAKE_CASE						)						):
                UpperCamelCase__			:   List[str]       				=  torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0						)							,   size=target_sizes[idx]							,   mode="bilinear"							,   align_corners=SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   int       				=  resized_logits[0].argmax(dim=0						)
                semantic_segmentation.append(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   Dict       				=  logits.argmax(dim=1						)
           UpperCamelCase__			:   Union[str, Any]       				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

      return semantic_segmentation







from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






      return (
          F'{self.real}+'
          F'{"+".join(str(SCREAMING_SNAKE_CASE						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
      )







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






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

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i * other						)
           return Dual(self.real * other							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  [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							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i / other						)
           return Dual(self.real / other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  []
           for i in self.duals:
                new_duals.append(i // other						)
           return Dual(self.real // other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if n < 0 or isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           raise ValueError("power must be a positive integer"						)
      if n == 0:
           return 1
      if n == 1:
           return self
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     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"			)
     UpperCamelCase__			:   Optional[Any]       				=  Dual(__lowerCAmelCase					,    1			)
     UpperCamelCase__			:   Any       				=  func(__lowerCAmelCase			)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))