





'''simple docstring'''






from math import factorial







class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					Optional[Any]							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					str  )     ->						Tuple:
    _UpperCamelCase       			=real
    if isinstance(UpperCamelCase__							,	UpperCamelCase__  ):
      _UpperCamelCase       			=[1] * rank
    else:
      _UpperCamelCase       			=rank


  def __repr__(				self    :					Optional[int]  )     ->						Dict:
    return (
        F'''{self.real}+'''
        F'''{'+'.join(str(UpperCamelCase__  )+'E'+str(n+1  )for n,dual in enumerate(self.duals  )  )}'''
    )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						List[str]:
    _UpperCamelCase       			=self.duals.copy()
    while cur[-1] == 0:
      cur.pop(-1  )
    return Dual(self.real							,	UpperCamelCase__  )


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

  lowerCAmelCase_					=				__add__


  def __sub__(				self    :					Tuple							,	UpperCamelCase__    :					Optional[int]  )     ->						Tuple:
    return self + other * -1


  def __mul__(				self    :					Tuple							,	UpperCamelCase__    :					Optional[int]  )     ->						Union[str, Any]:
    if not isinstance(UpperCamelCase__							,	UpperCamelCase__  ):
      _UpperCamelCase       			=[]
      for i in self.duals:
        new_duals.append(i * other  )
      return Dual(self.real * other							,	UpperCamelCase__  )
    _UpperCamelCase       			=[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							,	UpperCamelCase__  )

  lowerCAmelCase_					=				__mul__


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


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




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




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if not callable(__SCREAMING_SNAKE_CASE   ):
    raise ValueError('''differentiate() requires a function as input for func'''   )
  if not isinstance(__SCREAMING_SNAKE_CASE  ,      (float, int)   ):
    raise ValueError('''differentiate() requires a float as input for position'''   )
  if not isinstance(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):
    raise ValueError('''differentiate() requires an int as input for order'''   )
  _UpperCamelCase       			=Dual(__SCREAMING_SNAKE_CASE  ,      1   )
  _UpperCamelCase       			=func(__SCREAMING_SNAKE_CASE   )
  if order == 0:
    return result.real
  return result.duals[order - 1] * factorial(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


      def 					_a   (__SCREAMING_SNAKE_CASE   ):


        """simple docstring"""

        return y**2 * y**4
      print(differentiate(f, 9, 2))













'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					Union[str, Any]=400							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[str]=0.9							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					List[str]=[0.5, 0.5, 0.5]							,	)     ->						Dict:
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 30}
    _UpperCamelCase       			=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize_and_center_crop
    _UpperCamelCase       			=size
    _UpperCamelCase       			=crop_pct
    _UpperCamelCase       			=crop_size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std




  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }







@require_torch
@require_vision
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				PoolFormerImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						int:
    _UpperCamelCase       			=PoolFormerImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize_and_center_crop'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''crop_pct'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 30}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 30, '''width''': 30}  )

    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict							,	size=42							,	crop_size=84  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 42}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 84, '''width''': 84}  )


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[Any]:
    pass


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	Image.Image  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values

    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[int]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)




  def   UpperCamelCase__      (				self    :					str  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)






