

'''simple docstring'''






from math import factorial






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Dict      ,lowercase__     :  str      ,lowercase__     :  Tuple    ):
   __lowercase													=				real
   if isinstance(lowercase__      ,lowercase__    ):
    __lowercase													=				[1] * rank
   else:
    __lowercase													=				rank






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






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				self.duals.copy()
   while cur[-1] == 0:
    cur.pop(-1    )
   return Dual(self.real      ,lowercase__    )






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

  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    __add__






  def __sub__(   self     :  Union[str, Any]      ,lowercase__     :  int    ):
   return self + other * -1






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

  SCREAMING_SNAKE_CASE					:						Dict           =    __mul__






  def __truediv__(   self     :  Tuple      ,lowercase__     :  Dict    ):
   if not isinstance(lowercase__      ,lowercase__    ):
    __lowercase													=				[]
    for i in self.duals:
     new_duals.append(i / other    )
    return Dual(self.real / other      ,lowercase__    )
   raise ValueError






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






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


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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


if __name__ == "__main__":
 import doctest

 doctest.testmod()


 def 	_A  (					A__   ):






  """simple docstring"""



  return y**2 * y**4






 print(differentiate(f, 9, 2))




'''simple docstring'''






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   import datasets

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				depth_estimator(
       [
           Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           # RGBA
           dataset[0]['''file'''],
           # LA
           dataset[1]['''file'''],
           # L
           dataset[2]['''file'''],
       ]    )
   self.assertEqual(
       [
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __lowercase													=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
   __lowercase													=				hashimage(outputs['''depth''']    )

   # This seems flaky.
   # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   # This is highly irregular to have no small tests.
   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

