


"""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 ViTImageProcessor


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=7  ,					__UpperCAmelCase=3  ,					__UpperCAmelCase=18  ,					__UpperCAmelCase=30  ,					__UpperCAmelCase=4_00  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=[0.5, 0.5, 0.5]  ,					__UpperCAmelCase=[0.5, 0.5, 0.5]  ,					)				->					List[str]:
     a   :	Any						=    size if size is not None else {'height': 18, 'width': 18}
     a   :	str						=    parent
     a   :	List[Any]						=    batch_size
     a   :	Optional[Any]						=    num_channels
     a   :	List[str]						=    image_size
     a   :	str						=    min_resolution
     a   :	int						=    max_resolution
     a   :	int						=    do_resize
     a   :	Tuple						=    size
     a   :	Optional[int]						=    do_normalize
     a   :	Optional[int]						=    image_mean
     a   :	Tuple						=    image_std






  def 							lowercase_     (						self						)				->					str:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }



@require_torch
@require_vision
class 	A_       (     _a      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Union[str, Any]   =						ViTImageProcessor if is_vision_available() else None
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	List[Any]						=    ViTImageProcessingTester(self						)
  @property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	Any						=    self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'image_mean'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'image_std'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'do_normalize'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'do_resize'						)						)
     self.assertTrue(hasattr(__UpperCAmelCase  ,					'size'						)						)
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	Any						=    self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size  ,					{'height': 18, 'width': 18}						)

     a   :	List[Any]						=    self.image_processing_class.from_dict(self.image_processor_dict  ,					size=42						)
     self.assertEqual(image_processor.size  ,					{'height': 42, 'width': 42}						)
  def 							lowercase_     (						self						)				->					int:
     pass
  def 							lowercase_     (						self						)				->					Optional[Any]:
     # Initialize image_processing
     a   :	Optional[int]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     a   :	str						=    prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__UpperCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase  ,					Image.Image						)

     # Test not batched input
     a   :	Optional[Any]						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)

     # Test batched
     a   :	List[Any]						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)
  def 							lowercase_     (						self						)				->					List[Any]:
     # Initialize image_processing
     a   :	Union[str, Any]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     a   :	int						=    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
     a   :	Dict						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)

     # Test batched
     a   :	str						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)






  def 							lowercase_     (						self						)				->					Dict:
     # Initialize image_processing
     a   :	Optional[Any]						=    self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     a   :	List[str]						=    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
     a   :	Tuple						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)

     # Test batched
     a   :	str						=    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.size['height'],
             self.image_processor_tester.size['width'],
         )  ,					)






from collections.abc import Callable

_SCREAMING_SNAKE_CASE     =     Callable[[float], float]  # type alias for a real -> real function
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					)					->float:

  '''simple docstring'''




  a__  		=  float(lowerCamelCase__       )
  while True:
    try:
      a__  		=  prev_guess - function(lowerCamelCase__       ) / derivative(lowerCamelCase__       )
    except ZeroDivisionError:
      raise ZeroDivisionError("Could not find root"       ) from None
    if abs(prev_guess - next_guess       ) < 10**-5:
      return next_guess
    a__  		=  next_guess
def 					__lowercase						(    lowerCamelCase__       )					->float:

  '''simple docstring'''




  return (x**3) - (2 * x) - 5
def 					__lowercase						(    lowerCamelCase__       )					->float:

  '''simple docstring'''




  return 3 * (x**2) - 2


if __name__ == "__main__":
     print(newton(f, fa, 3))

