


"""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 EfficientNetImageProcessor



class 		snake_case_(							unittest.TestCase      ):






     def __init__(						self	:       Dict       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       List[str]=1_3       ,					UpperCamelCase_	:       int=3       ,					UpperCamelCase_	:       str=1_8       ,					UpperCamelCase_	:       int=3_0       ,					UpperCamelCase_	:       int=4_0_0       ,					UpperCamelCase_	:       Optional[Any]=True       ,					UpperCamelCase_	:       List[Any]=None       ,					UpperCamelCase_	:       Dict=True       ,					UpperCamelCase_	:       Dict=[0.5, 0.5, 0.5]       ,					UpperCamelCase_	:       List[str]=[0.5, 0.5, 0.5]       ,					):
           lowerCAmelCase       :						Optional[int]							= size if size is not None else {'''height''': 1_8, '''width''': 1_8}
           lowerCAmelCase       :						Tuple							= parent
           lowerCAmelCase       :						Tuple							= batch_size
           lowerCAmelCase       :						Any							= num_channels
           lowerCAmelCase       :						Tuple							= image_size
           lowerCAmelCase       :						Any							= min_resolution
           lowerCAmelCase       :						Any							= max_resolution
           lowerCAmelCase       :						Tuple							= do_resize
           lowerCAmelCase       :						List[str]							= size
           lowerCAmelCase       :						int							= do_normalize
           lowerCAmelCase       :						Tuple							= image_mean
           lowerCAmelCase       :						Optional[int]							= image_std






     def   lowerCamelCase__  (						self	:       Tuple					):
           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 		snake_case_(							a__				,       unittest.TestCase      ):
     __UpperCamelCase	=       EfficientNetImageProcessor if is_vision_available() else None






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						List[str]							= EfficientNetImageProcessorTester(self					)






     @property
     def   lowerCamelCase__  (						self	:       Any					):
           return self.image_processor_tester.prepare_image_processor_dict()






     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						Dict							= 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   lowerCamelCase__  (						self	:       str					):
           lowerCAmelCase       :						List[str]							= self.image_processing_class.from_dict(self.image_processor_dict					)
           self.assertEqual(image_processor.size       ,					{'''height''': 1_8, '''width''': 1_8}					)

           lowerCAmelCase       :						Tuple							= self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2					)
           self.assertEqual(image_processor.size       ,					{'''height''': 4_2, '''width''': 4_2}					)






     def   lowerCamelCase__  (						self	:       str					):
           # Initialize image_processing
           lowerCAmelCase       :						Dict							= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCAmelCase       :						List[Any]							= prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=UpperCamelCase_					)
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_       ,					Image.Image					)

           # Test not batched input
           lowerCAmelCase       :						Union[str, 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
           lowerCAmelCase       :						Dict							= 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   lowerCamelCase__  (						self	:       str					):
           # Initialize image_processing
           lowerCAmelCase       :						Optional[Any]							= self.image_processing_class(**self.image_processor_dict					)
           # create random numpy tensors
           lowerCAmelCase       :						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
           lowerCAmelCase       :						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
           lowerCAmelCase       :						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   lowerCamelCase__  (						self	:       int					):
           # Initialize image_processing
           lowerCAmelCase       :						Union[str, Any]							= self.image_processing_class(**self.image_processor_dict					)
           # create random PyTorch tensors
           lowerCAmelCase       :						Tuple							= 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
           lowerCAmelCase       :						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
           lowerCAmelCase       :						Dict							= 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'''],
               )       ,					)






"""simple docstring"""





from collections.abc import Callable

snake_case__		:  Optional[int]     					=					Callable[[float], float]  # type alias for a real -> real function


def   _snake_case    (    _snake_case					:      RealFunc			, _snake_case					:      RealFunc			, _snake_case					:      int			, ):
      lowerCAmelCase       :						Dict							= float(_snake_case      )
      while True:
            try:
                  lowerCAmelCase       :						Optional[Any]							= prev_guess - function(_snake_case      ) / derivative(_snake_case      )
            except ZeroDivisionError:
                  raise ZeroDivisionError('''Could not find root'''      ) from None
            if abs(prev_guess - next_guess      ) < 10**-5:
                  return next_guess
            lowerCAmelCase       :						Optional[int]							= next_guess


def   _snake_case    (    _snake_case					:      float      ):
      return (x**3) - (2 * x) - 5


def   _snake_case    (    _snake_case					:      float      ):
      return 3 * (x**2) - 2


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


