






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor
class 				A_     (    unittest.TestCase      ):






 '''simple docstring'''




 def __init__(						self				,	_A				,	_A = True				,	_A = None				,	_A = 32				,	_A = True				,	_A = 1 / 255				,	_A = True				,	_A = True				,	_A = [0.48145466, 0.4578275, 0.40821073]				,	_A = [0.26862954, 0.26130258, 0.27577711]				,	_A = True				,	_A=7				,	_A=30				,	_A=400				,	_A=3				,	)			->		int:

     """simple docstring"""



     _UpperCAmelCase					:   Any      	=					parent
     _UpperCAmelCase					:   List[str]      	=					do_resize
     _UpperCAmelCase					:   str      	=					size if size is not None else {'''shortest_edge''': 288}
     _UpperCAmelCase					:   Union[str, Any]      	=					size_divisor
     _UpperCAmelCase					:   Optional[Any]      	=					do_rescale
     _UpperCAmelCase					:   Tuple      	=					rescale_factor
     _UpperCAmelCase					:   List[str]      	=					do_normalize
     _UpperCAmelCase					:   Tuple      	=					do_center_crop
     _UpperCAmelCase					:   Optional[int]      	=					image_mean
     _UpperCAmelCase					:   Optional[int]      	=					image_std
     _UpperCAmelCase					:   List[Any]      	=					do_pad
     _UpperCAmelCase					:   Optional[int]      	=					batch_size
     _UpperCAmelCase					:   List[Any]      	=					num_channels
     _UpperCAmelCase					:   List[str]      	=					min_resolution
     _UpperCAmelCase					:   int      	=					max_resolution


 def 			snake_case__					(						self)			->		int:

     """simple docstring"""



     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "size_divisor": self.size_divisor,
     }


 def 			snake_case__					(						self				,	_A				,	_A=False)			->		List[Any]:

     """simple docstring"""



     if not batched:
         _UpperCAmelCase					:   List[str]      	=					self.size['''shortest_edge''']
         _UpperCAmelCase					:   Optional[int]      	=					image_inputs[0]
         if isinstance(A_				,	Image.Image):
             _UpperCAmelCase					:   Any      	=					image.size
         else:
             _UpperCAmelCase					:   List[str]      	=					image.shape[1], image.shape[2]
         _UpperCAmelCase					:   int      	=					size / min(A_				,	A_)
         if h < w:
             _UpperCAmelCase					:   Dict      	=					size, scale * w
         else:
             _UpperCAmelCase					:   Any      	=					scale * h, size

         _UpperCAmelCase					:   Optional[Any]      	=					int((1333 / 800) * size)
         if max(A_				,	A_) > max_size:
             _UpperCAmelCase					:   Optional[Any]      	=					max_size / max(A_				,	A_)
             _UpperCAmelCase					:   Optional[int]      	=					newh * scale
             _UpperCAmelCase					:   List[Any]      	=					neww * scale

         _UpperCAmelCase					:   int      	=					int(newh + 0.5), int(neww + 0.5)
         _UpperCAmelCase					:   Dict      	=					(
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

     else:
         _UpperCAmelCase					:   Tuple      	=					[]
         for image in image_inputs:
             _UpperCAmelCase					:   str      	=					self.get_expected_values([image])
             expected_values.append((expected_height, expected_width))
         _UpperCAmelCase					:   Any      	=					max(A_				,	key=lambda _A: item[0])[0]
         _UpperCAmelCase					:   Tuple      	=					max(A_				,	key=lambda _A: item[1])[1]

     return expected_height, expected_width



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






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       BridgeTowerImageProcessor if is_vision_available() else None


 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					BridgeTowerImageProcessingTester(self)


 @property
 def 			snake_case__					(						self)			->		str:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 			snake_case__					(						self)			->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase					:   int      	=					self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(A_				,	'''image_mean'''))
     self.assertTrue(hasattr(A_				,	'''image_std'''))
     self.assertTrue(hasattr(A_				,	'''do_normalize'''))
     self.assertTrue(hasattr(A_				,	'''do_resize'''))
     self.assertTrue(hasattr(A_				,	'''size'''))
     self.assertTrue(hasattr(A_				,	'''size_divisor'''))


 def 			snake_case__					(						self)			->		List[str]:

     """simple docstring"""



     pass


 def 			snake_case__					(						self)			->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase					:   Dict      	=					self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase					:   List[str]      	=					prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A_)
     for image in image_inputs:
         self.assertIsInstance(A_				,	Image.Image)

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

     _UpperCAmelCase					:   Optional[Any]      	=					self.image_processor_tester.get_expected_values(A_)
     self.assertEqual(
         encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

     # Test batched
     _UpperCAmelCase					:   str      	=					image_processing(A_				,	return_tensors='''pt''').pixel_values

     _UpperCAmelCase					:   int      	=					self.image_processor_tester.get_expected_values(A_				,	batched=A_)
     self.assertEqual(
         encoded_images.shape				,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,	)


 def 			snake_case__					(						self)			->		int:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     _UpperCAmelCase					:   Dict      	=					prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A_				,	numpify=A_)
     for image in image_inputs:
         self.assertIsInstance(A_				,	np.ndarray)

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

     _UpperCAmelCase					:   Dict      	=					self.image_processor_tester.get_expected_values(A_)
     self.assertEqual(
         encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

     # Test batched
     _UpperCAmelCase					:   Union[str, Any]      	=					image_processing(A_				,	return_tensors='''pt''').pixel_values

     _UpperCAmelCase					:   List[str]      	=					self.image_processor_tester.get_expected_values(A_				,	batched=A_)
     self.assertEqual(
         encoded_images.shape				,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,	)


 def 			snake_case__					(						self)			->		int:

     """simple docstring"""



     _UpperCAmelCase					:   int      	=					self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     _UpperCAmelCase					:   Any      	=					prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A_				,	torchify=A_)
     for image in image_inputs:
         self.assertIsInstance(A_				,	torch.Tensor)

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

     _UpperCAmelCase					:   Optional[Any]      	=					self.image_processor_tester.get_expected_values(A_)
     self.assertEqual(
         encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

     # Test batched
     _UpperCAmelCase					:   Optional[Any]      	=					image_processing(A_				,	return_tensors='''pt''').pixel_values

     _UpperCAmelCase					:   Union[str, Any]      	=					self.image_processor_tester.get_expected_values(A_				,	batched=A_)
     self.assertEqual(
         encoded_images.shape				,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,	)






"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  b
  if function(_UpperCamelCase    ) == 0:  # one of the a or b is a root for the function
    return a
  elif function(_UpperCamelCase    ) == 0:
    return b
  elif (
      function(_UpperCamelCase    ) * function(_UpperCamelCase    ) > 0
  ):  # if none of these are root and they are both positive or negative,
    # then this algorithm can't find the root
    raise ValueError('''could not find root in given interval.'''    )
  else:
    _lowercase:      float   			=  start + (end - start) / 2.0
    while abs(start - mid    ) > 10**-7:  # until precisely equals to 10^-7
      if function(_UpperCamelCase    ) == 0:
        return mid
      elif function(_UpperCamelCase    ) * function(_UpperCamelCase    ) < 0:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()
