
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 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=2_2_4						,						__SCREAMING_SNAKE_CASE=3_0						,						__SCREAMING_SNAKE_CASE=4_0_0						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,						__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,						)  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				size if size is not None else {'''height''': 1_8, '''width''': 1_8}
           UpperCamelCase__    :   Optional[Any]					=				parent
           UpperCamelCase__    :   Dict					=				batch_size
           UpperCamelCase__    :   int					=				num_channels
           UpperCamelCase__    :   Optional[int]					=				image_size
           UpperCamelCase__    :   str					=				min_resolution
           UpperCamelCase__    :   List[str]					=				max_resolution
           UpperCamelCase__    :   Optional[int]					=				do_resize
           UpperCamelCase__    :   int					=				size
           UpperCamelCase__    :   List[Any]					=				do_normalize
           UpperCamelCase__    :   int					=				image_mean
           UpperCamelCase__    :   List[str]					=				image_std






     def    __SCREAMING_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,
           }




@require_torch
@require_vision
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ViTImageProcessor if is_vision_available() else None






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				EfficientFormerImageProcessorTester(self  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           return self.image_proc_tester.prepare_image_processor_dict()






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




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.image_processing_class(**self.image_processor_dict  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''image_mean'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''image_std'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''do_normalize'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''do_resize'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''size'''  )  )






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




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.image_processing_class(**self.image_processor_dict  )
           # create random PIL images
           UpperCamelCase__    :   List[str]					=				prepare_image_inputs(self.image_proc_tester						,						equal_resolution=__SCREAMING_SNAKE_CASE  )
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE						,						Image.Image  )

           # Test not batched input
           UpperCamelCase__    :   Optional[Any]					=				image_processor(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)

           # Test batched
           UpperCamelCase__    :   List[str]					=				image_processor(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.image_processing_class(**self.image_processor_dict  )
           # create random numpy tensors
           UpperCamelCase__    :   Tuple					=				prepare_image_inputs(self.image_proc_tester						,						equal_resolution=__SCREAMING_SNAKE_CASE						,						numpify=__SCREAMING_SNAKE_CASE  )
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE						,						np.ndarray  )

           # Test not batched input
           UpperCamelCase__    :   List[Any]					=				image_processor(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)

           # Test batched
           UpperCamelCase__    :   str					=				image_processor(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.image_processing_class(**self.image_processor_dict  )
           # create random PyTorch tensors
           UpperCamelCase__    :   Optional[int]					=				prepare_image_inputs(self.image_proc_tester						,						equal_resolution=__SCREAMING_SNAKE_CASE						,						torchify=__SCREAMING_SNAKE_CASE  )
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE						,						torch.Tensor  )

           # Test not batched input
           UpperCamelCase__    :   Dict					=				image_processor(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)

           # Test batched
           UpperCamelCase__    :   Tuple					=				image_processor(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).pixel_values
           self.assertEqual(
               encoded_images.shape						,						(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )						,						)




from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          return x**3 + x**2

    print("f(x) = x^3 + x^2")
    print("The area between the curve, x = -5, x = 5 and the x axis is:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


