

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 			_lowercase    (      __UpperCamelCase     :		Any   ,       __UpperCamelCase     :		int=256 ):
      snake_case__					=   min(img.size )

      if s < target_image_size:
            raise ValueError(F'''min dim for image {s} < {target_image_size}''' )

      snake_case__					=   target_image_size / s
      snake_case__					=   (round(r * img.size[1] ), round(r * img.size[0] ))
      snake_case__					=   TF.resize(__UpperCamelCase   ,       __UpperCamelCase   ,       interpolation=PIL.Image.LANCZOS )
      snake_case__					=   TF.center_crop(__UpperCamelCase   ,       output_size=2 * [target_image_size] )
      snake_case__					=   torch.unsqueeze(T.ToTensor()(__UpperCamelCase )   ,       0 )
      return img





def 			_lowercase    (      __UpperCamelCase     :		str ):
      snake_case__					=   2.0 * x - 1.0
      return x





def 			_lowercase    (      __UpperCamelCase     :		Dict   ,       __UpperCamelCase     :		List[str]=True   ,       __UpperCamelCase     :		List[Any]="RGB" ):
      snake_case__					=   x.detach().cpu()
      if process:
            snake_case__					=   post_process_tensor(__UpperCamelCase )
      snake_case__					=   x.numpy()
      if process:
            snake_case__					=   (255 * x).astype(np.uinta )
      snake_case__					=   Image.fromarray(__UpperCamelCase )
      if not x.mode == mode:
            snake_case__					=   x.convert(__UpperCamelCase )
      return x





def 			_lowercase    (      __UpperCamelCase     :		int ):
      snake_case__					=   torch.clamp(__UpperCamelCase   ,       -1.0   ,       1.0 )
      snake_case__					=   (x + 1.0) / 2.0
      snake_case__					=   x.permute(1   ,       2   ,       0 )
      return x





def 			_lowercase    (      __UpperCamelCase     :		Any ):
      snake_case__					=   post_process_tensor(x.squeeze() )
      return x.permute(2   ,       0   ,       1 ).unsqueeze(0 )









lowerCAmelCase						:   Optional[Any]										=    1000





def 			_lowercase    (      __UpperCamelCase     :		float ):
      return x * x





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		int = 4 ):
      assert callable(
          __UpperCamelCase ), F'''the function(object) passed should be callable your input : {function}'''
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''a should be float or integer your input : {a}'''
      assert isinstance(function(__UpperCamelCase )   ,       (float, int) ), (
          "the function should return integer or float return type of your function, "
          F'''{type(__UpperCamelCase )}'''
      )
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''b should be float or integer your input : {b}'''
      assert (
          isinstance(__UpperCamelCase   ,       __UpperCamelCase ) and precision > 0
      ), F'''precision should be positive integer your input : {precision}'''

      # just applying the formula of simpson for approximate integration written in
      # mentioned article in first comment of this file and above this function

      snake_case__					=   (b - a) / N_STEPS
      snake_case__					=   function(__UpperCamelCase ) + function(__UpperCamelCase )

      for i in range(1   ,       __UpperCamelCase ):
            snake_case__					=   a + h * i
            result += function(__UpperCamelCase ) * (4 if i % 2 else 2)

      result *= h / 3
      return round(__UpperCamelCase   ,       __UpperCamelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






