


"""simple docstring"""







import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class    __A       ( unittest.TestCase    ):






 """simple docstring"""




 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a					=1
     a					=3
     a					=8
     a					=8

     a					=torch.rand((batch_size, num_channels, height, width)		)

     return sample


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  List[str]:


     if isinstance(image[0]				,					PIL.Image.Image		):
         return np.stack([np.array(__UpperCamelCase		) for i in image]				,					axis=0		)
     elif isinstance(__UpperCamelCase				,					torch.Tensor		):
         return image.cpu().numpy().transpose(0				,					2				,					3				,					1		)
     return image


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					=VaeImageProcessor(do_resize=__UpperCamelCase				,					do_normalize=__UpperCamelCase		)

     a					=self.dummy_sample
     a					=self.to_np(__UpperCamelCase		)

     for output_type in ["pt", "np", "pil"]:
         a					=image_processor.postprocess(
             image_processor.preprocess(__UpperCamelCase		)				,					output_type=__UpperCamelCase				,					)
         a					=self.to_np(__UpperCamelCase		)
         a					=(input_np * 255).round() if output_type == '''pil''' else input_np
         assert (
             np.abs(in_np - out_np		).max() < 1E-6
         ), f'''decoded output does not match input for output_type {output_type}'''


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     a					=VaeImageProcessor(do_resize=__UpperCamelCase				,					do_normalize=__UpperCamelCase		)
     a					=self.dummy_sample.cpu().numpy().transpose(0				,					2				,					3				,					1		)

     for output_type in ["pt", "np", "pil"]:
         a					=image_processor.postprocess(image_processor.preprocess(__UpperCamelCase		)				,					output_type=__UpperCamelCase		)

         a					=self.to_np(__UpperCamelCase		)
         a					=(input_np * 255).round() if output_type == '''pil''' else input_np
         assert (
             np.abs(in_np - out_np		).max() < 1E-6
         ), f'''decoded output does not match input for output_type {output_type}'''


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     a					=VaeImageProcessor(do_resize=__UpperCamelCase				,					do_normalize=__UpperCamelCase		)

     a					=self.dummy_sample.cpu().numpy().transpose(0				,					2				,					3				,					1		)
     a					=image_processor.numpy_to_pil(__UpperCamelCase		)

     for output_type in ["pt", "np", "pil"]:
         a					=image_processor.postprocess(image_processor.preprocess(__UpperCamelCase		)				,					output_type=__UpperCamelCase		)
         for i, o in zip(__UpperCamelCase				,					__UpperCamelCase		):
             a					=np.array(__UpperCamelCase		)
             a					=self.to_np(__UpperCamelCase		) if output_type == '''pil''' else (self.to_np(__UpperCamelCase		) * 255).round()
             assert (
                 np.abs(in_np - out_np		).max() < 1E-6
             ), f'''decoded output does not match input for output_type {output_type}'''


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     a					=VaeImageProcessor(do_resize=__UpperCamelCase				,					do_normalize=__UpperCamelCase		)

     a					=self.dummy_sample
     a					=input_pt_ad.squeeze(0		)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)
     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     a					=self.to_np(self.dummy_sample		)
     a					=input_np_ad.squeeze(0		)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)
     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     assert np.abs(out_pt_ad - out_pt_ad		).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad		).max() < 1E-6





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a					=VaeImageProcessor(do_resize=__UpperCamelCase				,					do_normalize=__UpperCamelCase		)

     a					=self.dummy_sample
     a					=list(__UpperCamelCase		)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     a					=self.to_np(self.dummy_sample		)
     list(__UpperCamelCase		)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     a					=image_processor.postprocess(
         image_processor.preprocess(__UpperCamelCase		)				,					output_type='''np'''				,					)

     assert np.abs(out_pt_ad - out_pt_list		).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list		).max() < 1E-6



"""simple docstring"""







from math import gcd
def _A		(		lowercase = 1_20_00	):







    """simple docstring"""
    a					=0
    for d in range(max_d + 1	):
        for n in range(d // 3 + 1 ,					(d + 1) // 2	):
            if gcd(lowercase ,					lowercase	) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
       print(F'{solution() = }')