
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__          =False



class 		UpperCamelCase__  (	unittest.TestCase   ):
   pass





@nightly
@require_torch_gpu
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: List[str]       ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Any		    =  VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
    # remove text_unet
    pipe.remove_unused_weights()
    pipe.to(snake_case_       )
    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  Dict		    =  '''A painting of a squirrel eating a burger '''
    __a			:  str		    =  torch.manual_seed(0       )
    __a			:  Any		    =  pipe(
        prompt=snake_case_ ,    generator=snake_case_ ,    guidance_scale=7.5 ,    num_inference_steps=2 ,    output_type='''numpy'''       ).images

    with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(snake_case_       )
     __a			:  List[Any]		    =  VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case_       )
    pipe.to(snake_case_       )
    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  Any		    =  generator.manual_seed(0       )
    __a			:  Optional[Any]		    =  pipe(
        prompt=snake_case_ ,    generator=snake_case_ ,    guidance_scale=7.5 ,    num_inference_steps=2 ,    output_type='''numpy'''       ).images

    assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"







   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  str		    =  VersatileDiffusionTextToImagePipeline.from_pretrained(
        '''shi-labs/versatile-diffusion''' ,    torch_dtype=torch.floataa       )
    pipe.to(snake_case_       )
    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  int		    =  '''A painting of a squirrel eating a burger '''
    __a			:  Union[str, Any]		    =  torch.manual_seed(0       )
    __a			:  str		    =  pipe(
        prompt=snake_case_ ,    generator=snake_case_ ,    guidance_scale=7.5 ,    num_inference_steps=5_0 ,    output_type='''numpy'''       ).images

    __a			:  Optional[int]		    =  image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __a			:  List[str]		    =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


from math import pi, sqrt




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):
 if num <= 0:
  raise ValueError('''math domain error'''  )
 if num > 1_71.5:
  raise OverflowError('''math range error'''  )
 elif num - int(lowerCAmelCase__  ) not in (0, 0.5):
  raise NotImplementedError('''num must be an integer or a half-integer'''  )
 elif num == 0.5:
  return sqrt(lowerCAmelCase__  )
 else:
  return 1.0 if num == 1 else (num - 1) * gamma(num - 1  )




def        __UpperCamelCase					(    ):
 assert gamma(0.5  ) == sqrt(lowerCAmelCase__  )
 assert gamma(1  ) == 1.0
 assert gamma(2  ) == 1.0


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      lowercase__          =1.0
      while num:
            lowercase__          =float(input('Gamma of: '))
            print(F"""gamma({num}) = {gamma(num)}""")
            print('\nEnter 0 to exit...')
