"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCAmelCase    :							Any													=						False

class       a_      (     unittest.TestCase			):
					pass

@nightly
@require_torch_gpu
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )

							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt="""first prompt"""  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						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__     )
									lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained(snake_case__  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   generator.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt="""first prompt"""  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						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 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   """cyberpunk 2077"""
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt=snake_case__  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type="""numpy"""  ,						).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]     )

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

							lowerCAmelCase__        =   """A painting of a squirrel eating a burger """
							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.text_to_image(
							    prompt=snake_case__  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type="""numpy"""     ).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   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-1

							lowerCAmelCase__        =   pipe.image_variation(snake_case__  ,						generator=snake_case__  ,						output_type="""numpy"""     ).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]     )

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





"""simple docstring"""




from math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		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		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


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

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




