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_    :		Dict							=	False


class 						a__							(       unittest.TestCase		):
	pass






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



	def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



	def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
						__a		 =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'				,						torch_dtype=torch.floataa   )
						pipe.to(_UpperCAmelCase   )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

						__a		 =       torch.manual_seed(0   )
						__a		 =       pipe.dual_guided(
						    prompt='first prompt'				,						image=_UpperCAmelCase				,						text_to_image_strength=0.75				,						generator=_UpperCAmelCase				,						guidance_scale=7.5				,						num_inference_steps=2				,						output_type='numpy'				,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
											pipe.save_pretrained(_UpperCAmelCase   )
											__a		 =       VersatileDiffusionPipeline.from_pretrained(_UpperCAmelCase				,						torch_dtype=torch.floataa   )
						pipe.to(_UpperCAmelCase   )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

						__a		 =       generator.manual_seed(0   )
						__a		 =       pipe.dual_guided(
						    prompt='first prompt'				,						image=_UpperCAmelCase				,						text_to_image_strength=0.75				,						generator=_UpperCAmelCase				,						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   )			->   str:
						__a		 =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'				,						torch_dtype=torch.floataa   )
						pipe.to(_UpperCAmelCase   )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

						__a		 =       'cyberpunk 2077'
						__a		 =       load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
						__a		 =       torch.manual_seed(0   )
						__a		 =       pipe.dual_guided(
						    prompt=_UpperCAmelCase				,						image=_UpperCAmelCase				,						text_to_image_strength=0.75				,						generator=_UpperCAmelCase				,						guidance_scale=7.5				,						num_inference_steps=5_0				,						output_type='numpy'				,						).images

						__a		 =       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		 =       np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]   )

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

						__a		 =       'A painting of a squirrel eating a burger '
						__a		 =       torch.manual_seed(0   )
						__a		 =       pipe.text_to_image(
						    prompt=_UpperCAmelCase				,						generator=_UpperCAmelCase				,						guidance_scale=7.5				,						num_inference_steps=5_0				,						output_type='numpy'   ).images

						__a		 =       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		 =       np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]   )

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

						__a		 =       pipe.image_variation(_UpperCAmelCase				,						generator=_UpperCAmelCase				,						output_type='numpy'   ).images

						__a		 =       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		 =       np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]   )

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





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


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

	testmod()




