





'''simple docstring'''


import gc
import tempfile
import unittest

import numpy as np
import torch

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


_lowerCamelCase					:						Dict					     =							False



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







		'''simple docstring'''







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







		def 							A  (self    :   int   ):
			A					=   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""   )
			# remove text_unet
			pipe.remove_unused_weights()
			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(
			    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					=   VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase_   )

			pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			A					=   generator.manual_seed(0   )
			A					=   pipe(
			    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 							A  (self    :   Union[str, Any]   ):
			A					=   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""   )
			pipe.remove_unused_weights()
			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(
			    prompt=UpperCAmelCase_   ,   image=UpperCAmelCase_   ,   text_to_image_strength=0.75   ,   generator=UpperCAmelCase_   ,   guidance_scale=7.5   ,   num_inference_steps=50   ,   output_type="""numpy"""   ,   ).images

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

			assert image.shape == (1, 512, 512, 3)
			A					=   np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]   )

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





"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



