

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


__snake_case							:			str      		= False



@nightly
@require_torch_gpu
class 					A__(unittest.TestCase       ):
						"""simple docstring"""






						def  UpperCamelCase__				(						self						)    ->   Any:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	Optional[Any]          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(_lowercase						)
									pipe.set_progress_bar_config(disable=_lowercase						)

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

									a_	:	Any          =   torch.manual_seed(0						)
									a_	:	Tuple          =   pipe(
									    prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type="""numpy"""     ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(_lowercase						)
												a_	:	Dict          =   VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowercase						)

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

									a_	:	Optional[Any]          =   generator.manual_seed(0						)
									a_	:	Optional[Any]          =   pipe(
									    prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			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  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									a_	:	Union[str, Any]          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
									pipe.remove_unused_weights()
									pipe.to(_lowercase						)
									pipe.set_progress_bar_config(disable=_lowercase						)

									a_	:	List[str]          =   """cyberpunk 2077"""
									a_	:	List[Any]          =   load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""						)
									a_	:	str          =   torch.manual_seed(0						)
									a_	:	Optional[Any]          =   pipe(
									    prompt=_lowercase     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type="""numpy"""     ,			).images

									a_	:	Dict          =   image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a_	:	Union[str, Any]          =   np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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





from dataclasses import dataclass
from typing import Callable, Optional

import datasets



@dataclass
class 					A__(datasets.BuilderConfig       ):
						"""simple docstring"""






						_A						:							Optional[Callable]									= None
						_A						:							Optional[dict]									= None
						_A						:							Optional[datasets.Features]									= None





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									assert self.generator is not None, "generator must be specified"

									if self.gen_kwargs is None:
												a_	:	Tuple          =   {}







class 					A__(datasets.GeneratorBasedBuilder       ):
						"""simple docstring"""






						_A						:							Dict									= GeneratorConfig





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									return datasets.DatasetInfo(features=self.config.features						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,			gen_kwargs=self.config.gen_kwargs						)]





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   Tuple:
									for idx, ex in enumerate(self.config.generator(**_lowercase						)						):
												yield idx, ex


