



'''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			):



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



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

						__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(
						    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  )
							__lowerCAmelCase       				=	VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase  )

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

						__lowerCAmelCase       				=	generator.manual_seed(0  )
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
						pipe.remove_unused_weights()
						pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__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(
						    prompt=UpperCamelCase      ,					image=UpperCamelCase      ,					text_to_image_strength=0.75      ,					generator=UpperCamelCase      ,					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([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]  )

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




'''simple docstring'''



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image






class        UpperCAmelCase__						(		unittest.TestCase				,							UpperCamelCase__			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	load_tool("image-question-answering"  )
						self.tool.setup()
						__lowerCAmelCase       				=	load_tool("image-question-answering"      ,					remote=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )