

'''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 lowerCamelCase   (    unittest.TestCase				,				__lowerCAmelCase				):

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					load_tool('image-question-answering' )
					self.tool.setup()
					snake_case							=					load_tool('image-question-answering',						remote=lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' )
					snake_case							=					self.tool(lowercase_,						'How many cats are sleeping on the couch?' )
					self.assertEqual(lowercase_,						'2' )

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' )
					snake_case							=					self.remote_tool(lowercase_,						'How many cats are sleeping on the couch?' )
					self.assertEqual(lowercase_,						'2' )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' )
					snake_case							=					self.tool(image=lowercase_,						question='How many cats are sleeping on the couch?' )
					self.assertEqual(lowercase_,						'2' )






				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' )
					snake_case							=					self.remote_tool(image=lowercase_,						question='How many cats are sleeping on the couch?' )
					self.assertEqual(lowercase_,						'2' )









"""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


__snake_case												=							False


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

     '''simple docstring'''


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


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _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 				_UpperCAmelCase   (   self				)							->				Dict:
          _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([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]				)

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