



'''simple docstring'''



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        UpperCAmelCase__						(		UpperCamelCase__			):



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Tuple:
						super().__init__()
						self.register_modules(unet=UpperCamelCase      ,					scheduler=UpperCamelCase  )



					@torch.no_grad()
					def __call__(     self      ,					UpperCamelCase = 1      ,					UpperCamelCase = None      ,					UpperCamelCase = 50      ,					UpperCamelCase = "pil"      ,					UpperCamelCase = True      ,					**UpperCamelCase      ,					)  ->      Union[ImagePipelineOutput, Tuple]:
						__lowerCAmelCase       				=	torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,					generator=UpperCamelCase      ,					)
						__lowerCAmelCase       				=	image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(UpperCamelCase  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
							# 1. predict noise model_output
							__lowerCAmelCase       				=	self.unet(UpperCamelCase      ,					UpperCamelCase  ).sample

							# 2. predict previous mean of image x_t-1 and add variance depending on eta
							# eta corresponds to η in paper and should be between [0, 1]
							# do x_t -> x_t-1
							__lowerCAmelCase       				=	self.scheduler.step(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  ).prev_sample

						__lowerCAmelCase       				=	(image / 2 + 0.5).clamp(0      ,					1  )
						__lowerCAmelCase       				=	image.cpu().permute(0      ,					2      ,					3      ,					1  ).numpy()
						if output_type == "pil":
							__lowerCAmelCase       				=	self.numpy_to_pil(UpperCamelCase  )

						if not return_dict:
							return (image,), "This is a local test"

						return ImagePipelineOutput(images=UpperCamelCase  ), "This is a local test"




'''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"  )