

'''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 						__lowercase       (				unittest.TestCase	, UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Dict							= load_tool("image-captioning")
									self.tool.setup()
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= load_tool("image-captioning"     ,		remote=__UpperCamelCase)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				List[str]							= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.tool(__UpperCamelCase)
									self.assertEqual(__UpperCamelCase     ,		"two cats sleeping on a couch")



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				List[str]							= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.remote_tool(__UpperCamelCase)
									self.assertEqual(__UpperCamelCase     ,		"two cats sleeping on a couch")



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				Any							= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
									SCREAMING_SNAKE_CASE_:				Any							= self.tool(image=__UpperCamelCase)
									self.assertEqual(__UpperCamelCase     ,		"two cats sleeping on a couch")




			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
									SCREAMING_SNAKE_CASE_:				str							= self.remote_tool(image=__UpperCamelCase)
									self.assertEqual(__UpperCamelCase     ,		"two cats sleeping on a couch")









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

									self.register_modules(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






