

'''simple docstring'''

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   UpperCamelCase__   (       _lowerCamelCase       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							42
		SCREAMING_SNAKE_CASE__  :     List[Any]       =							42







		def __init__(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							super().__init__()

							UpperCAmelCase		:							List[str]        =       PNDMScheduler.from_config(scheduler.config      )

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







		@torch.no_grad()
		def __call__(			self   ,      snake_case = 1   ,      snake_case = 5_0   ,      snake_case = None   ,      snake_case = "pil"   ,      snake_case = True   ,      **snake_case   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,      generator=__UpperCamelCase   ,      device=self.device   ,      )

							self.scheduler.set_timesteps(__UpperCamelCase      )
							for t in self.progress_bar(self.scheduler.timesteps      ):
												UpperCAmelCase		:							Optional[int]        =       self.unet(__UpperCamelCase   ,      __UpperCamelCase      ).sample

												UpperCAmelCase		:							Optional[Any]        =       self.scheduler.step(__UpperCamelCase   ,      __UpperCamelCase   ,      __UpperCamelCase      ).prev_sample

							UpperCAmelCase		:							Union[str, Any]        =       (image / 2 + 0.5).clamp(0   ,      1      )
							UpperCAmelCase		:							int        =       image.cpu().permute(0   ,      2   ,      3   ,      1      ).numpy()
							if output_type == "pil":
												UpperCAmelCase		:							str        =       self.numpy_to_pil(__UpperCamelCase      )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase      )



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
