






"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												super().__init__()

												# make sure scheduler can always be converted to DDIM
												lowercase__    :				Any=	DDIMScheduler.from_config(scheduler.config      )

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




						@torch.no_grad()
						def __call__(       self	,  snake_case__ = 1	,  snake_case__ = None	,  snake_case__ = 0.0	,  snake_case__ = 50	,  snake_case__ = None	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''

												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size	,  snake_case__      ):
																		lowercase__    :				Dict=	(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		lowercase__    :				Optional[int]=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(snake_case__	,  snake_case__      ) and len(snake_case__      ) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(snake_case__      )}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

												lowercase__    :				str=	randn_tensor(snake_case__	,  generator=snake_case__	,  device=self.device	,  dtype=self.unet.dtype      )

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

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		# 1. predict noise model_output
																		lowercase__    :				Tuple=	self.unet(snake_case__	,  snake_case__      ).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
																		lowercase__    :				int=	self.scheduler.step(
																		    snake_case__	,  snake_case__	,  snake_case__	,  eta=snake_case__	,  use_clipped_model_output=snake_case__	,  generator=snake_case__      ).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=snake_case__      )














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






