


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    Optional[Any]	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Optional[Any]					):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_	,							scheduler=UpperCAmelCase_					)






				@torch.no_grad()
				def __call__(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Union[str, Any] = 1	,							lowerCamelCase_     :    int = None	,							lowerCamelCase_     :    str = 50	,							lowerCamelCase_     :    int = "pil"	,							lowerCamelCase_     :    Any = True	,							**lowerCamelCase_     :    List[Any]	,							):
								_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"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__			:						str							=			{
    """configuration_rwkv""": ["""RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RwkvConfig""", """RwkvOnnxConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[int]							=			[
							    """RWKV_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RwkvForCausalLM""",
							    """RwkvModel""",
							    """RwkvPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

							a__			:						List[str]							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


