






"""simple docstring"""



from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  str ):
										super().__init__()
										self.register_modules(unet=_A       ,	scheduler=_A )


			@torch.no_grad()
			def __call__(				self					:  Tuple       ,	snake_case__					:  int = 1       ,	snake_case__					:  Optional[torch.Generator] = None       ,	snake_case__					:  int = 50       ,	snake_case__					:  Optional[str] = "pil"       ,	snake_case__					:  bool = True       ,	**snake_case__					:  Tuple       ,	):
										lowerCamelCase_	:							int									=torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,	generator=_A       ,	)
										lowerCamelCase_	:							Any									=image.to(self.device )

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

										for t in self.progress_bar(self.scheduler.timesteps ):
																	# 1. predict noise model_output
																	lowerCamelCase_	:							Tuple									=self.unet(_A       ,	_A ).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_	:							List[Any]									=self.scheduler.step(_A       ,	_A       ,	_A ).prev_sample

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

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

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


A__	:    List[str]			 = {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

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



