






'''simple docstring'''





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Any							,				_A       :						List[Any]							,				_A       :						Tuple     )  ->    int:
								super().__init__()
								self.register_modules(unet=_A							,				scheduler=_A     )





					@torch.no_grad()
					def __call__(       self       :						Any							,				_A       :						int = 1							,				_A       :						Optional[torch.Generator] = None							,				_A       :						int = 50							,				_A       :						Optional[str] = "pil"							,				_A       :						bool = True							,				**_A       :						int							,				)  ->    Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase_     :   Union[str, Any]       =   torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,				generator=_A							,				)
								UpperCAmelCase_     :   List[str]       =   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
											UpperCAmelCase_     :   Any       =   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
											UpperCAmelCase_     :   Dict       =   self.scheduler.step(_A							,				_A							,				_A     ).prev_sample

								UpperCAmelCase_     :   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(_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 OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


