





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					def __init__(    self     :	Tuple			,       __lowercase     :	Any			,       __lowercase     :	Any		):






						'''simple docstring'''







						super().__init__()
						self.register_modules(unet=__lowercase			,       scheduler=__lowercase		)





					@torch.no_grad()
					def __call__(    self     :	int			,       __lowercase     :	int = 1			,       __lowercase     :	Optional[torch.Generator] = None			,       __lowercase     :	int = 50			,       __lowercase     :	Optional[str] = "pil"			,       __lowercase     :	bool = True			,       **__lowercase     :	List[str]			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,       generator=__lowercase			,       )
						UpperCAmelCase_		    =  image.to(self.device		)

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

						for t in self.progress_bar(self.scheduler.timesteps		):
							# 1. predict noise model_output
							UpperCAmelCase_		    =  self.unet(__lowercase			,       __lowercase		).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_		    =  self.scheduler.step(__lowercase			,       __lowercase			,       __lowercase		).prev_sample

						UpperCAmelCase_		    =  (image / 2 + 0.5).clamp(0			,       1		)
						UpperCAmelCase_		    =  image.cpu().permute(0			,       2			,       3			,       1		).numpy()
						if output_type == "pil":
							UpperCAmelCase_		    =  self.numpy_to_pil(__lowercase		)

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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

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


