


"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class _lowerCAmelCase				(      _snake_case	):
   __UpperCAmelCase :       UNetaDModel
   __UpperCAmelCase :       ScoreSdeVeScheduler







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		int:

       '''simple docstring'''




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







   @torch.no_grad()
   def __call__(	self						,	UpperCamelCase__ = 1						,	UpperCamelCase__ = 2000						,	UpperCamelCase__ = None						,	UpperCamelCase__ = "pil"						,	UpperCamelCase__ = True						,	**UpperCamelCase__						,	)	->		Union[ImagePipelineOutput, Tuple]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.unet.config.sample_size
       snake_case    :      Optional[Any]      					=    (batch_size, 3, img_size, img_size)

       snake_case    :      Dict      					=    self.unet

       snake_case    :      Optional[Any]      					=    randn_tensor(_UpperCamelCase						,	generator=_UpperCamelCase						) * self.scheduler.init_noise_sigma
       snake_case    :      List[Any]      					=    sample.to(self.device						)

       self.scheduler.set_timesteps(_UpperCamelCase						)
       self.scheduler.set_sigmas(_UpperCamelCase						)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
           snake_case    :      str      					=    self.scheduler.sigmas[i] * torch.ones(shape[0]						,	device=self.device						)

           # correction step
           for _ in range(self.scheduler.config.correct_steps						):
               snake_case    :      str      					=    self.unet(_UpperCamelCase						,	_UpperCamelCase						).sample
               snake_case    :      Tuple      					=    self.scheduler.step_correct(_UpperCamelCase						,	_UpperCamelCase						,	generator=_UpperCamelCase						).prev_sample

           # prediction step
           snake_case    :      str      					=    model(_UpperCamelCase						,	_UpperCamelCase						).sample
           snake_case    :      List[Any]      					=    self.scheduler.step_pred(_UpperCamelCase						,	_UpperCamelCase						,	_UpperCamelCase						,	generator=_UpperCamelCase						)

           snake_case    :      Any      					=    output.prev_sample, output.prev_sample_mean

       snake_case    :      Any      					=    sample_mean.clamp(0						,	1						)
       snake_case    :      Optional[Any]      					=    sample.cpu().permute(0						,	2						,	3						,	1						).numpy()
       if output_type == "pil":
           snake_case    :      Tuple      					=    self.numpy_to_pil(_UpperCamelCase						)

       if not return_dict:
           return (sample,)

       return ImagePipelineOutput(images=_UpperCamelCase						)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






