"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Any			):
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCAmelCase__             =							DDIMScheduler.from_config(scheduler.config			)

									self.register_modules(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)







					@torch.no_grad()
					def __call__(				self   :	Optional[Any]    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,lowerCamelCase__   :	float = 0.0    ,lowerCamelCase__   :	int = 50    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	Optional[str] = "pil"    ,lowerCamelCase__   :	bool = True    ,):

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size    ,lowerCamelCase__			):
													UpperCAmelCase__             =							(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													UpperCAmelCase__             =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(lowerCamelCase__    ,lowerCamelCase__			) and len(lowerCamelCase__			) != batch_size:
													raise ValueError(
													    f'''You have passed a list of generators of length {len(lowerCamelCase__			)}, but requested an effective batch'''
													    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

									UpperCAmelCase__             =							randn_tensor(lowerCamelCase__    ,generator=lowerCamelCase__    ,device=self.device    ,dtype=self.unet.dtype			)

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

									for t in self.progress_bar(self.scheduler.timesteps			):
													# 1. predict noise model_output
													UpperCAmelCase__             =							self.unet(lowerCamelCase__    ,lowerCamelCase__			).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(
													    lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,eta=lowerCamelCase__    ,use_clipped_model_output=lowerCamelCase__    ,generator=lowerCamelCase__			).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(lowerCamelCase__			)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__			)




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  str						     =	{
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  Any						     =	[
						    'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MCTCTForCTC',
						    'MCTCTModel',
						    'MCTCTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						lowerCAmelCase__  :  Any						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



