



from typing import List, Optional, Tuple, Union

import torch

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






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		UNetaDModel
							__lowerCamelCase		:		PNDMScheduler





							def __init__(				self						:						int ,			__lowercase						:						UNetaDModel ,			__lowercase						:						PNDMScheduler		):

											'''simple docstring'''
											super().__init__()

											__a					       =						PNDMScheduler.from_config(scheduler.config		)

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







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

											'''simple docstring'''
											__a					       =						randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=__lowercase ,			device=self.device ,			)

											self.scheduler.set_timesteps(__lowercase		)
											for t in self.progress_bar(self.scheduler.timesteps		):
															__a					       =						self.unet(__lowercase ,			__lowercase		).sample

															__a					       =						self.scheduler.step(__lowercase ,			__lowercase ,			__lowercase		).prev_sample

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

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=__lowercase		)





from typing import TYPE_CHECKING

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


lowerCamelCase__     =	{"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """HubertForCTC""",
						    """HubertForSequenceClassification""",
						    """HubertModel""",
						    """HubertPreTrainedModel""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFHubertForCTC""",
						    """TFHubertModel""",
						    """TFHubertPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_hubert import (
												    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    HubertForCTC,
												    HubertForSequenceClassification,
												    HubertModel,
												    HubertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_hubert import (
												    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFHubertForCTC,
												    TFHubertModel,
												    TFHubertPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
