

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase  )      ->				Any:
									super().__init__()
									self.register_modules(unet=_lowercase       ,     scheduler=_lowercase  )





						@torch.no_grad()
						def __call__(  self       ,     _lowercase = 1       ,     _lowercase = None       ,     _lowercase = 1_000       ,     _lowercase = "pil"       ,     _lowercase = True       ,     )      ->				Union[ImagePipelineOutput, Tuple]:
									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size       ,     _lowercase  ):
												a_		:		int				    =				(
												    batch_size,
												    self.unet.config.in_channels,
												    self.unet.config.sample_size,
												    self.unet.config.sample_size,
												)
									else:
												a_		:		List[Any]				    =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if self.device.type == "mps":
												# randn does not work reproducibly on mps
												a_		:		Optional[int]				    =				randn_tensor(_lowercase       ,     generator=_lowercase  )
												a_		:		Union[str, Any]				    =				image.to(self.device  )
									else:
												a_		:		Dict				    =				randn_tensor(_lowercase       ,     generator=_lowercase       ,     device=self.device  )

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

									for t in self.progress_bar(self.scheduler.timesteps  ):
												# 1. predict noise model_output
												a_		:		int				    =				self.unet(_lowercase       ,     _lowercase  ).sample

												# 2. compute previous image: x_t -> x_t-1
												a_		:		Any				    =				self.scheduler.step(_lowercase       ,     _lowercase       ,     _lowercase       ,     generator=_lowercase  ).prev_sample

									a_		:		Optional[Any]				    =				(image / 2 + 0.5).clamp(0       ,     1  )
									a_		:		Union[str, Any]				    =				image.cpu().permute(0       ,     2       ,     3       ,     1  ).numpy()
									if output_type == "pil":
												a_		:		Optional[Any]				    =				self.numpy_to_pil(_lowercase  )

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=_lowercase  )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


