

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__snake_case							:     Optional[int] 						=	logging.get_logger(__name__)  # pylint: disable=invalid-name



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






						_A  :							torch.FloatTensor







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






						_A  :							int   			=	1





						@register_to_config
						def __init__(  self       ,     _lowercase = 40       ,     _lowercase = 0.0_0_2       ,     _lowercase = 80.0       ,     _lowercase = 0.5       ,     _lowercase = 1.0       ,     _lowercase = 7.0       ,     _lowercase = True       ,     )      ->				List[str]:
									# standard deviation of the initial noise distribution
									a_		:		Tuple				    =				sigma_max

									a_		:		Optional[Any]				    =				np.linspace(0       ,     1       ,     _lowercase  )
									a_		:		Tuple				    =				self._convert_to_karras(_lowercase  )
									a_		:		Dict				    =				self.sigma_to_t(_lowercase  )

									# setable values
									a_		:		Union[str, Any]				    =				None
									a_		:		Any				    =				torch.from_numpy(_lowercase  )
									a_		:		Dict				    =				torch.from_numpy(_lowercase  )
									a_		:		Optional[Any]				    =				False
									a_		:		Optional[Any]				    =				False





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=None  )      ->				Tuple:
									if schedule_timesteps is None:
												a_		:		List[Any]				    =				self.timesteps

									a_		:		List[str]				    =				(schedule_timesteps == timestep).nonzero()
									return indices.item()





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				torch.FloatTensor:
									# Get sigma corresponding to timestep
									if isinstance(_lowercase       ,     torch.Tensor  ):
												a_		:		List[Any]				    =				timestep.to(self.timesteps.device  )
									a_		:		Union[str, Any]				    =				self.index_for_timestep(_lowercase  )
									a_		:		List[Any]				    =				self.sigmas[step_idx]

									a_		:		int				    =				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									a_		:		Dict				    =				True
									return sample





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Any:
									if not isinstance(_lowercase       ,     np.ndarray  ):
												a_		:		List[str]				    =				np.array(_lowercase       ,     dtype=np.floataa  )

									a_		:		str				    =				1_000 * 0.2_5 * np.log(sigmas + 1e-44  )

									return timesteps





						def 	UpperCamelCase__			(  self       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     )      ->				List[str]:
									if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""  )

									if num_inference_steps is not None and timesteps is not None:
												raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""  )

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
												for i in range(1       ,     len(_lowercase  )  ):
															if timesteps[i] >= timesteps[i - 1]:
																		raise ValueError("""`timesteps` must be in descending order."""  )

												if timesteps[0] >= self.config.num_train_timesteps:
															raise ValueError(
															    F'''`timesteps` must start before `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps}.'''  )

												a_		:		Optional[Any]				    =				np.array(_lowercase       ,     dtype=np.intaa  )
												a_		:		Union[str, Any]				    =				True
									else:
												if num_inference_steps > self.config.num_train_timesteps:
															raise ValueError(
															    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
															    F''' maximal {self.config.num_train_timesteps} timesteps.'''  )

												a_		:		Optional[Any]				    =				num_inference_steps

												a_		:		List[str]				    =				self.config.num_train_timesteps // self.num_inference_steps
												a_		:		str				    =				(np.arange(0       ,     _lowercase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
												a_		:		Any				    =				False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									a_		:		Tuple				    =				self.config.num_train_timesteps
									a_		:		Union[str, Any]				    =				timesteps[::-1].copy()
									a_		:		Optional[Any]				    =				ramp / (num_train_timesteps - 1)
									a_		:		Optional[Any]				    =				self._convert_to_karras(_lowercase  )
									a_		:		Dict				    =				self.sigma_to_t(_lowercase  )

									a_		:		Optional[Any]				    =				np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
									a_		:		str				    =				torch.from_numpy(_lowercase  ).to(device=_lowercase  )

									if str(_lowercase  ).startswith("""mps"""  ):
												# mps does not support float64
												a_		:		Tuple				    =				torch.from_numpy(_lowercase  ).to(_lowercase       ,     dtype=torch.floataa  )
									else:
												a_		:		Union[str, Any]				    =				torch.from_numpy(_lowercase  ).to(device=_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Dict:
									a_		:		float				    =				self.config.sigma_min
									a_		:		float				    =				self.config.sigma_max

									a_		:		Union[str, Any]				    =				self.config.rho
									a_		:		List[str]				    =				sigma_min ** (1 / rho)
									a_		:		Union[str, Any]				    =				sigma_max ** (1 / rho)
									a_		:		Tuple				    =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		Union[str, Any]				    =				self.config.sigma_data

									a_		:		Dict				    =				sigma_data**2 / (sigma**2 + sigma_data**2)
									a_		:		Union[str, Any]				    =				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				str:
									a_		:		List[str]				    =				self.config.sigma_min
									a_		:		Union[str, Any]				    =				self.config.sigma_data

									a_		:		Any				    =				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									a_		:		Tuple				    =				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = True       ,     )      ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									if (
									    isinstance(_lowercase       ,     _lowercase  )
									    or isinstance(_lowercase       ,     torch.IntTensor  )
									    or isinstance(_lowercase       ,     torch.LongTensor  )
									):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
												        """ one of the `scheduler.timesteps` as a timestep."""
												    )       ,     )

									if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""  )

									if isinstance(_lowercase       ,     torch.Tensor  ):
												a_		:		str				    =				timestep.to(self.timesteps.device  )

									a_		:		Tuple				    =				self.config.sigma_min
									a_		:		List[Any]				    =				self.config.sigma_max

									a_		:		str				    =				self.index_for_timestep(_lowercase  )

									# sigma_next corresponds to next_t in original implementation
									a_		:		Dict				    =				self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
												a_		:		List[Any]				    =				self.sigmas[step_index + 1]
									else:
												# Set sigma_next to sigma_min
												a_		:		Optional[Any]				    =				self.sigmas[-1]

									# Get scalings for boundary conditions
									a_		:		str				    =				self.get_scalings_for_boundary_condition(_lowercase  )

									# 1. Denoise model output using boundary conditions
									a_		:		Optional[Any]				    =				c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												a_		:		Optional[int]				    =				denoised.clamp(-1       ,     1  )

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps  ) > 1:
												a_		:		Union[str, Any]				    =				randn_tensor(
												    model_output.shape       ,     dtype=model_output.dtype       ,     device=model_output.device       ,     generator=_lowercase  )
									else:
												a_		:		List[Any]				    =				torch.zeros_like(_lowercase  )
									a_		:		Any				    =				noise * self.config.s_noise

									a_		:		Any				    =				sigma_next.clamp(min=_lowercase       ,     max=_lowercase  )

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									a_		:		str				    =				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									a_		:		List[Any]				    =				self.sigmas.to(device=original_samples.device       ,     dtype=original_samples.dtype  )
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase  ):
												# mps does not support float64
												a_		:		Dict				    =				self.timesteps.to(original_samples.device       ,     dtype=torch.floataa  )
												a_		:		List[Any]				    =				timesteps.to(original_samples.device       ,     dtype=torch.floataa  )
									else:
												a_		:		int				    =				self.timesteps.to(original_samples.device  )
												a_		:		Any				    =				timesteps.to(original_samples.device  )

									a_		:		Optional[int]				    =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									a_		:		Optional[int]				    =				sigmas[step_indices].flatten()
									while len(sigma.shape  ) < len(original_samples.shape  ):
												a_		:		Tuple				    =				sigma.unsqueeze(-1  )

									a_		:		Dict				    =				original_samples + noise * sigma
									return noisy_samples





						def __len__(  self  )      ->				Union[str, Any]:
									return self.config.num_train_timesteps





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


