'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class 						A   (   UpperCAmelCase   ):
							a_        =      42
							a_        =      42






class 						A   (   UpperCAmelCase       ,	UpperCAmelCase   ):
							a_        =      1





							@register_to_config
							def __init__(      self				:							List[str]     ,		__a				:							int = 2_0_0_0     ,		__a				:							float = 0.1_5     ,		__a				:							float = 0.0_1     ,		__a				:							float = 1_3_4_8.0     ,		__a				:							float = 1e-5     ,		__a				:							int = 1     ,		)							->	Tuple:
								# standard deviation of the initial noise distribution
								__UpperCAmelCase							=			sigma_max

								# setable values
								__UpperCAmelCase							=			None

								self.set_sigmas(__a     ,		__a     ,		__a     ,		__a	)





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							torch.FloatTensor     ,		__a				:							Optional[int] = None	)							->	torch.FloatTensor:
								return sample





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int     ,		__a				:							float = None     ,		__a				:							Union[str, torch.device] = None	)							->	Tuple:
								__UpperCAmelCase							=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

								__UpperCAmelCase							=			torch.linspace(1     ,		__a     ,		__a     ,		device=__a	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							int     ,		__a				:							float = None     ,		__a				:							float = None     ,		__a				:							float = None	)							->	int:
								__UpperCAmelCase							=			sigma_min if sigma_min is not None else self.config.sigma_min
								__UpperCAmelCase							=			sigma_max if sigma_max is not None else self.config.sigma_max
								__UpperCAmelCase							=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
									self.set_timesteps(__a     ,		__a	)

								__UpperCAmelCase							=			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								__UpperCAmelCase							=			torch.exp(torch.linspace(math.log(__a	)     ,		math.log(__a	)     ,		__a	)	)
								__UpperCAmelCase							=			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]	)





							def       snake_case__						(      self				:							Any     ,		__a				:							Union[str, Any]     ,		__a				:							Dict	)							->	Any:
								return torch.where(
								    timesteps == 0     ,		torch.zeros_like(t.to(timesteps.device	)	)     ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device	)     ,		)





							def       snake_case__						(      self				:							int     ,		__a				:							torch.FloatTensor     ,		__a				:							int     ,		__a				:							torch.FloatTensor     ,		__a				:							Optional[torch.Generator] = None     ,		__a				:							bool = True     ,		)							->	Union[SdeVeOutput, Tuple]:
								if self.timesteps is None:
									raise ValueError(
									    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''	)

								__UpperCAmelCase							=			timestep * torch.ones(
								    sample.shape[0]     ,		device=sample.device	)  # torch.repeat_interleave(timestep, sample.shape[0])
								__UpperCAmelCase							=			(timestep * (len(self.timesteps	) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								__UpperCAmelCase							=			timesteps.to(self.discrete_sigmas.device	)

								__UpperCAmelCase							=			self.discrete_sigmas[timesteps].to(sample.device	)
								__UpperCAmelCase							=			self.get_adjacent_sigma(__a     ,		__a	).to(sample.device	)
								__UpperCAmelCase							=			torch.zeros_like(__a	)
								__UpperCAmelCase							=			(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								__UpperCAmelCase							=			diffusion.flatten()
								while len(diffusion.shape	) < len(sample.shape	):
									__UpperCAmelCase							=			diffusion.unsqueeze(-1	)
								__UpperCAmelCase							=			drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								__UpperCAmelCase							=			randn_tensor(
								    sample.shape     ,		layout=sample.layout     ,		generator=__a     ,		device=sample.device     ,		dtype=sample.dtype	)
								__UpperCAmelCase							=			sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								__UpperCAmelCase							=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
									return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=__a     ,		prev_sample_mean=__a	)





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							torch.FloatTensor     ,		__a				:							torch.FloatTensor     ,		__a				:							Optional[torch.Generator] = None     ,		__a				:							bool = True     ,		)							->	Union[SchedulerOutput, Tuple]:
								if self.timesteps is None:
									raise ValueError(
									    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''	)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								__UpperCAmelCase							=			randn_tensor(sample.shape     ,		layout=sample.layout     ,		generator=__a	).to(sample.device	)

								# compute step size from the model_output, the noise, and the snr
								__UpperCAmelCase							=			torch.norm(model_output.reshape(model_output.shape[0]     ,		-1	)     ,		dim=-1	).mean()
								__UpperCAmelCase							=			torch.norm(noise.reshape(noise.shape[0]     ,		-1	)     ,		dim=-1	).mean()
								__UpperCAmelCase							=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								__UpperCAmelCase							=			step_size * torch.ones(sample.shape[0]	).to(sample.device	)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								__UpperCAmelCase							=			step_size.flatten()
								while len(step_size.shape	) < len(sample.shape	):
									__UpperCAmelCase							=			step_size.unsqueeze(-1	)
								__UpperCAmelCase							=			sample + step_size * model_output
								__UpperCAmelCase							=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a	)





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							torch.FloatTensor     ,		__a				:							torch.FloatTensor     ,		__a				:							torch.FloatTensor     ,		)							->	torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__UpperCAmelCase							=			timesteps.to(original_samples.device	)
								__UpperCAmelCase							=			self.discrete_sigmas.to(original_samples.device	)[timesteps]
								__UpperCAmelCase							=			(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(__a	) * sigmas[:, None, None, None]
								)
								__UpperCAmelCase							=			noise + original_samples
								return noisy_samples






							def __len__(      self				:							Optional[int]	)							->	List[Any]:
								return self.config.num_train_timesteps

'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       List[Any]						       =	{
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	[
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
