

'''simple docstring'''



import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput



class lowerCamelCase			(  lowercase_	,						lowercase_						):



			'''simple docstring'''






			__snake_case        =	1


			@register_to_config
			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int = 10_00		,      lowerCAmelCase_	:						Optional[Union[np.ndarray, List[float]]] = None			)      ->		Optional[int]:





									'''simple docstring'''
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(lowerCAmelCase_			)

									# standard deviation of the initial noise distribution
									A__					:       int     =1.0

									# For now we only support F-PNDM, i.e. the runge-kutta method
									# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
									# mainly at formula (9), (12), (13) and the Algorithm 2.
									A__					:       Optional[int]     =4

									# running values
									A__					:       Optional[Any]     =[]


			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Union[str, torch.device] = None			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Tuple     =num_inference_steps
									A__					:       int     =torch.linspace(1		,      0		,      num_inference_steps + 1			)[:-1]
									A__					:       Dict     =torch.cat([steps, torch.tensor([0.0]			)]			)

									if self.config.trained_betas is not None:
															A__					:       Optional[int]     =torch.tensor(self.config.trained_betas		,      dtype=torch.floataa			)
									else:
															A__					:       Tuple     =torch.sin(steps * math.pi / 2			) ** 2

									A__					:       Tuple     =(1.0 - self.betas**2) ** 0.5

									A__					:       Any     =(torch.atana(self.betas		,      self.alphas			) / math.pi * 2)[:-1]
									A__					:       Optional[int]     =timesteps.to(lowerCAmelCase_			)

									A__					:       str     =[]


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						torch.FloatTensor		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						torch.FloatTensor		,      lowerCAmelCase_	:						bool = True		,      )      ->		Union[SchedulerOutput, Tuple]:





									'''simple docstring'''
									if self.num_inference_steps is None:
															raise ValueError(
															    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""			)

									A__					:       str     =(self.timesteps == timestep).nonzero().item()
									A__					:       Tuple     =timestep_index + 1

									A__					:       Optional[Any]     =sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(lowerCAmelCase_			)

									if len(self.ets			) == 1:
															A__					:       Union[str, Any]     =self.ets[-1]
									elif len(self.ets			) == 2:
															A__					:       Any     =(3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets			) == 3:
															A__					:       Optional[int]     =(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
															A__					:       Optional[Any]     =(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									A__					:       Dict     =self._get_prev_sample(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCAmelCase_			)


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						torch.FloatTensor		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						int			)      ->		torch.FloatTensor:





									'''simple docstring'''
									return sample


			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						List[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[Any]     =self.alphas[timestep_index]
									A__					:       Dict     =self.betas[timestep_index]

									A__					:       List[str]     =self.alphas[prev_timestep_index]
									A__					:       Optional[int]     =self.betas[prev_timestep_index]

									A__					:       Any     =(sample - sigma * ets) / max(lowerCAmelCase_		,      1e-8			)
									A__					:       List[Any]     =next_alpha * pred + ets * next_sigma

									return prev_sample



			def __len__(				self	:						Tuple			)      ->		Union[str, Any]:





									'''simple docstring'''
									return self.config.num_train_timesteps




'''simple docstring'''



from typing import TYPE_CHECKING

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


__snake_case			:     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:
		__snake_case			:     Optional[int]										=      [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     List[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

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

