





'''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					(						__UpperCAmelCase     ,							__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    1


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





											'''simple docstring'''





											self.set_timesteps(__A       )

											# standard deviation of the initial noise distribution
											_snake_case:   List[str]  					=		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.
											_snake_case:   Any  					=		4

											# running values
											_snake_case:   List[str]  					=		[]


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  int       ,     __snake_case	:  Union[str, torch.device] = None       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		num_inference_steps
											_snake_case:   Optional[int]  					=		torch.linspace(1       ,     0       ,     num_inference_steps + 1       )[:-1]
											_snake_case:   Any  					=		torch.cat([steps, torch.tensor([0.0]       )]       )

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

											_snake_case:   List[Any]  					=		(1.0 - self.betas**2) ** 0.5

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

											_snake_case:   Union[str, Any]  					=		[]


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  int       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  bool = True       ,     ):





											'''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'       )

											_snake_case:   Any  					=		(self.timesteps == timestep).nonzero().item()
											_snake_case:   Optional[Any]  					=		timestep_index + 1

											_snake_case:   int  					=		sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
											self.ets.append(__A       )

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

											_snake_case:   Optional[Any]  					=		self._get_prev_sample(__A       ,     __A       ,     __A       ,     __A       )

											if not return_dict:
																	return (prev_sample,)

											return SchedulerOutput(prev_sample=__A       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  torch.FloatTensor       ,     *__snake_case	:  Tuple       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return sample


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Any       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.alphas[timestep_index]
											_snake_case:   List[str]  					=		self.betas[timestep_index]

											_snake_case:   Optional[int]  					=		self.alphas[prev_timestep_index]
											_snake_case:   Union[str, Any]  					=		self.betas[prev_timestep_index]

											_snake_case:   Union[str, Any]  					=		(sample - sigma * ets) / max(__A       ,     1e-8       )
											_snake_case:   Optional[Any]  					=		next_alpha * pred + ets * next_sigma

											return prev_sample




					def __len__(					self	:  List[str]       ):





											'''simple docstring'''





											return self.config.num_train_timesteps






'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		A   : int             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





