
"""simple docstring"""




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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      UpperCamelCase	(       A_      ):

				"""simple docstring"""



				UpperCAmelCase_				:      torch.FloatTensor
				UpperCAmelCase_				:      Optional[torch.FloatTensor]						  =				None






def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=0.999,     SCREAMING_SNAKE_CASE_="cosine",     ):

					if alpha_transform_type == "cosine":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_			):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_			):
															return math.exp(t * -12.0			)

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

					SCREAMING_SNAKE_CASE				=	[]
					for i in range(A__			):
										SCREAMING_SNAKE_CASE				=	i / num_diffusion_timesteps
										SCREAMING_SNAKE_CASE				=	(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A__			) / alpha_bar_fn(A__			),     A__			)			)
					return torch.tensor(A__,     dtype=torch.floataa			)




class      UpperCamelCase	(       A_  ,      A_      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Union[str, Any]						  =				[e.name for e in KarrasDiffusionSchedulers]
				UpperCAmelCase_				:      List[Any]						  =				1


				@register_to_config
				def __init__(				self		,	lowercase__ = 1000		,	lowercase__ = 0.0_001		,	lowercase__ = 0.02		,	lowercase__ = "linear"		,	lowercase__ = None		,	lowercase__ = False		,	lowercase__ = "epsilon"		,	lowercase__ = "linspace"		,	lowercase__ = 0		,	)    ->    Any:
									"""simple docstring"""







									if trained_betas is not None:
														SCREAMING_SNAKE_CASE				=	torch.tensor(_A		,	dtype=torch.floataa			)
									elif beta_schedule == "linear":
														SCREAMING_SNAKE_CASE				=	torch.linspace(_A		,	_A		,	_A		,	dtype=torch.floataa			)
									elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														SCREAMING_SNAKE_CASE				=	(
														    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	_A		,	dtype=torch.floataa			) ** 2
														)
									elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														SCREAMING_SNAKE_CASE				=	betas_for_alpha_bar(_A			)
									else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

									SCREAMING_SNAKE_CASE				=	1.0 - self.betas
									SCREAMING_SNAKE_CASE				=	torch.cumprod(self.alphas		,	dim=0			)

									SCREAMING_SNAKE_CASE				=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
									SCREAMING_SNAKE_CASE				=	np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
									SCREAMING_SNAKE_CASE				=	torch.from_numpy(_A			)

									# setable values
									SCREAMING_SNAKE_CASE				=	None
									SCREAMING_SNAKE_CASE				=	use_karras_sigmas
									self.set_timesteps(_A		,	_A			)
									SCREAMING_SNAKE_CASE				=	[]
									SCREAMING_SNAKE_CASE				=	False


				@property
				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







									if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    torch.FloatTensor:
									"""simple docstring"""







									if isinstance(_A		,	torch.Tensor			):
														SCREAMING_SNAKE_CASE				=	timestep.to(self.timesteps.device			)
									SCREAMING_SNAKE_CASE				=	(self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE				=	self.sigmas[step_index]
									SCREAMING_SNAKE_CASE				=	sample / ((sigma**2 + 1) ** 0.5)
									SCREAMING_SNAKE_CASE				=	True
									return sample


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    str:
									"""simple docstring"""







									def lms_derivative(lowercase__			):
														SCREAMING_SNAKE_CASE				=	1.0
														for k in range(_A			):
																			if current_order == k:
																								continue
																			prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
														return prod

									SCREAMING_SNAKE_CASE				=	integrate.quad(_A		,	self.sigmas[t]		,	self.sigmas[t + 1]		,	epsrel=1E-4			)[0]

									return integrated_coeff


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
														SCREAMING_SNAKE_CASE				=	np.linspace(0		,	self.config.num_train_timesteps - 1		,	_A		,	dtype=_A			)[
														    ::-1
														].copy()
									elif self.config.timestep_spacing == "leading":
														SCREAMING_SNAKE_CASE				=	self.config.num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														SCREAMING_SNAKE_CASE				=	(np.arange(0		,	_A			) * step_ratio).round()[::-1].copy().astype(_A			)
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														SCREAMING_SNAKE_CASE				=	self.config.num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														SCREAMING_SNAKE_CASE				=	(np.arange(self.config.num_train_timesteps		,	0		,	-step_ratio			)).round().copy().astype(_A			)
														timesteps -= 1
									else:
														raise ValueError(
														    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

									SCREAMING_SNAKE_CASE				=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
									SCREAMING_SNAKE_CASE				=	np.log(_A			)
									SCREAMING_SNAKE_CASE				=	np.interp(_A		,	np.arange(0		,	len(_A			)			)		,	_A			)

									if self.use_karras_sigmas:
														SCREAMING_SNAKE_CASE				=	self._convert_to_karras(in_sigmas=_A			)
														SCREAMING_SNAKE_CASE				=	np.array([self._sigma_to_t(_A		,	_A			) for sigma in sigmas]			)

									SCREAMING_SNAKE_CASE				=	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)

									SCREAMING_SNAKE_CASE				=	torch.from_numpy(_A			).to(device=_A			)
									if str(_A			).startswith('mps'			):
														# mps does not support float64
														SCREAMING_SNAKE_CASE				=	torch.from_numpy(_A			).to(_A		,	dtype=torch.floataa			)
									else:
														SCREAMING_SNAKE_CASE				=	torch.from_numpy(_A			).to(device=_A			)

									SCREAMING_SNAKE_CASE				=	[]


				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	np.log(_A			)

									# get distribution
									SCREAMING_SNAKE_CASE				=	log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									SCREAMING_SNAKE_CASE				=	np.cumsum((dists >= 0)		,	axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
									SCREAMING_SNAKE_CASE				=	low_idx + 1

									SCREAMING_SNAKE_CASE				=	log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE				=	log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE				=	(low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE				=	np.clip(_A		,	0		,	1			)

									# transform interpolation to time range
									SCREAMING_SNAKE_CASE				=	(1 - w) * low_idx + w * high_idx
									SCREAMING_SNAKE_CASE				=	t.reshape(sigma.shape			)
									return t


				def 							A					(				self		,	lowercase__			)    ->    torch.FloatTensor:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	in_sigmas[-1].item()
									SCREAMING_SNAKE_CASE				=	in_sigmas[0].item()

									SCREAMING_SNAKE_CASE				=	7.0  # 7.0 is the value used in the paper
									SCREAMING_SNAKE_CASE				=	np.linspace(0		,	1		,	self.num_inference_steps			)
									SCREAMING_SNAKE_CASE				=	sigma_min ** (1 / rho)
									SCREAMING_SNAKE_CASE				=	sigma_max ** (1 / rho)
									SCREAMING_SNAKE_CASE				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ = 4		,	lowercase__ = True		,	)    ->    Union[LMSDiscreteSchedulerOutput, Tuple]:
									"""simple docstring"""







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

									if isinstance(_A		,	torch.Tensor			):
														SCREAMING_SNAKE_CASE				=	timestep.to(self.timesteps.device			)
									SCREAMING_SNAKE_CASE				=	(self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE				=	self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
														SCREAMING_SNAKE_CASE				=	sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
														# * c_out + input * c_skip
														SCREAMING_SNAKE_CASE				=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
														SCREAMING_SNAKE_CASE				=	model_output
									else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

									# 2. Convert to an ODE derivative
									SCREAMING_SNAKE_CASE				=	(sample - pred_original_sample) / sigma
									self.derivatives.append(_A			)
									if len(self.derivatives			) > order:
														self.derivatives.pop(0			)

									# 3. Compute linear multistep coefficients
									SCREAMING_SNAKE_CASE				=	min(step_index + 1		,	_A			)
									SCREAMING_SNAKE_CASE				=	[self.get_lms_coefficient(_A		,	_A		,	_A			) for curr_order in range(_A			)]

									# 4. Compute previous sample based on the derivatives path
									SCREAMING_SNAKE_CASE				=	sample + sum(
									    coeff * derivative for coeff, derivative in zip(_A		,	reversed(self.derivatives			)			)			)

									if not return_dict:
														return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=_A		,	pred_original_sample=_A			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	)    ->    torch.FloatTensor:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.sigmas.to(device=original_samples.device		,	dtype=original_samples.dtype			)
									if original_samples.device.type == "mps" and torch.is_floating_point(_A			):
														# mps does not support float64
														SCREAMING_SNAKE_CASE				=	self.timesteps.to(original_samples.device		,	dtype=torch.floataa			)
														SCREAMING_SNAKE_CASE				=	timesteps.to(original_samples.device		,	dtype=torch.floataa			)
									else:
														SCREAMING_SNAKE_CASE				=	self.timesteps.to(original_samples.device			)
														SCREAMING_SNAKE_CASE				=	timesteps.to(original_samples.device			)

									SCREAMING_SNAKE_CASE				=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									SCREAMING_SNAKE_CASE				=	sigmas[step_indices].flatten()
									while len(sigma.shape			) < len(original_samples.shape			):
														SCREAMING_SNAKE_CASE				=	sigma.unsqueeze(-1			)

									SCREAMING_SNAKE_CASE				=	original_samples + noise * sigma
									return noisy_samples




				def __len__(				self			)    ->    Optional[int]:
									"""simple docstring"""







									return self.config.num_train_timesteps


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





     def __call__(self      :    Optional[Any]				,    _A      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    _A      :    Union[bool, str, PaddingStrategy] = False				,    _A      :    Optional[int] = None				,    _A      :    bool = False				,    _A      :    Optional[int] = None				,    _A      :    Optional[bool] = None				,    _A      :    Optional[Union[str, TensorType]] = None				,    _A      :    Optional[int] = None				,    **_A      :    List[Any]				,    )						->						BatchFeature:

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     f' {self.sampling_rate} and not {sampling_rate}.'   )
         else:
             logger.warning(
                 "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."   )

         snake_case      =					isinstance(_A				,    np.ndarray   ) and len(raw_speech.shape   ) > 1
         if is_batched_numpy and len(raw_speech.shape   ) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

         # always return batch
         if not is_batched:
             snake_case      =					[raw_speech]

         # convert into correct format for padding
         snake_case      =					BatchFeature({"input_values": raw_speech}   )

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

         # convert input values to correct format
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[np.asarray(_A				,    dtype=np.floataa   ) for array in input_values]
         elif (
             not isinstance(_A				,    np.ndarray   )
             and isinstance(input_values[0]				,    np.ndarray   )
             and input_values[0].dtype is np.dtype(np.floataa   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

         # convert attention_mask to correct format
         snake_case      =					padded_inputs.get("attention_mask"   )
         if attention_mask is not None:
             snake_case      =					[np.asarray(_A				,    dtype=np.intaa   ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case      =					(
                 attention_mask
                 if self._get_padding_strategies(_A				,    max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case      =					self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]				,    attention_mask=_A				,    padding_value=self.padding_value   )

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

