



"""simple docstring"""






import math
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 KarrasDiffusionSchedulers, SchedulerMixin


_lowercase					:					List[Any] 		=		logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			__SCREAMING_SNAKE_CASE      ( lowercase_  ):


					'''simple docstring'''





					_a       =				4_2
					_a       =				None






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       List[Any]=0.9_99				,						__lowerCamelCase							:       Union[str, Any]="cosine"				,						):
			"""simple docstring"""
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__lowerCamelCase							:       int      ):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

			elif alpha_transform_type == "exp":

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

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

			lowerCamelCase__						:						List[str]						      =[]
			for i in range(__lowerCamelCase      ):
						lowerCamelCase__						:						Any						      =i / num_diffusion_timesteps
						lowerCamelCase__						:						Any						      =(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__lowerCamelCase      ) / alpha_bar_fn(__lowerCamelCase      )				,						__lowerCamelCase      )      )
			return torch.tensor(__lowerCamelCase				,						dtype=torch.floataa      )

class 			__SCREAMING_SNAKE_CASE      ( lowercase_						,  lowercase_  ):


					'''simple docstring'''





					_a       =				[e.name for e in KarrasDiffusionSchedulers]
					_a       =				1



					@register_to_config
					def __init__(			self				:    int,    lowerCamelCase				:    int = 1000,    lowerCamelCase				:    float = 0.0_001,    lowerCamelCase				:    float = 0.02,    lowerCamelCase				:    str = "linear",    lowerCamelCase				:    Optional[Union[np.ndarray, List[float]]] = None,    lowerCamelCase				:    str = "epsilon",    lowerCamelCase				:    str = "linspace",    lowerCamelCase				:    int = 0,    )->   int:
								if trained_betas is not None:
											lowerCamelCase__						:						List[str]						      =torch.tensor(__UpperCamelCase,    dtype=torch.floataa	)
								elif beta_schedule == "linear":
											lowerCamelCase__						:						Dict						      =torch.linspace(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    dtype=torch.floataa	)
								elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											lowerCamelCase__						:						int						      =(
											    torch.linspace(beta_start**0.5,    beta_end**0.5,    __UpperCamelCase,    dtype=torch.floataa	) ** 2
											)
								elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											lowerCamelCase__						:						int						      =betas_for_alpha_bar(__UpperCamelCase	)
								else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

								lowerCamelCase__						:						Union[str, Any]						      =1.0 - self.betas
								lowerCamelCase__						:						Union[str, Any]						      =torch.cumprod(self.alphas,    dim=0	)

								lowerCamelCase__						:						str						      =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
								lowerCamelCase__						:						List[Any]						      =np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
								lowerCamelCase__						:						Union[str, Any]						      =torch.from_numpy(__UpperCamelCase	)

								# setable values
								lowerCamelCase__						:						Tuple						      =None
								lowerCamelCase__						:						str						      =np.linspace(0,    num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase	)[::-1].copy()
								lowerCamelCase__						:						Optional[int]						      =torch.from_numpy(__UpperCamelCase	)
								lowerCamelCase__						:						List[Any]						      =False



					@property
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

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



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    torch.FloatTensor,    lowerCamelCase				:    Union[float, torch.FloatTensor]	)->   Union[str, Any]:
								if isinstance(__UpperCamelCase,    torch.Tensor	):
											lowerCamelCase__						:						Optional[Any]						      =timestep.to(self.timesteps.device	)
								lowerCamelCase__						:						Tuple						      =(self.timesteps == timestep).nonzero().item()
								lowerCamelCase__						:						Optional[Any]						      =self.sigmas[step_index]
								lowerCamelCase__						:						Optional[Any]						      =sample / ((sigma**2 + 1) ** 0.5)
								lowerCamelCase__						:						Optional[Any]						      =True
								return sample



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    int,    lowerCamelCase				:    Union[str, torch.device] = None	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =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":
											lowerCamelCase__						:						Tuple						      =np.linspace(0,    self.config.num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase	)[
											    ::-1
											].copy()
								elif self.config.timestep_spacing == "leading":
											lowerCamelCase__						:						int						      =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
											lowerCamelCase__						:						Tuple						      =(np.arange(0,    __UpperCamelCase	) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase	)
											timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
											lowerCamelCase__						:						Tuple						      =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
											lowerCamelCase__						:						List[Any]						      =(np.arange(self.config.num_train_timesteps,    0,    -step_ratio	)).round().copy().astype(__UpperCamelCase	)
											timesteps -= 1
								else:
											raise ValueError(
											    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

								lowerCamelCase__						:						Tuple						      =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
								lowerCamelCase__						:						Optional[Any]						      =np.interp(__UpperCamelCase,    np.arange(0,    len(__UpperCamelCase	)	),    __UpperCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
								lowerCamelCase__						:						Any						      =torch.from_numpy(__UpperCamelCase	).to(device=__UpperCamelCase	)
								if str(__UpperCamelCase	).startswith('''mps'''	):
											# mps does not support float64
											lowerCamelCase__						:						Any						      =torch.from_numpy(__UpperCamelCase	).to(__UpperCamelCase,    dtype=torch.floataa	)
								else:
											lowerCamelCase__						:						Any						      =torch.from_numpy(__UpperCamelCase	).to(device=__UpperCamelCase	)



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    torch.FloatTensor,    lowerCamelCase				:    Union[float, torch.FloatTensor],    lowerCamelCase				:    torch.FloatTensor,    lowerCamelCase				:    Optional[torch.Generator] = None,    lowerCamelCase				:    bool = True,    )->   Tuple:

								if (
								    isinstance(__UpperCamelCase,    __UpperCamelCase	)
								    or isinstance(__UpperCamelCase,    torch.IntTensor	)
								    or isinstance(__UpperCamelCase,    torch.LongTensor	)
								):
											raise ValueError(
											    (
											        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
											        ''' `EulerDiscreteScheduler.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(__UpperCamelCase,    torch.Tensor	):
											lowerCamelCase__						:						str						      =timestep.to(self.timesteps.device	)

								lowerCamelCase__						:						List[str]						      =(self.timesteps == timestep).nonzero().item()
								lowerCamelCase__						:						Union[str, Any]						      =self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
											lowerCamelCase__						:						Dict						      =sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											lowerCamelCase__						:						str						      =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
											raise NotImplementedError('''prediction_type not implemented yet: sample'''	)
								else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

								lowerCamelCase__						:						Any						      =self.sigmas[step_index]
								lowerCamelCase__						:						Tuple						      =self.sigmas[step_index + 1]
								lowerCamelCase__						:						Optional[int]						      =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								lowerCamelCase__						:						int						      =(sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								lowerCamelCase__						:						List[str]						      =(sample - pred_original_sample) / sigma

								lowerCamelCase__						:						int						      =sigma_down - sigma

								lowerCamelCase__						:						Dict						      =sample + derivative * dt

								lowerCamelCase__						:						Optional[Any]						      =model_output.device
								lowerCamelCase__						:						Any						      =randn_tensor(model_output.shape,    dtype=model_output.dtype,    device=__UpperCamelCase,    generator=__UpperCamelCase	)

								lowerCamelCase__						:						str						      =prev_sample + noise * sigma_up

								if not return_dict:
											return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=__UpperCamelCase,    pred_original_sample=__UpperCamelCase	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    torch.FloatTensor,    lowerCamelCase				:    torch.FloatTensor,    lowerCamelCase				:    torch.FloatTensor,    )->   List[str]:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase__						:						Tuple						      =self.sigmas.to(device=original_samples.device,    dtype=original_samples.dtype	)
								if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase	):
											# mps does not support float64
											lowerCamelCase__						:						Tuple						      =self.timesteps.to(original_samples.device,    dtype=torch.floataa	)
											lowerCamelCase__						:						Dict						      =timesteps.to(original_samples.device,    dtype=torch.floataa	)
								else:
											lowerCamelCase__						:						Dict						      =self.timesteps.to(original_samples.device	)
											lowerCamelCase__						:						Optional[Any]						      =timesteps.to(original_samples.device	)

								lowerCamelCase__						:						Dict						      =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								lowerCamelCase__						:						Tuple						      =sigmas[step_indices].flatten()
								while len(sigma.shape	) < len(original_samples.shape	):
											lowerCamelCase__						:						List[str]						      =sigma.unsqueeze(-1	)

								lowerCamelCase__						:						Optional[int]						      =original_samples + noise * sigma
								return noisy_samples




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











"""simple docstring"""
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


_snake_case	:						List[str]					=					logging.get_logger(__name__)






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

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

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												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."						)

												A						=    isinstance(__UpperCamelCase	,			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}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

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

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

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

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

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

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

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


