



"""simple docstring"""





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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class       __lowercase       :


				'''simple docstring'''


				_lowerCAmelCase				=42

				# setable values
				_lowerCAmelCase				=42
				_lowerCAmelCase				=42
				_lowerCAmelCase				=42
				_lowerCAmelCase				=None

				# running values
				_lowerCAmelCase				=None

				@classmethod
				def       _lowerCamelCase					(   cls   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     CommonSchedulerState      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ):
										return cls(common=SCREAMING_SNAKE_CASE_      ,init_noise_sigma=SCREAMING_SNAKE_CASE_      ,timesteps=SCREAMING_SNAKE_CASE_      ,sigmas=SCREAMING_SNAKE_CASE_      )







@dataclass
class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				_lowerCAmelCase				=42







class       __lowercase       (			snake_case_						,   snake_case_	):


				'''simple docstring'''


				_lowerCAmelCase				=[e.name for e in FlaxKarrasDiffusionSchedulers]

				_lowerCAmelCase				=42

				@property
				def       _lowerCamelCase					(   self   :     str      ):
										return True

				@register_to_config
				def __init__(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     int = 1_0_0_0      ,SCREAMING_SNAKE_CASE_   :     float = 0.0001      ,SCREAMING_SNAKE_CASE_   :     float = 0.02      ,SCREAMING_SNAKE_CASE_   :     str = "linear"      ,SCREAMING_SNAKE_CASE_   :     Optional[jnp.ndarray] = None      ,SCREAMING_SNAKE_CASE_   :     str = "epsilon"      ,SCREAMING_SNAKE_CASE_   :     jnp.dtype = jnp.floataa      ,):
										snake_case__       :					Optional[Any]										=					dtype

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[CommonSchedulerState] = None      ):
										if common is None:
																snake_case__       :					str										=					CommonSchedulerState.create(self      )

										snake_case__       :					Optional[Any]										=					jnp.arange(0      ,self.config.num_train_timesteps      ).round()[::-1]
										snake_case__       :					Any										=					((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										snake_case__       :					List[str]										=					sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=SCREAMING_SNAKE_CASE_      ,init_noise_sigma=SCREAMING_SNAKE_CASE_      ,timesteps=SCREAMING_SNAKE_CASE_      ,sigmas=SCREAMING_SNAKE_CASE_      ,)

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     LMSDiscreteSchedulerState      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     int      ):
										(snake_case__	)       :					Optional[Any]										=					jnp.where(state.timesteps == timestep      ,size=1      )
										snake_case__       :					Optional[int]										=					step_index[0]

										snake_case__       :					Optional[int]										=					state.sigmas[step_index]
										snake_case__       :					Optional[int]										=					sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     LMSDiscreteSchedulerState      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     List[str]      ):

										def lms_derivative(SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
																snake_case__       :					Tuple										=					1.0
																for k in range(SCREAMING_SNAKE_CASE_      ):
																						if current_order == k:
																												continue
																						prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																return prod

										snake_case__       :					Union[str, Any]										=					integrate.quad(SCREAMING_SNAKE_CASE_      ,state.sigmas[t]      ,state.sigmas[t + 1]      ,epsrel=1E-4      )[0]

										return integrated_coeff

				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     LMSDiscreteSchedulerState      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     Tuple = ()      ):
										snake_case__       :					Optional[int]										=					jnp.linspace(self.config.num_train_timesteps - 1      ,0      ,SCREAMING_SNAKE_CASE_      ,dtype=self.dtype      )

										snake_case__       :					List[Any]										=					jnp.floor(SCREAMING_SNAKE_CASE_      ).astype(jnp.intaa      )
										snake_case__       :					str										=					jnp.ceil(SCREAMING_SNAKE_CASE_      ).astype(jnp.intaa      )

										snake_case__       :					int										=					jnp.mod(SCREAMING_SNAKE_CASE_      ,1.0      )

										snake_case__       :					Optional[Any]										=					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										snake_case__       :					Dict										=					(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										snake_case__       :					int										=					jnp.concatenate([sigmas, jnp.array([0.0]      ,dtype=self.dtype      )]      )

										snake_case__       :					Union[str, Any]										=					timesteps.astype(jnp.intaa      )

										# initial running values
										snake_case__       :					Dict										=					jnp.zeros((0,) + shape      ,dtype=self.dtype      )

										return state.replace(
										    timesteps=SCREAMING_SNAKE_CASE_      ,sigmas=SCREAMING_SNAKE_CASE_      ,num_inference_steps=SCREAMING_SNAKE_CASE_      ,derivatives=SCREAMING_SNAKE_CASE_      ,)

				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     LMSDiscreteSchedulerState      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     int = 4      ,SCREAMING_SNAKE_CASE_   :     bool = True      ,):
										if state.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__       :					List[str]										=					state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case__       :					List[Any]										=					sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																snake_case__       :					str										=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										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
										snake_case__       :					str										=					(sample - pred_original_sample) / sigma
										snake_case__       :					Tuple										=					state.replace(derivatives=jnp.append(state.derivatives      ,SCREAMING_SNAKE_CASE_      )      )
										if len(state.derivatives      ) > order:
																snake_case__       :					str										=					state.replace(derivatives=jnp.delete(state.derivatives      ,0      )      )

										# 3. Compute linear multistep coefficients
										snake_case__       :					Union[str, Any]										=					min(timestep + 1      ,SCREAMING_SNAKE_CASE_      )
										snake_case__       :					str										=					[self.get_lms_coefficient(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) for curr_order in range(SCREAMING_SNAKE_CASE_      )]

										# 4. Compute previous sample based on the derivatives path
										snake_case__       :					str										=					sample + sum(
										    coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_      ,reversed(state.derivatives      )      )      )

										if not return_dict:
																return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      ,state=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     LMSDiscreteSchedulerState      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,SCREAMING_SNAKE_CASE_   :     jnp.ndarray      ,):
										snake_case__       :					List[Any]										=					state.sigmas[timesteps].flatten()
										snake_case__       :					List[str]										=					broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_      ,noise.shape      )

										snake_case__       :					List[Any]										=					original_samples + noise * sigma

										return noisy_samples





				def __len__(   self   :     Optional[int]      ):
										return self.config.num_train_timesteps








"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


_lowercase	      =    logging.get_logger(__name__)

class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				_lowerCAmelCase				=["input_values", "attention_mask"]

				def __init__(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Dict=1      ,SCREAMING_SNAKE_CASE_   :     List[str]=1_6_0_0_0      ,SCREAMING_SNAKE_CASE_   :     Any=1_2_8      ,SCREAMING_SNAKE_CASE_   :     List[str]=1_0_2_4      ,SCREAMING_SNAKE_CASE_   :     Any=0.0      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=True      ,SCREAMING_SNAKE_CASE_   :     Tuple=-4.2677393      ,SCREAMING_SNAKE_CASE_   :     List[Any]=4.5689974      ,SCREAMING_SNAKE_CASE_   :     str=False      ,**SCREAMING_SNAKE_CASE_   :     Dict      ,):
										super().__init__(feature_size=SCREAMING_SNAKE_CASE_      ,sampling_rate=SCREAMING_SNAKE_CASE_      ,padding_value=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )
										snake_case__       :					str										=					num_mel_bins
										snake_case__       :					Union[str, Any]										=					max_length
										snake_case__       :					List[str]										=					do_normalize
										snake_case__       :					Optional[int]										=					mean
										snake_case__       :					int										=					std
										snake_case__       :					Any										=					return_attention_mask

				def       _lowerCamelCase					(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     np.ndarray      ,SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					Any										=					torch.from_numpy(SCREAMING_SNAKE_CASE_      ).unsqueeze(0      )
										snake_case__       :					Optional[int]										=					ta_kaldi.fbank(
										    SCREAMING_SNAKE_CASE_      ,htk_compat=SCREAMING_SNAKE_CASE_      ,sample_frequency=self.sampling_rate      ,use_energy=SCREAMING_SNAKE_CASE_      ,window_type='''hanning'''      ,num_mel_bins=self.num_mel_bins      ,dither=0.0      ,frame_shift=1_0      ,)

										snake_case__       :					Dict										=					fbank.shape[0]
										snake_case__       :					Optional[int]										=					max_length - n_frames

										# pad or truncate, depending on difference
										if difference > 0:
																snake_case__       :					List[str]										=					torch.nn.ZeroPadad((0, 0, 0, difference)      )
																snake_case__       :					List[Any]										=					pad_module(SCREAMING_SNAKE_CASE_      )
										elif difference < 0:
																snake_case__       :					List[str]										=					fbank[0:max_length, :]

										snake_case__       :					Dict										=					fbank.numpy()

										return fbank

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     np.ndarray      ):
										return (input_values - (self.mean)) / (self.std * 2)





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

										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__       :					Optional[Any]										=					isinstance(SCREAMING_SNAKE_CASE_      ,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__       :					Union[str, Any]										=					is_batched_numpy or (
										    isinstance(SCREAMING_SNAKE_CASE_      ,(list, tuple)      ) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)      ))
										)

										if is_batched:
																snake_case__       :					Optional[Any]										=					[np.asarray(SCREAMING_SNAKE_CASE_      ,dtype=np.floataa      ) for speech in raw_speech]
										elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_      ,np.ndarray      ):
																snake_case__       :					Union[str, Any]										=					np.asarray(SCREAMING_SNAKE_CASE_      ,dtype=np.floataa      )
										elif isinstance(SCREAMING_SNAKE_CASE_      ,np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
																snake_case__       :					List[str]										=					raw_speech.astype(np.floataa      )

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

										# extract fbank features and pad/truncate to max_length
										snake_case__       :					List[Any]										=					[self._extract_fbank_features(SCREAMING_SNAKE_CASE_      ,max_length=self.max_length      ) for waveform in raw_speech]

										# convert into BatchFeature
										snake_case__       :					str										=					BatchFeature({'''input_values''': features}      )

										# make sure list is in array format
										snake_case__       :					Union[str, Any]										=					padded_inputs.get('''input_values'''      )
										if isinstance(input_values[0]      ,SCREAMING_SNAKE_CASE_      ):
																snake_case__       :					List[str]										=					[np.asarray(SCREAMING_SNAKE_CASE_      ,dtype=np.floataa      ) for feature in input_values]

										# normalization
										if self.do_normalize:
																snake_case__       :					List[Any]										=					[self.normalize(SCREAMING_SNAKE_CASE_      ) for feature in input_values]

										if return_tensors is not None:
																snake_case__       :					Union[str, Any]										=					padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_      )

										return padded_inputs


