
import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case      = logging.get_logger(__name__)
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=["input_values", "attention_mask"]

 def __init__(						self			:			Any					, SCREAMING_SNAKE_CASE__			:			int = 1					, SCREAMING_SNAKE_CASE__			:			int = 1_60_00					, SCREAMING_SNAKE_CASE__			:			float = 0.0					, SCREAMING_SNAKE_CASE__			:			bool = False					, SCREAMING_SNAKE_CASE__			:			int = 80					, SCREAMING_SNAKE_CASE__			:			int = 16					, SCREAMING_SNAKE_CASE__			:			int = 64					, SCREAMING_SNAKE_CASE__			:			str = "hann_window"					, SCREAMING_SNAKE_CASE__			:			float = 1.0					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 76_00					, SCREAMING_SNAKE_CASE__			:			float = 1e-10					, SCREAMING_SNAKE_CASE__			:			int = 2					, SCREAMING_SNAKE_CASE__			:			bool = True					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      super().__init__(feature_size=SCREAMING_SNAKE_CASE__					, sampling_rate=SCREAMING_SNAKE_CASE__					, padding_value=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					return_attention_mask

      UpperCamelCase						    =					num_mel_bins
      UpperCamelCase						    =					hop_length
      UpperCamelCase						    =					win_length
      UpperCamelCase						    =					win_function
      UpperCamelCase						    =					frame_signal_scale
      UpperCamelCase						    =					fmin
      UpperCamelCase						    =					fmax
      UpperCamelCase						    =					mel_floor
      UpperCamelCase						    =					reduction_factor

      UpperCamelCase						    =					win_length * sampling_rate // 10_00
      UpperCamelCase						    =					hop_length * sampling_rate // 10_00
      UpperCamelCase						    =					optimal_fft_length(self.sample_size   )
      UpperCamelCase						    =					(self.n_fft // 2) + 1

      UpperCamelCase						    =					window_function(window_length=self.sample_size					, name=self.win_function					, periodic=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					mel_filter_bank(
          num_frequency_bins=self.n_freqs					, num_mel_filters=self.num_mel_bins					, min_frequency=self.fmin					, max_frequency=self.fmax					, sampling_rate=self.sampling_rate					, norm='slaney'					, mel_scale='slaney'					, )

      if frame_signal_scale != 1.0:
           warnings.warn(
               'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'					, SCREAMING_SNAKE_CASE__					, )
      if reduction_factor != 2.0:
           warnings.warn(
               'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'					, SCREAMING_SNAKE_CASE__					, )

 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def      __lowerCAmelCase  (						SCREAMING_SNAKE_CASE__			:			List[np.ndarray]					, SCREAMING_SNAKE_CASE__			:			List[np.ndarray]					, SCREAMING_SNAKE_CASE__			:			float = 0.0   ):






      """simple docstring"""



      if attention_mask is not None:
           UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__					, np.intaa   )
           UpperCamelCase						    =					[]

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

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

      return normed_input_values

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, ):






      """simple docstring"""



      UpperCamelCase						    =					spectrogram(
          SCREAMING_SNAKE_CASE__					, window=self.window					, frame_length=self.sample_size					, hop_length=self.sample_stride					, fft_length=self.n_fft					, mel_filters=self.mel_filters					, mel_floor=self.mel_floor					, log_mel='log10'					, )
      return log_mel_spec.T

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






      """simple docstring"""



      if audio is None and audio_target is None:
           raise ValueError('You must provide either `audio` or `audio_target` values.'   )

      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 audio 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.'   )

      if audio is not None:
           UpperCamelCase						    =					self._process_audio(
               SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )
      else:
           UpperCamelCase						    =					None

      if audio_target is not None:
           UpperCamelCase						    =					self._process_audio(
               SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )

           if inputs is None:
                return inputs_target
           else:
                UpperCamelCase						    =					inputs_target['input_values']
                UpperCamelCase						    =					inputs_target.get('attention_mask'   )
                if decoder_attention_mask is not None:
                     UpperCamelCase						    =					decoder_attention_mask

      return inputs

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






      """simple docstring"""



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

      if is_batched:
           UpperCamelCase						    =					[np.asarray(SCREAMING_SNAKE_CASE__					, dtype=np.floataa   ) for speech in speech]
      elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__					, np.ndarray   ):
           UpperCamelCase						    =					np.asarray(SCREAMING_SNAKE_CASE__					, dtype=np.floataa   )
      elif isinstance(SCREAMING_SNAKE_CASE__					, np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
           UpperCamelCase						    =					speech.astype(np.floataa   )

      # always return batch
      if not is_batched:
           UpperCamelCase						    =					[speech]

      # needed to make pad() work on spectrogram inputs
      UpperCamelCase						    =					self.feature_size

      # convert into correct format for padding
      if is_target:
           UpperCamelCase						    =					[self._extract_mel_features(SCREAMING_SNAKE_CASE__   ) for waveform in speech]
           UpperCamelCase						    =					BatchFeature({'input_values': features}   )
           UpperCamelCase						    =					self.num_mel_bins
      else:
           UpperCamelCase						    =					BatchFeature({'input_values': speech}   )

      UpperCamelCase						    =					self.pad(
          SCREAMING_SNAKE_CASE__					, padding=SCREAMING_SNAKE_CASE__					, max_length=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, pad_to_multiple_of=SCREAMING_SNAKE_CASE__					, return_attention_mask=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					feature_size_hack

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

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

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

      if return_tensors is not None:
           UpperCamelCase						    =					padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__   )

      return padded_inputs






 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      UpperCamelCase						    =					['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
      for name in names:
           if name in output:
                del output[name]

      return output




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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


