




"""simple docstring"""


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

import numpy as np

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


A_						=				logging.get_logger(__name__)






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["input_features"]


 def __init__(				self:		List[Any],      a_:		Tuple=80,      a_:		List[str]=16_000,      a_:		Tuple=160,      a_:		Optional[int]=30,      a_:		List[str]=400,      a_:		Union[str, Any]=0.0,      a_:		Union[str, Any]=False,      **a_:		Dict,      ):




   '''simple docstring'''



   super().__init__(
       feature_size=a_,      sampling_rate=a_,      padding_value=a_,      return_attention_mask=a_,      **a_,      )
   _snake_case							:		Union[str, Any]								=  n_fft
   _snake_case							:		Optional[Any]								=  hop_length
   _snake_case							:		Optional[int]								=  chunk_length
   _snake_case							:		str								=  chunk_length * sampling_rate
   _snake_case							:		Dict								=  self.n_samples // hop_length
   _snake_case							:		List[str]								=  sampling_rate
   _snake_case							:		Optional[int]								=  mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2,      num_mel_filters=a_,      min_frequency=0.0,      max_frequency=8_000.0,      sampling_rate=a_,      norm="""slaney""",      mel_scale="""slaney""",      )


 def  UpperCamelCase_   (				self:		List[Any],      a_:		np.array		):




   '''simple docstring'''



   _snake_case							:		str								=  spectrogram(
       a_,      window_function(self.n_fft,      """hann"""		),      frame_length=self.n_fft,      hop_length=self.hop_length,      power=2.0,      mel_filters=self.mel_filters,      log_mel="""log10""",      )
   _snake_case							:		Dict								=  log_spec[:, :-1]
   _snake_case							:		List[Any]								=  np.maximum(a_,      log_spec.max() - 8.0		)
   _snake_case							:		int								=  (log_spec + 4.0) / 4.0
   return log_spec


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




   '''simple docstring'''



   if attention_mask is not None:
     _snake_case							:		str								=  np.array(a_,      np.intaa		)
     _snake_case							:		str								=  []

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

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

   return normed_input_values


 def __call__(				self:		Optional[int],      a_:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],      a_:		bool = True,      a_:		Optional[int] = None,      a_:		Optional[Union[str, TensorType]] = None,      a_:		Optional[bool] = None,      a_:		Optional[str] = "max_length",      a_:		Optional[int] = None,      a_:		Optional[int] = None,      a_:		Optional[bool] = None,      **a_:		Any,      ):




   '''simple docstring'''



   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
           f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
           f" was sampled with {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							:		str								=  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							:		Optional[Any]								=  is_batched_numpy or (
       isinstance(a_,      (list, tuple)		) and (isinstance(raw_speech[0],      (np.ndarray, tuple, list)		))
   )

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

   # always return batch
   if not is_batched:
     _snake_case							:		List[str]								=  [np.asarray([raw_speech]		).T]

   _snake_case							:		Optional[int]								=  BatchFeature({"""input_features""": raw_speech}		)

   # convert into correct format for padding

   _snake_case							:		Optional[Any]								=  self.pad(
       a_,      padding=a_,      max_length=max_length if max_length else self.n_samples,      truncation=a_,      pad_to_multiple_of=a_,      return_attention_mask=return_attention_mask or do_normalize,      )

   # zero-mean and unit-variance normalization
   if do_normalize:
     _snake_case							:		Dict								=  self.zero_mean_unit_var_norm(
         padded_inputs["""input_features"""],      attention_mask=padded_inputs["""attention_mask"""],      padding_value=self.padding_value,      )
     _snake_case							:		Tuple								=  np.stack(padded_inputs["""input_features"""],      axis=0		)

   # make sure list is in array format
   _snake_case							:		Optional[int]								=  padded_inputs.get("""input_features"""		).transpose(2,      0,      1		)

   _snake_case							:		Dict								=  [self._np_extract_fbank_features(a_		) for waveform in input_features[0]]

   if isinstance(input_features[0],      a_		):
     _snake_case							:		List[str]								=  [np.asarray(a_,      dtype=np.floataa		) for feature in input_features]
   else:
     _snake_case							:		List[Any]								=  input_features

   if return_attention_mask:
     # rescale from sample (48000) to feature (3000)
     _snake_case							:		Any								=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

   if return_tensors is not None:
     _snake_case							:		List[str]								=  padded_inputs.convert_to_tensors(a_		)

   return padded_inputs


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		Union[str, Any]								=  self.__class__.__name__
   if "mel_filters" in output:
     del output["mel_filters"]
   return output








"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


