

"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=["audio_values", "audio_mask"]







  def __init__(      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]=20_48	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   Tuple=[16, 16]	,							lowerCAmelCase			:   Any=1_28	,							lowerCAmelCase			:   Dict=4_41_00	,							lowerCAmelCase			:   int=86	,							lowerCAmelCase			:   str=20_48	,							lowerCAmelCase			:   List[str]=0.0	,							**lowerCAmelCase			:   int	,							)							->     Optional[int]:






     """simple docstring"""







     super().__init__(
         feature_size=lowerCAmelCase	,							sampling_rate=lowerCAmelCase	,							padding_value=lowerCAmelCase	,							**lowerCAmelCase	,							)

     __lowerCAmelCase  :      Dict    =						spectrogram_length
     __lowerCAmelCase  :      str    =						num_channels
     __lowerCAmelCase  :      Union[str, Any]    =						patch_size
     __lowerCAmelCase  :      int    =						feature_size // self.patch_size[1]
     __lowerCAmelCase  :      str    =						n_fft
     __lowerCAmelCase  :      Optional[Any]    =						sampling_rate // hop_length_to_sampling_rate
     __lowerCAmelCase  :      Tuple    =						sampling_rate
     __lowerCAmelCase  :      Any    =						padding_value
     __lowerCAmelCase  :      str    =						mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2	,							num_mel_filters=lowerCAmelCase	,							min_frequency=0.0	,							max_frequency=2_20_50.0	,							sampling_rate=lowerCAmelCase	,							norm="""slaney"""	,							mel_scale="""slaney"""	,							).T







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   np.array   )							->     np.ndarray:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						spectrogram(
         lowerCAmelCase	,							window_function(self.n_fft	,							"""hann"""   )	,							frame_length=self.n_fft	,							hop_length=self.hop_length	,							power=2.0	,							mel_filters=self.mel_filters.T	,							log_mel="""dB"""	,							db_range=80.0	,							)
     __lowerCAmelCase  :      Union[str, Any]    =						log_spec[:, :-1]
     __lowerCAmelCase  :      Optional[int]    =						log_spec - 20.0
     __lowerCAmelCase  :      List[str]    =						np.clip(log_spec / 40.0	,							-2.0	,							0.0   ) + 1.0
     return log_spec







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






     """simple docstring"""







     if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
           raise ValueError(
               """This feature extractor is set to support sampling rate"""
               f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
               f''' 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."""   )

     __lowerCAmelCase  :      List[str]    =						isinstance(lowerCAmelCase	,							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}'''   )
     __lowerCAmelCase  :      Any    =						is_batched_numpy or (
         isinstance(lowerCAmelCase	,							(list, tuple)   ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)   ))
     )
     if is_batched:
        __lowerCAmelCase  :      int    =						[np.asarray([speech]	,							dtype=np.floataa   ).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowerCAmelCase	,							np.ndarray   ):
        __lowerCAmelCase  :      Dict    =						np.asarray(lowerCAmelCase	,							dtype=np.floataa   )
     elif isinstance(lowerCAmelCase	,							np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
        __lowerCAmelCase  :      Optional[Any]    =						raw_speech.astype(np.floataa   )
     # always return batch
     if not is_batched:
        __lowerCAmelCase  :      List[Any]    =						[np.asarray([raw_speech]   ).T]

     # Convert audio signals to log mel spectrograms, truncate by time axis
     __lowerCAmelCase  :      int    =						[
         self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
     ]
     if isinstance(audio_features[0]	,							lowerCAmelCase   ):
        __lowerCAmelCase  :      Dict    =						[np.asarray(lowerCAmelCase	,							dtype=np.floataa   ) for feature in audio_features]

     # Create audio attention mask
     __lowerCAmelCase  :      int    =						max(
         [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
     if return_attention_mask:
        __lowerCAmelCase  :      Union[str, Any]    =						[
            (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
            + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
            for feature in audio_features
        ]
        __lowerCAmelCase  :      List[str]    =						np.array(lowerCAmelCase   ).astype(np.floataa   )

     # convert into correct format for padding
     __lowerCAmelCase  :      List[Any]    =						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
     __lowerCAmelCase  :      Dict    =						np.ones([len(lowerCAmelCase   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
     __lowerCAmelCase  :      Optional[int]    =						padded_audio_features * self.padding_value
     for i in range(len(lowerCAmelCase   )   ):
        __lowerCAmelCase  :      Tuple    =						audio_features[i]
        __lowerCAmelCase  :      Optional[int]    =						feature

     # return as BatchFeature
     if return_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
     else:
        __lowerCAmelCase  :      Dict    =						{"""audio_values""": padded_audio_features}

     __lowerCAmelCase  :      Tuple    =						BatchFeature(data=lowerCAmelCase	,							tensor_type=lowerCAmelCase   )
     return encoded_inputs





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
   import transformers





def      snake_case_				(__A					:						int					)      ->     List[Any]:
   random.seed(__A					)
   np.random.seed(__A					)
   torch.manual_seed(__A					)
   torch.cuda.manual_seed_all(__A					)
   # ^^ safe to call this function even if cuda is not available



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 0	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Union[float, int] = 1.0	,							lowerCAmelCase			:   Union[float, int] = 2 / 3	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Dict[str, Any] = None	,							**lowerCAmelCase			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      int    =						parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        __lowerCAmelCase  :      Optional[Any]    =						True

     if kwargs.get("""max_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[Any]    =						"""The `max_value` argument is deprecated. Please use `decay` instead."""
        deprecate("""max_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						kwargs["""max_value"""]

     if kwargs.get("""min_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[int]    =						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
        deprecate("""min_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						kwargs["""min_value"""]

     __lowerCAmelCase  :      Optional[Any]    =						list(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[p.clone().detach() for p in parameters]

     if kwargs.get("""device"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      str    =						"""The `device` argument is deprecated. Please use `to` instead."""
        deprecate("""device"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        self.to(device=kwargs["""device"""]   )

     __lowerCAmelCase  :      List[Any]    =						None

     __lowerCAmelCase  :      Any    =						decay
     __lowerCAmelCase  :      Any    =						min_decay
     __lowerCAmelCase  :      List[Any]    =						update_after_step
     __lowerCAmelCase  :      Optional[Any]    =						use_ema_warmup
     __lowerCAmelCase  :      Union[str, Any]    =						inv_gamma
     __lowerCAmelCase  :      Union[str, Any]    =						power
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      str    =						None  # set in `step()`

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     int:






     """simple docstring"""







     if self.model_cls is None:
        raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""   )

     if self.model_config is None:
        raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[Any]    =						self.state_dict()
     state_dict.pop("""shadow_params"""	,							lowerCAmelCase   )

     model.register_to_config(**lowerCAmelCase   )
     self.copy_to(model.parameters()   )
     model.save_pretrained(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        __lowerCAmelCase  :      str    =						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        __lowerCAmelCase  :      Optional[int]    =						(1 + step) / (10 + step)

     __lowerCAmelCase  :      Any    =						min(lowerCAmelCase	,							self.decay   )
     # make sure decay is not smaller than min_decay
     __lowerCAmelCase  :      Union[str, Any]    =						max(lowerCAmelCase	,							self.min_decay   )
     return cur_decay_value







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     __lowerCAmelCase  :      Optional[Any]    =						self.get_decay(self.optimization_step   )
     __lowerCAmelCase  :      Dict    =						decay
     __lowerCAmelCase  :      Tuple    =						1 - decay

     __lowerCAmelCase  :      str    =						contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           __lowerCAmelCase  :      List[Any]    =						deepspeed.zero.GatheredParameters(lowerCAmelCase	,							modifier_rank=lowerCAmelCase   )

        with context_manager():
           if param.requires_grad:
              s_param.sub_(one_minus_decay * (s_param - param)   )
           else:
              s_param.copy_(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						list(lowerCAmelCase   )
     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        param.data.copy_(s_param.to(param.device   ).data   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[Any]=None   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[
         p.to(device=lowerCAmelCase	,							dtype=lowerCAmelCase   ) if p.is_floating_point() else p.to(device=lowerCAmelCase   )
         for p in self.shadow_params
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     dict:






     """simple docstring"""







     return {
         "decay": self.decay,
         "min_decay": self.min_decay,
         "optimization_step": self.optimization_step,
         "update_after_step": self.update_after_step,
         "use_ema_warmup": self.use_ema_warmup,
         "inv_gamma": self.inv_gamma,
         "power": self.power,
         "shadow_params": self.shadow_params,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[param.detach().cpu().clone() for param in parameters]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     if self.temp_stored_params is None:
        raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""   )
     for c_param, param in zip(self.temp_stored_params	,							lowerCAmelCase   ):
        param.data.copy_(c_param.data   )

     # Better memory-wise.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						state_dict.get("""decay"""	,							self.decay   )
     if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("""Decay must be between 0 and 1"""   )

     __lowerCAmelCase  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, Any]    =						state_dict.get("""update_after_step"""	,							self.update_after_step   )
     if not isinstance(self.update_after_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid update_after_step"""   )

     __lowerCAmelCase  :      List[str]    =						state_dict.get("""use_ema_warmup"""	,							self.use_ema_warmup   )
     if not isinstance(self.use_ema_warmup	,							lowerCAmelCase   ):
        raise ValueError("""Invalid use_ema_warmup"""   )

     __lowerCAmelCase  :      Optional[int]    =						state_dict.get("""inv_gamma"""	,							self.inv_gamma   )
     if not isinstance(self.inv_gamma	,							(float, int)   ):
        raise ValueError("""Invalid inv_gamma"""   )

     __lowerCAmelCase  :      Any    =						state_dict.get("""power"""	,							self.power   )
     if not isinstance(self.power	,							(float, int)   ):
        raise ValueError("""Invalid power"""   )

     __lowerCAmelCase  :      Optional[Any]    =						state_dict.get("""shadow_params"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						shadow_params
        if not isinstance(self.shadow_params	,							lowerCAmelCase   ):
           raise ValueError("""shadow_params must be a list"""   )
        if not all(isinstance(lowerCAmelCase	,							torch.Tensor   ) for p in self.shadow_params   ):
           raise ValueError("""shadow_params must all be Tensors"""   )


