



"""simple docstring"""






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__              =							logging.get_logger(__name__)






def 			snake_case_    ( A_   :						List[Any],							A_   :						int,							A_   :						List[Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					UniSpeechSatForSequenceClassification.from_pretrained(A_,							config=A_		)
       _lowerCamelCase		:							List[str]            =					downstream_dict['''projector.weight''']
       _lowerCamelCase		:							Union[str, Any]            =					downstream_dict['''projector.bias''']
       _lowerCamelCase		:							Union[str, Any]            =					downstream_dict['''model.post_net.linear.weight''']
       _lowerCamelCase		:							int            =					downstream_dict['''model.post_net.linear.bias''']
       return model






def 			snake_case_    ( A_   :						List[str],							A_   :						Optional[Any],							A_   :						Dict		):


       '''simple docstring'''





       _lowerCamelCase		:							int            =					UniSpeechSatForAudioFrameClassification.from_pretrained(A_,							config=A_		)
       _lowerCamelCase		:							Dict            =					downstream_dict['''model.linear.weight''']
       _lowerCamelCase		:							int            =					downstream_dict['''model.linear.bias''']
       return model






def 			snake_case_    ( A_   :						int,							A_   :						str,							A_   :						Tuple		):


       '''simple docstring'''





       _lowerCamelCase		:							List[Any]            =					UniSpeechSatForXVector.from_pretrained(A_,							config=A_		)
       _lowerCamelCase		:							Tuple            =					downstream_dict['''connector.weight''']
       _lowerCamelCase		:							Any            =					downstream_dict['''connector.bias''']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              _lowerCamelCase		:							Dict            =					downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              _lowerCamelCase		:							int            =					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       _lowerCamelCase		:							str            =					downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
       _lowerCamelCase		:							Optional[Any]            =					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
       _lowerCamelCase		:							List[str]            =					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
       _lowerCamelCase		:							int            =					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
       _lowerCamelCase		:							Dict            =					downstream_dict['''objective.W''']
       return model






@torch.no_grad()
def 			snake_case_    ( A_   :						Union[str, Any],							A_   :						Optional[int],							A_   :						Union[str, Any],							A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[Any]            =					torch.load(A_,							map_location='''cpu'''		)

       _lowerCamelCase		:							str            =					checkpoint['''Downstream''']

       _lowerCamelCase		:							Any            =					UniSpeechSatConfig.from_pretrained(A_		)
       _lowerCamelCase		:							Tuple            =					WavaVecaFeatureExtractor.from_pretrained(
           A_,							return_attention_mask=A_,							do_normalize=A_		)

       _lowerCamelCase		:							Tuple            =					hf_config.architectures[0]
       if arch.endswith('''ForSequenceClassification'''		):
              _lowerCamelCase		:							Union[str, Any]            =					convert_classification(A_,							A_,							A_		)
       elif arch.endswith('''ForAudioFrameClassification'''		):
              _lowerCamelCase		:							int            =					convert_diarization(A_,							A_,							A_		)
       elif arch.endswith('''ForXVector'''		):
              _lowerCamelCase		:							Tuple            =					convert_xvector(A_,							A_,							A_		)
       else:
              raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''		)

       if hf_config.use_weighted_layer_sum:
              _lowerCamelCase		:							Tuple            =					checkpoint['''Featurizer''']['''weights''']

       hf_feature_extractor.save_pretrained(A_		)
       hf_model.save_pretrained(A_		)


if __name__ == "__main__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   parser.add_argument(
       '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
   )
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
   parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
   lowerCAmelCase__              =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










'''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 PaddingStrategy, TensorType, logging


UpperCamelCase      					=logging.get_logger(__name__)




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    List[Any]			  =    ['''input_features''', '''attention_mask''']
 def __init__(				self      ,  __lowerCAmelCase=80      ,  __lowerCAmelCase=1_60_00      ,  __lowerCAmelCase=80      ,  __lowerCAmelCase=0.0      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  **__lowerCAmelCase      ,  ):
       super().__init__(feature_size=__lowerCAmelCase      ,  sampling_rate=__lowerCAmelCase      ,  padding_value=__lowerCAmelCase      ,  **__lowerCAmelCase			)
       UpperCamelCase_					: int            =				num_mel_bins
       UpperCamelCase_					: Union[str, Any]            =				do_ceptral_normalize
       UpperCamelCase_					: Any            =				normalize_means
       UpperCamelCase_					: int            =				normalize_vars
       UpperCamelCase_					: int            =				True
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Optional[Any]            =				waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase_					: Optional[int]            =				torch.from_numpy(__lowerCAmelCase			).unsqueeze(0			)
       UpperCamelCase_					: Tuple            =				ta_kaldi.fbank(__lowerCAmelCase      ,  num_mel_bins=self.num_mel_bins      ,  sample_frequency=self.sampling_rate			)
       return features.numpy()
 @staticmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 0.0      ,  ):
       # make sure we normalize float32 arrays
       if normalize_means:
             UpperCamelCase_					: int            =				x[:input_length].mean(axis=0			)
             UpperCamelCase_					: str            =				np.subtract(__lowerCAmelCase      ,  __lowerCAmelCase			)
       if normalize_vars:
             UpperCamelCase_					: Optional[int]            =				x[:input_length].std(axis=0			)
             UpperCamelCase_					: Optional[Any]            =				np.divide(__lowerCAmelCase      ,  __lowerCAmelCase			)

       if input_length < x.shape[0]:
             UpperCamelCase_					: Any            =				padding_value

       # make sure array is in float32
       UpperCamelCase_					: str            =				x.astype(np.floataa			)

       return x
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: List[str]            =				attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(__lowerCAmelCase      ,  __lowerCAmelCase      ,  self.normalize_means      ,  self.normalize_vars      ,  self.padding_value			)
           for x, n in zip(__lowerCAmelCase      ,  __lowerCAmelCase			)
       ]





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  ):

       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."""			)

       UpperCamelCase_					: Optional[int]            =				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}"			)
       UpperCamelCase_					: List[str]            =				is_batched_numpy or (
           isinstance(__lowerCAmelCase      ,  (list, tuple)			) and (isinstance(raw_speech[0]      ,  (np.ndarray, tuple, list)			))
       )

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

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

       # extract fbank features
       UpperCamelCase_					: Any            =				[self._extract_fbank_features(__lowerCAmelCase			) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase_					: Tuple            =				BatchFeature({"""input_features""": features}			)

       UpperCamelCase_					: Tuple            =				self.pad(
           __lowerCAmelCase      ,  padding=__lowerCAmelCase      ,  max_length=__lowerCAmelCase      ,  truncation=__lowerCAmelCase      ,  pad_to_multiple_of=__lowerCAmelCase      ,  return_attention_mask=__lowerCAmelCase      ,  **__lowerCAmelCase      ,  )

       # make sure list is in array format
       UpperCamelCase_					: str            =				padded_inputs.get("""input_features"""			)
       if isinstance(input_features[0]      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Any            =				[np.asarray(__lowerCAmelCase      ,  dtype=np.floataa			) for feature in input_features]

       UpperCamelCase_					: Union[str, Any]            =				padded_inputs.get("""attention_mask"""			)
       if attention_mask is not None:
             UpperCamelCase_					: Union[str, Any]            =				[np.asarray(__lowerCAmelCase      ,  dtype=np.intaa			) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
             UpperCamelCase_					: List[str]            =				(
                 np.array(__lowerCAmelCase      ,  dtype=np.intaa			)
                 if self._get_padding_strategies(__lowerCAmelCase      ,  max_length=__lowerCAmelCase			) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase_					: Optional[int]            =				self.normalize(
                 padded_inputs["""input_features"""]      ,  attention_mask=__lowerCAmelCase			)

       if return_tensors is not None:
             UpperCamelCase_					: Optional[Any]            =				padded_inputs.convert_to_tensors(__lowerCAmelCase			)

       return padded_inputs






