


"""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


__snake_case												=							logging.get_logger(__name__)


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Any     =		['input_values', 'attention_mask']


     def __init__(   self     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=16000     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=1024     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=-4.2677393     ,					__UpperCAmelCase=4.5689974     ,					__UpperCAmelCase=False     ,					**__UpperCAmelCase     ,					)							->				Optional[Any]:
          super().__init__(feature_size=__UpperCAmelCase     ,					sampling_rate=__UpperCAmelCase     ,					padding_value=__UpperCAmelCase     ,					**__UpperCAmelCase				)
          _a     						=					num_mel_bins
          _a     						=					max_length
          _a     						=					do_normalize
          _a     						=					mean
          _a     						=					std
          _a     						=					return_attention_mask


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)							->				np.ndarray:
          _a     						=					torch.from_numpy(__UpperCAmelCase				).unsqueeze(0				)
          _a     						=					ta_kaldi.fbank(
              __UpperCAmelCase     ,					htk_compat=__UpperCAmelCase     ,					sample_frequency=self.sampling_rate     ,					use_energy=__UpperCAmelCase     ,					window_type='''hanning'''     ,					num_mel_bins=self.num_mel_bins     ,					dither=0.0     ,					frame_shift=10     ,					)

          _a     						=					fbank.shape[0]
          _a     						=					max_length - n_frames

          # pad or truncate, depending on difference
          if difference > 0:
               _a     						=					torch.nn.ZeroPadad((0, 0, 0, difference)				)
               _a     						=					pad_module(__UpperCAmelCase				)
          elif difference < 0:
               _a     						=					fbank[0:max_length, :]

          _a     						=					fbank.numpy()

          return fbank


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				np.ndarray:
          return (input_values - (self.mean)) / (self.std * 2)





     def __call__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					)							->				BatchFeature:

          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.'''				)

          _a     						=					isinstance(__UpperCAmelCase     ,					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}'				)
          _a     						=					is_batched_numpy or (
              isinstance(__UpperCAmelCase     ,					(list, tuple)				) and (isinstance(raw_speech[0]     ,					(np.ndarray, tuple, list)				))
          )

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

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

          # extract fbank features and pad/truncate to max_length
          _a     						=					[self._extract_fbank_features(__UpperCAmelCase     ,					max_length=self.max_length				) for waveform in raw_speech]

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

          # make sure list is in array format
          _a     						=					padded_inputs.get('''input_values'''				)
          if isinstance(input_values[0]     ,					__UpperCAmelCase				):
               _a     						=					[np.asarray(__UpperCAmelCase     ,					dtype=np.floataa				) for feature in input_values]

          # normalization
          if self.do_normalize:
               _a     						=					[self.normalize(__UpperCAmelCase				) for feature in input_values]

          if return_tensors is not None:
               _a     						=					padded_inputs.convert_to_tensors(__UpperCAmelCase				)

          return padded_inputs



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)