





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


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Tuple						      =     ["input_values", "attention_mask"]






     def __init__(				self		:       Dict		,lowerCamelCase__		:       str=1		,lowerCamelCase__		:       List[str]=16000		,lowerCamelCase__		:       Optional[int]=128		,lowerCamelCase__		:       str=1024		,lowerCamelCase__		:       List[str]=0.0		,lowerCamelCase__		:       Dict=True		,lowerCamelCase__		:       Union[str, Any]=-4.2677393		,lowerCamelCase__		:       Optional[int]=4.5689974		,lowerCamelCase__		:       List[Any]=False		,**lowerCamelCase__		:       Optional[Any]		,)	->						Optional[Any]:




           '''simple docstring'''


           super().__init__(feature_size=lowerCamelCase__		,sampling_rate=lowerCamelCase__		,padding_value=lowerCamelCase__		,**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		num_mel_bins
           SCREAMING_SNAKE_CASE						=		max_length
           SCREAMING_SNAKE_CASE						=		do_normalize
           SCREAMING_SNAKE_CASE						=		mean
           SCREAMING_SNAKE_CASE						=		std
           SCREAMING_SNAKE_CASE						=		return_attention_mask






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       np.ndarray		,lowerCamelCase__		:       int		,)	->						np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		torch.from_numpy(lowerCamelCase__			).unsqueeze(0			)
           SCREAMING_SNAKE_CASE						=		ta_kaldi.fbank(
               lowerCamelCase__		,htk_compat=lowerCamelCase__		,sample_frequency=self.sampling_rate		,use_energy=lowerCamelCase__		,window_type="""hanning"""		,num_mel_bins=self.num_mel_bins		,dither=0.0		,frame_shift=10		,)

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

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

           SCREAMING_SNAKE_CASE						=		fbank.numpy()

           return fbank






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       np.ndarray			)	->						np.ndarray:




           '''simple docstring'''


           return (input_values - (self.mean)) / (self.std * 2)






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




           '''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} 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."""			)

           SCREAMING_SNAKE_CASE						=		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}"""			)
           SCREAMING_SNAKE_CASE						=		is_batched_numpy or (
               isinstance(lowerCamelCase__		,(list, tuple)			) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)			))
           )

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

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

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

           # convert into BatchFeature
           SCREAMING_SNAKE_CASE						=		BatchFeature({"""input_values""": features}			)

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

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

           if return_tensors is not None:
                 SCREAMING_SNAKE_CASE						=		padded_inputs.convert_to_tensors(lowerCamelCase__			)

           return padded_inputs









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


