




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


_lowerCAmelCase						:   Dict		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_features""", """attention_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :List[str]=80 ,      lowerCamelCase   :Any=1_6000 ,      lowerCamelCase   :List[Any]=80 ,      lowerCamelCase   :Dict=0.0 ,      lowerCamelCase   :str=True ,      lowerCamelCase   :Dict=True ,      lowerCamelCase   :List[str]=True ,      **lowerCamelCase   :Any ,      )     ->			Tuple:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						num_mel_bins
       UpperCAmelCase__							 =						do_ceptral_normalize
       UpperCAmelCase__							 =						normalize_means
       UpperCAmelCase__							 =						normalize_vars
       UpperCAmelCase__							 =						True
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Dict ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCAmelCase__							 =						torch.from_numpy(lowerCamelCase      ).unsqueeze(0      )
       UpperCAmelCase__							 =						ta_kaldi.fbank(lowerCamelCase ,      num_mel_bins=self.num_mel_bins ,      sample_frequency=self.sampling_rate      )
       return features.numpy()
      @staticmethod
      def 	UpperCAmelCase_						(			lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :str = True ,      lowerCamelCase   :int = True ,      lowerCamelCase   :int = 0.0 ,      )     ->			np.ndarray:
       # make sure we normalize float32 arrays
       if normalize_means:
        UpperCAmelCase__							 =						x[:input_length].mean(axis=0      )
        UpperCAmelCase__							 =						np.subtract(lowerCamelCase ,      lowerCamelCase      )
       if normalize_vars:
        UpperCAmelCase__							 =						x[:input_length].std(axis=0      )
        UpperCAmelCase__							 =						np.divide(lowerCamelCase ,      lowerCamelCase      )

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

       # make sure array is in float32
       UpperCAmelCase__							 =						x.astype(np.floataa      )

       return x
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Any = None      )     ->			List[np.ndarray]:
       UpperCAmelCase__							 =						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   :Any ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Optional[int] = False ,      lowerCamelCase   :Dict = None ,      lowerCamelCase   :Any = False ,      lowerCamelCase   :Optional[Any] = None ,      lowerCamelCase   :Optional[Any] = None ,      lowerCamelCase   :List[str] = None ,      lowerCamelCase   :Optional[Any] = None ,      **lowerCamelCase   :Tuple ,      )     ->			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."      )

       UpperCAmelCase__							 =						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__							 =						is_batched_numpy or (
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)      ))
       )

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

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

       # extract fbank features
       UpperCAmelCase__							 =						[self._extract_fbank_features(lowerCamelCase      ) for waveform in raw_speech]

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

       UpperCAmelCase__							 =						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__							 =						padded_inputs.get("input_features"      )
       if isinstance(input_features[0] ,      lowerCamelCase      ):
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ) for feature in input_features]

       UpperCAmelCase__							 =						padded_inputs.get("attention_mask"      )
       if attention_mask is not None:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.intaa      ) for array in attention_mask]

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

       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





