






"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase    :   Optional[int]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   Dict   			=						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
_UpperCamelCase    :   Dict   			=						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def       snake_case  (A_				:str     ,      A_				:Tuple     ,      A_				:Optional[Any]     ,      A_				:int     ,      A_				:Optional[int]     ,      A_				:Dict				):







  '''simple docstring'''

  for attribute in key.split('.'				):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        a					:      int    =       'lm_head'

    a					:      Union[str, Any]    =       getattr(A_     ,      A_				)

  if weight_type is not None:
    a					:      Any    =       getattr(A_     ,      A_				).shape
  else:
    a					:      str    =       hf_pointer.shape

  assert hf_shape == value.shape, (
      f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
      f''' {value.shape} for {full_name}'''
  )

  if weight_type == "weight":
    a					:      int    =       value
  elif weight_type == "weight_g":
    a					:      List[str]    =       value
  elif weight_type == "weight_v":
    a					:      List[Any]    =       value
  elif weight_type == "bias":
    a					:      List[str]    =       value
  else:
    a					:      Optional[int]    =       value

  logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)




def       snake_case  (A_				:str     ,      A_				:List[str]     ,      A_				:Optional[int]				):







  '''simple docstring'''

  a					:      Any    =       []
  a					:      str    =       fairseq_model.state_dict()

  a					:      Optional[int]    =       hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    a					:      Tuple    =       False
    if "conv_layers" in name:
      load_conv_layer(
          A_     ,      A_     ,      A_     ,      A_     ,      hf_model.config.feat_extract_norm == 'group'     ,      )
      a					:      Optional[int]    =       True
    else:
      for key, mapped_key in MAPPING.items():
        a					:      int    =       'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
          a					:      Optional[Any]    =       True
          if "*" in mapped_key:
            a					:      Optional[int]    =       name.split(A_				)[0].split('.'				)[-2]
            a					:      List[str]    =       mapped_key.replace('*'     ,      A_				)
          if "weight_g" in name:
            a					:      Any    =       'weight_g'
          elif "weight_v" in name:
            a					:      List[Any]    =       'weight_v'
          elif "bias" in name:
            a					:      str    =       'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a					:      Dict    =       'weight'
          else:
            a					:      Optional[Any]    =       None
          set_recursively(A_     ,      A_     ,      A_     ,      A_     ,      A_     ,      A_				)
        continue
    if not is_used:
      unused_weights.append(A_				)

  logger.warning(f'''Unused weights: {unused_weights}'''				)




def       snake_case  (A_				:Union[str, Any]     ,      A_				:Optional[Any]     ,      A_				:List[str]     ,      A_				:List[Any]     ,      A_				:Any				):







  '''simple docstring'''

  a					:      Any    =       full_name.split('conv_layers.'				)[-1]
  a					:      Tuple    =       name.split('.'				)
  a					:      Optional[Any]    =       int(items[0]				)
  a					:      Optional[int]    =       int(items[1]				)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
      )
      a					:      Optional[int]    =       value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
      )
      a					:      Optional[int]    =       value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
          " found."
      )
      a					:      Optional[Any]    =       value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
      )
      a					:      Optional[int]    =       value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
  else:
    unused_weights.append(A_				)




@torch.no_grad()
def       snake_case  (A_				:List[Any]     ,      A_				:Optional[Any]     ,      A_				:str=None     ,      A_				:Any=None     ,      A_				:List[Any]=True				):







  '''simple docstring'''

  if config_path is not None:
    a					:      int    =       UniSpeechConfig.from_pretrained(A_				)
  else:
    a					:      int    =       UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      a					:      Optional[int]    =       Dictionary.load_from_json(A_				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a					:      List[Any]    =       target_dict.pad_index
      a					:      List[str]    =       target_dict.bos_index
      a					:      Optional[int]    =       target_dict.eos_index
      a					:      List[Any]    =       len(target_dict.symbols				)
      a					:      Union[str, Any]    =       os.path.join(A_     ,      'vocab.json'				)
      if not os.path.isdir(A_				):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A_				)				)
        return
      os.makedirs(A_     ,      exist_ok=A_				)
      a					:      Union[str, Any]    =       target_dict.indices

      # fairseq has the <pad> and <s> switched
      a					:      List[str]    =       4_2
      a					:      int    =       4_3
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as vocab_handle:
        json.dump(A_     ,      A_				)
      a					:      List[str]    =       WavaVecaPhonemeCTCTokenizer(
          A_     ,      unk_token=target_dict.unk_word     ,      pad_token=target_dict.pad_word     ,      bos_token=target_dict.bos_word     ,      eos_token=target_dict.eos_word     ,      word_delimiter_token='|'     ,      do_lower_case=A_     ,      )
      a					:      Optional[Any]    =       True if config.feat_extract_norm == 'layer' else False
      a					:      List[Any]    =       WavaVecaFeatureExtractor(
          feature_size=1     ,      sampling_rate=1_6_0_0_0     ,      padding_value=0     ,      do_normalize=A_     ,      return_attention_mask=A_     ,      )
      a					:      Dict    =       WavaVecaProcessor(feature_extractor=A_     ,      tokenizer=A_				)
      processor.save_pretrained(A_				)

    a					:      List[Any]    =       UniSpeechForCTC(A_				)
  else:
    a					:      int    =       UniSpeechForPreTraining(A_				)

  if is_finetuned:
    a,					a,					a					:      Union[str, Any]    =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     ,      arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				), 'w2v_path': checkpoint_path}				)
  else:
    a,					a,					a					:      Optional[int]    =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

  a					:      str    =       model[0].eval()

  recursively_load_weights(A_     ,      A_     ,      A_				)

  hf_unispeech.save_pretrained(A_				)


if __name__ == "__main__":
   _UpperCamelCase    :   Dict   			=						argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
   parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument(
       '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   _UpperCamelCase    :   Optional[int]   			=						parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )












"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_UpperCamelCase    :   int   			=						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					['''input_features''', '''attention_mask''']





    def __init__(							self     : Optional[int]    ,		A     : Optional[Any]=8_0    ,		A     : str=1_6_0_0_0    ,		A     : List[str]=0.0    ,		A     : Any=1_0    ,		A     : Union[str, Any]=2_5    ,		A     : str="hamming_window"    ,		A     : str=3_27_68.0    ,		A     : Union[str, Any]=0.97    ,		A     : Dict=1.0    ,		A     : Any=True    ,		A     : Union[str, Any]=True    ,		A     : List[Any]=False    ,		**A     : Tuple    ,		):


      '''simple docstring'''




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

      a					:      Any    =       feature_size
      a					:      List[Any]    =       sampling_rate
      a					:      Any    =       padding_value
      a					:      str    =       hop_length
      a					:      Any    =       win_length
      a					:      List[Any]    =       frame_signal_scale
      a					:      Tuple    =       preemphasis_coeff
      a					:      Dict    =       mel_floor
      a					:      Optional[int]    =       normalize_means
      a					:      List[str]    =       normalize_vars
      a					:      Dict    =       win_function
      a					:      Union[str, Any]    =       return_attention_mask

      a					:      List[Any]    =       win_length * sampling_rate // 1_0_0_0
      a					:      Tuple    =       hop_length * sampling_rate // 1_0_0_0

      a					:      List[Any]    =       optimal_fft_length(self.sample_size      )
      a					:      Any    =       (self.n_fft // 2) + 1





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : np.array      ):


      '''simple docstring'''




      if self.win_function == "hamming_window":
        a					:      List[str]    =       window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=A      )
      else:
        a					:      Dict    =       window_function(window_length=self.sample_size    ,		name=self.win_function      )

      a					:      str    =       mel_filter_bank(
          num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.feature_size    ,		min_frequency=0.0    ,		max_frequency=self.sampling_rate / 2.0    ,		sampling_rate=self.sampling_rate    ,		)

      a					:      List[Any]    =       spectrogram(
          one_waveform * self.frame_signal_scale    ,		window=A    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=A    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=A    ,		mel_floor=self.mel_floor    ,		log_mel='log'    ,		)
      return msfc_features.T





    def     lowerCamelCase__				(							self     : int    ,		A     : Tuple    ,		A     : int    ,		A     : Optional[int]      ):


      '''simple docstring'''




      if self.normalize_means:
        a					:      Any    =       x[:input_length].mean(axis=0      )
        a					:      Dict    =       np.subtract(A    ,		A      )
      if self.normalize_vars:
        a					:      Dict    =       x[:input_length].std(axis=0      )
        a					:      Dict    =       np.divide(A    ,		A      )

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

      # make sure array is in float32
      a					:      Optional[int]    =       x.astype(np.floataa      )

      return x





    def     lowerCamelCase__				(							self     : str    ,		A     : List[np.ndarray]    ,		A     : Optional[np.ndarray] = None      ):


      '''simple docstring'''




      a					:      str    =       attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [self._normalize_one(A    ,		A    ,		self.padding_value      ) for x, n in zip(A    ,		A      )]





    def __call__(							self     : Dict    ,		A     : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,		A     : Union[bool, str, PaddingStrategy] = False    ,		A     : Optional[int] = None    ,		A     : bool = False    ,		A     : Optional[int] = None    ,		A     : Optional[bool] = None    ,		A     : Optional[Union[str, TensorType]] = None    ,		A     : Optional[int] = None    ,		**A     : Union[str, Any]    ,		):


      '''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.'      )

      a					:      Optional[int]    =       isinstance(A    ,		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					:      Dict    =       is_batched_numpy or (
          isinstance(A    ,		(list, tuple)      ) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)      ))
      )

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

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

      # extract fbank features
      a					:      str    =       [self._extract_mfsc_features(A      ) for one_waveform in raw_speech]

      # convert into correct format for padding
      a					:      int    =       BatchFeature({'input_features': features}      )

      a					:      Union[str, Any]    =       self.pad(
          A    ,		padding=A    ,		max_length=A    ,		truncation=A    ,		pad_to_multiple_of=A    ,		return_attention_mask=A    ,		**A    ,		)
      # make sure list is in array format
      a					:      Optional[Any]    =       padded_inputs.get('input_features'      )
      if isinstance(input_features[0]    ,		A      ):
        a					:      List[str]    =       [np.asarray(A    ,		dtype=np.floataa      ) for feature in input_features]

      a					:      List[Any]    =       padded_inputs.get('attention_mask'      )
      if attention_mask is not None:
        a					:      int    =       [np.asarray(A    ,		dtype=np.intaa      ) for array in attention_mask]

      if self.normalize_means or self.normalize_vars:
        a					:      Any    =       (
            np.array(A    ,		dtype=np.intaa      )
            if self._get_padding_strategies(A    ,		max_length=A      ) is not PaddingStrategy.DO_NOT_PAD
            and padding
            else None
        )
        a					:      List[str]    =       self.normalize(
            padded_inputs['input_features']    ,		attention_mask=A      )

      if return_tensors is not None:
        a					:      Optional[int]    =       padded_inputs.convert_to_tensors(A      )

      return padded_inputs




