



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowercase_		       =  {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowercase_		       =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {}
     with open(__A  ,	'''r''') as file:
          for line_number, line in enumerate(__A):
               _a      =  line.strip()
               if line:
                    _a      =  line.split()
                    _a      =  line_number
                    _a      =  words[0]
                    _a      =  value
     return result




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     for attribute in key.split('''.'''):
          _a      =  getattr(__A  ,	__A)

     _a      =  None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(__A):
               _a      =  PARAM_MAPPING[full_name.split('''.''')[-1]]
               _a      =  '''param'''

     if weight_type is not None and weight_type != "param":
          _a      =  getattr(__A  ,	__A).shape
     elif weight_type is not None and weight_type == "param":
          _a      =  hf_pointer
          for attribute in hf_param_name.split('''.'''):
               _a      =  getattr(__A  ,	__A)
          _a      =  shape_pointer.shape

          # let's reduce dimension
          _a      =  value[0]
     else:
          _a      =  hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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      =  value
     elif weight_type == "weight_g":
          _a      =  value
     elif weight_type == "weight_v":
          _a      =  value
     elif weight_type == "bias":
          _a      =  value
     elif weight_type == "param":
          for attribute in hf_param_name.split('''.'''):
               _a      =  getattr(__A  ,	__A)
          _a      =  value
     else:
          _a      =  value

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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(__A):
               _a      =  PARAM_MAPPING[full_name.split('''.''')[-1]]
               _a      =  '''param'''

     if weight_type is not None and weight_type != "param":
          _a      =  '''.'''.join([key, weight_type])
     elif weight_type is not None and weight_type == "param":
          _a      =  '''.'''.join([key, hf_param_name])
     else:
          _a      =  key

     _a      =  value if '''lm_head''' in full_key else value[0]


lowercase_		       =  {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}




def lowerCAmelCase  (__A  ,	__A  ,	__A=None  ,	__A=None):



     """simple docstring"""



     _a      =  False
     for key, mapped_key in MAPPING.items():
          _a      =  '''wav2vec2.''' + 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      =  True
               if "*" in mapped_key:
                    _a      =  name.split(__A)[0].split('''.''')[-2]
                    _a      =  mapped_key.replace('''*'''  ,	__A)
               if "weight_g" in name:
                    _a      =  '''weight_g'''
               elif "weight_v" in name:
                    _a      =  '''weight_v'''
               elif "bias" in name:
                    _a      =  '''bias'''
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    _a      =  '''weight'''
               else:
                    _a      =  None
               if hf_dict is not None:
                    rename_dict(__A  ,	__A  ,	__A  ,	__A  ,	__A)
               else:
                    set_recursively(__A  ,	__A  ,	__A  ,	__A  ,	__A)
               return is_used
     return is_used




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  []
     _a      =  fairseq_model.state_dict()

     _a      =  hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =  False
          if "conv_layers" in name:
               load_conv_layer(
                   __A  ,	__A  ,	__A  ,	__A  ,	hf_model.config.feat_extract_norm == '''group'''  ,	)
               _a      =  True
          else:
               _a      =  load_wavaveca_layer(__A  ,	__A  ,	__A)
          if not is_used:
               unused_weights.append(__A)

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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  full_name.split('''conv_layers.''')[-1]
     _a      =  name.split('''.''')
     _a      =  int(items[0])
     _a      =  int(items[1])

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''')
               _a      =  value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''')
               _a      =  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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''')
               _a      =  value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''')
               _a      =  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 lowerCAmelCase  (__A  ,	__A  ,	__A=None  ,	__A=None  ,	__A=True  ,	__A=False):



     """simple docstring"""



     if config_path is not None:
          _a      =  WavaVecaConfig.from_pretrained(__A)
     else:
          _a      =  WavaVecaConfig()

     if is_seq_class:
          _a      =  read_txt_into_dict(__A)
          _a      =  idalabel
          _a      =  WavaVecaForSequenceClassification(__A)
          _a      =  WavaVecaFeatureExtractor(
              feature_size=1  ,	sampling_rate=16_000  ,	padding_value=0  ,	do_normalize=__A  ,	return_attention_mask=__A  ,	)
          feature_extractor.save_pretrained(__A)

     elif is_finetuned:
          if dict_path:
               _a      =  Dictionary.load(__A)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _a      =  target_dict.pad_index
               _a      =  target_dict.bos_index
               _a      =  target_dict.eos_index
               _a      =  len(target_dict.symbols)
               _a      =  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      =  target_dict.indices

               # fairseq has the <pad> and <s> switched
               _a      =  0
               _a      =  1
               with open(__A  ,	'''w'''  ,	encoding='''utf-8''') as vocab_handle:
                    json.dump(__A  ,	__A)
               _a      =  WavaVecaCTCTokenizer(
                   __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      =  True if config.feat_extract_norm == '''layer''' else False
               _a      =  WavaVecaFeatureExtractor(
                   feature_size=1  ,	sampling_rate=16_000  ,	padding_value=0  ,	do_normalize=__A  ,	return_attention_mask=__A  ,	)
               _a      =  WavaVecaProcessor(feature_extractor=__A  ,	tokenizer=__A)
               processor.save_pretrained(__A)

          _a      =  WavaVecaForCTC(__A)
     else:
          _a      =  WavaVecaForPreTraining(__A)

     if is_finetuned or is_seq_class:
          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1])})
     else:
          _a      =  argparse.Namespace(task='''audio_pretraining''')
          _a      =  fairseq.tasks.setup_task(__A)

          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,	task=__A)

     _a      =  model[0].eval()

     recursively_load_weights(__A  ,	__A  ,	not is_finetuned)

     hf_wavavec.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_		       =  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"
  )
  parser.add_argument(
      "--is_seq_class",
      action="store_true",
      help="Whether the model to convert is a fine-tuned sequence classification model or not",
  )
  lowercase_		       =  parser.parse_args()

  lowercase_		       =  not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )








'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       'MCTCTFeatureExtractor'
 __lowerCamelCase    :       Optional[Any]          =       'AutoTokenizer'



 def __init__(self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      super().__init__(A ,   A       )
      _a      =  self.feature_extractor
      _a      =  False



 def __call__(self ,   *A ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor(*A ,   **A       )

      if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
           _a      =  kwargs.pop('''raw_speech'''       )
      else:
           _a      =  kwargs.pop('''audio''' ,   A       )
      _a      =  kwargs.pop('''sampling_rate''' ,   A       )
      _a      =  kwargs.pop('''text''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

      if audio is not None:
           _a      =  self.feature_extractor(A ,   *A ,   sampling_rate=A ,   **A       )
      if text is not None:
           _a      =  self.tokenizer(A ,   **A       )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _a      =  encodings['''input_ids''']
           return inputs



 def 					a__  (self ,   *A ,   **A       )    ->     int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor.pad(*A ,   **A       )

      _a      =  kwargs.pop('''input_features''' ,   A       )
      _a      =  kwargs.pop('''labels''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if input_features is not None:
           _a      =  self.feature_extractor.pad(A ,   *A ,   **A       )
      if labels is not None:
           _a      =  self.tokenizer.pad(A ,   **A       )

      if labels is None:
           return input_features
      elif input_features is None:
           return labels
      else:
           _a      =  labels['''input_ids''']
           return input_features



 def 					a__  (self ,   *A ,   **A       )    ->     List[str]:


      """simple docstring"""



      return self.tokenizer.decode(*A ,   **A       )



 @contextmanager
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      warnings.warn(
          '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
          '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
          '''your audio inputs, or in a separate call.'''       )
      _a      =  True
      _a      =  self.tokenizer
      yield
      _a      =  self.feature_extractor
      _a      =  False



