

'''simple docstring'''



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase					:			Dict       =	2







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			List[Any]   ,				*,  # begin keyword-only arguments
     __SCREAMING_SNAKE_CASE   :			List[str]="<s>"   ,				__SCREAMING_SNAKE_CASE   :			str="<pad>"   ,				__SCREAMING_SNAKE_CASE   :			List[str]="</s>"   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]="<unk>"   ,				__SCREAMING_SNAKE_CASE   :			Dict=None   ,				)    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = bos, unk, pad, eos
      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = {}
      __SCREAMING_SNAKE_CASE      = self.add_symbol(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = self.add_symbol(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = self.add_symbol(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = self.add_symbol(__SCREAMING_SNAKE_CASE     )
      if extra_special_symbols:
           for s in extra_special_symbols:
                self.add_symbol(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = len(self.symbols     )
 def __eq__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      return self.indices == other.indices
 def __getitem__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Any:


      """simple docstring"""




      if idx < len(self.symbols     ):
           return self.symbols[idx]
      return self.unk_word
 def __len__(						self   :			Optional[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      return len(self.symbols     )
 def __contains__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					Any:


      """simple docstring"""




      return sym in self.indices
 @classmethod
 def  UpperCAmelCase__						(						cls   :			str   ,				__SCREAMING_SNAKE_CASE   :			str     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = cls()
      d.add_from_file(__SCREAMING_SNAKE_CASE     )
      return d
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=False     )    ->					List[str]:


      """simple docstring"""




      if word in self.indices and not overwrite:
           __SCREAMING_SNAKE_CASE      = self.indices[word]
           __SCREAMING_SNAKE_CASE      = self.count[idx] + n
           return idx
      else:
           __SCREAMING_SNAKE_CASE      = len(self.symbols     )
           __SCREAMING_SNAKE_CASE      = idx
           self.symbols.append(__SCREAMING_SNAKE_CASE     )
           self.count.append(__SCREAMING_SNAKE_CASE     )
           return idx
 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					List[str]:


      """simple docstring"""




      return 0


 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					Optional[int]:


      """simple docstring"""




      if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           try:
                with open(__SCREAMING_SNAKE_CASE   ,				"""r"""   ,				encoding="""utf-8"""     ) as fd:
                     self.add_from_file(__SCREAMING_SNAKE_CASE     )
           except FileNotFoundError as fnfe:
                raise fnfe
           except UnicodeError:
                raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(__SCREAMING_SNAKE_CASE     )     )
           return

      __SCREAMING_SNAKE_CASE      = f.readlines()
      __SCREAMING_SNAKE_CASE      = self._load_meta(__SCREAMING_SNAKE_CASE     )

      for line in lines[indices_start_line:]:
           try:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = line.rstrip().rsplit(""" """   ,				1     )
                if field == "#fairseq:overwrite":
                     __SCREAMING_SNAKE_CASE      = True
                     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = line.rsplit(""" """   ,				1     )
                else:
                     __SCREAMING_SNAKE_CASE      = False
                __SCREAMING_SNAKE_CASE      = int(__SCREAMING_SNAKE_CASE     )
                __SCREAMING_SNAKE_CASE      = line
                if word in self and not overwrite:
                     raise RuntimeError(
                         """Duplicate word found when loading Dictionary: '{}'. """
                         """Duplicate words can overwrite earlier ones by adding the """
                         """#fairseq:overwrite flag at the end of the corresponding row """
                         """in the dictionary file. If using the Camembert model, please """
                         """download an updated copy of the model file.""".format(__SCREAMING_SNAKE_CASE     )     )
                self.add_symbol(__SCREAMING_SNAKE_CASE   ,				n=__SCREAMING_SNAKE_CASE   ,				overwrite=__SCREAMING_SNAKE_CASE     )
           except ValueError:
                raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""     )







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = dict((re.sub(R"""@@$""" ,       """""" ,       a__		), v) if k.endswith("""@@"""		) else (re.sub(R"""$""" ,       """</w>""" ,       a__		), v) for k, v in d.items()		)
     __SCREAMING_SNAKE_CASE      = """<s> <pad> </s> <unk>""".split()
     # restore the special tokens
     for k in keep_keys:
          del da[F'{k}</w>']
          __SCREAMING_SNAKE_CASE      = d[k]  # restore
     return da







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     if not os.path.exists(a__		):
          raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'		)
     os.makedirs(a__ ,       exist_ok=a__		)
     print(F'Writing results to {pytorch_dump_folder_path}'		)

     # handle various types of models

     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       """checkpoint.pt"""		)
     if not os.path.isfile(a__		):
          raise ValueError(F'path to the file {checkpoint_file} does not exist!'		)
     __SCREAMING_SNAKE_CASE      = torch.load(a__ ,       map_location="""cpu"""		)

     __SCREAMING_SNAKE_CASE      = chkpt["""cfg"""]["""model"""]

     # dicts
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       """dict.txt"""		)
     if not os.path.isfile(a__		):
          raise ValueError(F'path to the file {dict_file} does not exist!'		)
     __SCREAMING_SNAKE_CASE      = Dictionary.load(a__		)
     __SCREAMING_SNAKE_CASE      = rewrite_dict_keys(src_dict.indices		)
     __SCREAMING_SNAKE_CASE      = len(a__		)
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       VOCAB_FILES_NAMES["""vocab_file"""]		)
     print(F'Generating {src_vocab_file} of {src_vocab_size} records'		)
     with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as f:
          f.write(json.dumps(a__ ,       ensure_ascii=a__ ,       indent=a__		)		)

     # merges_file (bpecodes)
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       """bpecodes"""		)
     if not os.path.isfile(a__		):
          raise ValueError(F'path to the file {bpecodes_file} does not exist!'		)

     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       VOCAB_FILES_NAMES["""merges_file"""]		)
     shutil.copyfile(a__ ,       a__		)

     # model config
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       """config.json"""		)

     __SCREAMING_SNAKE_CASE      = {
         """activation_dropout""": args["""activation_dropout"""],
         """architectures""": ["""BioGptForCausalLM"""],
         """attention_probs_dropout_prob""": args["""attention_dropout"""],
         """bos_token_id""": 0,
         """eos_token_id""": 2,
         """hidden_act""": args["""activation_fn"""],
         """hidden_dropout_prob""": args["""dropout"""],
         """hidden_size""": args["""decoder_embed_dim"""],
         """initializer_range""": 0.02,
         """intermediate_size""": args["""decoder_ffn_embed_dim"""],
         """layer_norm_eps""": 1E-1_2,
         """layerdrop""": args["""decoder_layerdrop"""],
         """max_position_embeddings""": args["""max_target_positions"""],
         """model_type""": """biogpt""",
         """num_attention_heads""": args["""decoder_attention_heads"""],
         """num_hidden_layers""": args["""decoder_layers"""],
         """pad_token_id""": 1,
         """scale_embedding""": not args["""no_scale_embedding"""],
         """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
         """vocab_size""": src_vocab_size,
     }

     # good hparam defaults to start with

     print(F'Generating {biogpt_model_config_file}'		)
     with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as f:
          f.write(json.dumps(a__ ,       ensure_ascii=a__ ,       indent=a__		)		)

     # tokenizer config
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = {
         """bos_token""": """<s>""",
         """eos_token""": """</s>""",
         """model_max_length""": 10_24,
         """pad_token""": """<pad>""",
         """special_tokens_map_file""": None,
         """tokenizer_class""": """BioGptTokenizer""",
         """unk_token""": """<unk>""",
     }

     print(F'Generating {biogpt_tokenizer_config_file}'		)
     with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as f:
          f.write(json.dumps(a__ ,       ensure_ascii=a__ ,       indent=a__		)		)

     # model
     __SCREAMING_SNAKE_CASE      = chkpt["""model"""]

     # remove unneeded keys
     __SCREAMING_SNAKE_CASE      = [
         """decoder.version""",
     ]
     for k in ignore_keys:
          model_state_dict.pop(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = list(model_state_dict.keys()		)
     for layer_name in layer_names:
          if layer_name.endswith("""output_projection.weight"""		):
               __SCREAMING_SNAKE_CASE      = model_state_dict.pop(a__		)
          else:
               __SCREAMING_SNAKE_CASE      = model_state_dict.pop(a__		)

     __SCREAMING_SNAKE_CASE      = BioGptConfig.from_pretrained(a__		)
     __SCREAMING_SNAKE_CASE      = BioGptForCausalLM(a__		)

     # check that it loads ok
     model_new.load_state_dict(a__		)

     # save
     __SCREAMING_SNAKE_CASE      = os.path.join(a__ ,       a__		)
     print(F'Generating {pytorch_weights_dump_path}'		)
     torch.save(a__ ,       a__		)

     print("""Conversion is done!"""		)


if __name__ == "__main__":
  UpperCAmelCase					:			List[Any]       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--biogpt_checkpoint_path',
      default=None,
      type=str,
      required=True,
      help=(
          'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
          ' bpecodes, etc.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  UpperCAmelCase					:			int       =	parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









'''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()
UpperCAmelCase					:			List[Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    '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',
}
UpperCAmelCase					:			Any       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def   a__			(    a__		):




     """simple docstring"""


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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     for attribute in key.split("""."""		):
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

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

          # let's reduce dimension
          __SCREAMING_SNAKE_CASE      = value[0]
     else:
          __SCREAMING_SNAKE_CASE      = 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":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_g":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_v":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "bias":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "param":
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = value
     else:
          __SCREAMING_SNAKE_CASE      = value

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

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

     __SCREAMING_SNAKE_CASE      = value if """lm_head""" in full_key else value[0]


UpperCAmelCase					:			Any       =	{
    '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   a__			(    a__ ,       a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = False
     for key, mapped_key in MAPPING.items():
          __SCREAMING_SNAKE_CASE      = """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]:
               __SCREAMING_SNAKE_CASE      = True
               if "*" in mapped_key:
                    __SCREAMING_SNAKE_CASE      = name.split(a__		)[0].split("""."""		)[-2]
                    __SCREAMING_SNAKE_CASE      = mapped_key.replace("""*""" ,       a__		)
               if "weight_g" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_g"""
               elif "weight_v" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_v"""
               elif "bias" in name:
                    __SCREAMING_SNAKE_CASE      = """bias"""
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __SCREAMING_SNAKE_CASE      = """weight"""
               else:
                    __SCREAMING_SNAKE_CASE      = 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   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = fairseq_model.state_dict()

     __SCREAMING_SNAKE_CASE      = hf_model.wavaveca.feature_extractor

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

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = full_name.split("""conv_layers."""		)[-1]
     __SCREAMING_SNAKE_CASE      = name.split("""."""		)
     __SCREAMING_SNAKE_CASE      = int(items[0]		)
     __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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   a__			(    a__ ,       a__ ,       a__=None ,       a__=None ,       a__=True ,       a__=False		):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig.from_pretrained(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig()

     if is_seq_class:
          __SCREAMING_SNAKE_CASE      = read_txt_into_dict(a__		)
          __SCREAMING_SNAKE_CASE      = idalabel
          __SCREAMING_SNAKE_CASE      = WavaVecaForSequenceClassification(a__		)
          __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
              feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
          feature_extractor.save_pretrained(a__		)

     elif is_finetuned:
          if dict_path:
               __SCREAMING_SNAKE_CASE      = Dictionary.load(a__		)

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

               # fairseq has the <pad> and <s> switched
               __SCREAMING_SNAKE_CASE      = 0
               __SCREAMING_SNAKE_CASE      = 1
               with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as vocab_handle:
                    json.dump(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = 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__ ,       )
               __SCREAMING_SNAKE_CASE      = True if config.feat_extract_norm == """layer""" else False
               __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
                   feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
               __SCREAMING_SNAKE_CASE      = WavaVecaProcessor(feature_extractor=a__ ,       tokenizer=a__		)
               processor.save_pretrained(a__		)

          __SCREAMING_SNAKE_CASE      = WavaVecaForCTC(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaForPreTraining(a__		)

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

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,       task=a__		)

     __SCREAMING_SNAKE_CASE      = model[0].eval()

     recursively_load_weights(a__ ,       a__ ,       not is_finetuned		)

     hf_wavavec.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			int       =	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',
  )
  UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()

  UpperCAmelCase					:			int       =	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,
  )






