





'''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()

lowerCAmelCase :					Union[str, Any] 							=2
class 				a_       :





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





        """simple docstring"""




        lowercase_      :List[Any]         =			bos, unk, pad, eos
        lowercase_      :str         =			[]
        lowercase_      :Any         =			[]
        lowercase_      :Dict         =			{}
        lowercase_      :Union[str, Any]         =			self.add_symbol(lowercase						)
        lowercase_      :str         =			self.add_symbol(lowercase						)
        lowercase_      :Tuple         =			self.add_symbol(lowercase						)
        lowercase_      :Any         =			self.add_symbol(lowercase						)
        if extra_special_symbols:
           for s in extra_special_symbols:
              self.add_symbol(lowercase						)
        lowercase_      :Tuple         =			len(self.symbols						)





     def __eq__(			self		: Union[str, Any]     ,				lowercase		: Any						):





        """simple docstring"""




        return self.indices == other.indices





     def __getitem__(			self		: Optional[Any]     ,				lowercase		: Optional[Any]						):





        """simple docstring"""




        if idx < len(self.symbols						):
           return self.symbols[idx]
        return self.unk_word





     def __len__(			self		: List[str]						):





        """simple docstring"""




        return len(self.symbols						)





     def __contains__(			self		: List[str]     ,				lowercase		: str						):





        """simple docstring"""




        return sym in self.indices





     @classmethod
     def 							lowercase__				(			cls		: int     ,				lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			cls()
        d.add_from_file(lowercase						)
        return d





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: List[Any]     ,				lowercase		: Dict=1     ,				lowercase		: Dict=False						):





        """simple docstring"""




        if word in self.indices and not overwrite:
           lowercase_      :Union[str, Any]         =			self.indices[word]
           lowercase_      :Dict         =			self.count[idx] + n
           return idx
        else:
           lowercase_      :Optional[Any]         =			len(self.symbols						)
           lowercase_      :Optional[int]         =			idx
           self.symbols.append(lowercase						)
           self.count.append(lowercase						)
           return idx





     def 							lowercase__				(			self		: str     ,				lowercase		: Union[str, Any]						):





        """simple docstring"""




        return 0







     def 							lowercase__				(			self		: Any     ,				lowercase		: List[str]						):





        """simple docstring"""




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

        lowercase_      :Optional[Any]         =			f.readlines()
        lowercase_      :str         =			self._load_meta(lowercase						)

        for line in lines[indices_start_line:]:
           try:
              lowercase_      :Dict         =			line.rstrip().rsplit(" "     ,				1						)
              if field == "#fairseq:overwrite":
                 lowercase_      :Any         =			True
                 lowercase_      :Dict         =			line.rsplit(" "     ,				1						)
              else:
                 lowercase_      :str         =			False
              lowercase_      :Optional[Any]         =			int(lowercase						)
              lowercase_      :Optional[Any]         =			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(lowercase						)						)
              self.add_symbol(lowercase     ,				n=lowercase     ,				overwrite=lowercase						)
           except ValueError:
              raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)







def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
   # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
   lowercase_      :Any         =			dict((re.sub(r"@@$"     ,""     ,__lowerCamelCase			), v) if k.endswith("@@"			) else (re.sub(r"$"     ,"</w>"     ,__lowerCamelCase			), v) for k, v in d.items()			)
   lowercase_      :Dict         =			"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'{k}</w>']
      lowercase_      :List[Any]         =			d[k]  # restore
   return da
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   # prep
   if not os.path.exists(__lowerCamelCase			):
      raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'			)
   os.makedirs(__lowerCamelCase     ,exist_ok=__lowerCamelCase			)
   print(F'Writing results to {pytorch_dump_folder_path}'			)

   # handle various types of models

   lowercase_      :List[Any]         =			os.path.join(__lowerCamelCase     ,"checkpoint.pt"			)
   if not os.path.isfile(__lowerCamelCase			):
      raise ValueError(F'path to the file {checkpoint_file} does not exist!'			)
   lowercase_      :Union[str, Any]         =			torch.load(__lowerCamelCase     ,map_location="cpu"			)

   lowercase_      :Optional[int]         =			chkpt["cfg"]["model"]

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

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

   lowercase_      :Optional[int]         =			os.path.join(__lowerCamelCase     ,VOCAB_FILES_NAMES["merges_file"]			)
   shutil.copyfile(__lowerCamelCase     ,__lowerCamelCase			)

   # model config
   lowercase_      :List[str]         =			os.path.join(__lowerCamelCase     ,"config.json"			)

   lowercase_      :int         =			{
       "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-12,
       "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(__lowerCamelCase     ,"w"     ,encoding="utf-8"			) as f:
      f.write(json.dumps(__lowerCamelCase     ,ensure_ascii=__lowerCamelCase     ,indent=__lowerCamelCase			)			)

   # tokenizer config
   lowercase_      :List[Any]         =			os.path.join(__lowerCamelCase     ,__lowerCamelCase			)

   lowercase_      :List[Any]         =			{
       "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(__lowerCamelCase     ,"w"     ,encoding="utf-8"			) as f:
      f.write(json.dumps(__lowerCamelCase     ,ensure_ascii=__lowerCamelCase     ,indent=__lowerCamelCase			)			)

   # model
   lowercase_      :List[str]         =			chkpt["model"]

   # remove unneeded keys
   lowercase_      :Optional[int]         =			[
       "decoder.version",
   ]
   for k in ignore_keys:
      model_state_dict.pop(__lowerCamelCase     ,__lowerCamelCase			)

   lowercase_      :Tuple         =			list(model_state_dict.keys()			)
   for layer_name in layer_names:
      if layer_name.endswith("output_projection.weight"			):
         lowercase_      :str         =			model_state_dict.pop(__lowerCamelCase			)
      else:
         lowercase_      :Optional[int]         =			model_state_dict.pop(__lowerCamelCase			)

   lowercase_      :Any         =			BioGptConfig.from_pretrained(__lowerCamelCase			)
   lowercase_      :Optional[int]         =			BioGptForCausalLM(__lowerCamelCase			)

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

   # save
   lowercase_      :Union[str, Any]         =			os.path.join(__lowerCamelCase     ,__lowerCamelCase			)
   print(F'Generating {pytorch_weights_dump_path}'			)
   torch.save(__lowerCamelCase     ,__lowerCamelCase			)

   print("Conversion is done!"			)


if __name__ == "__main__":
    lowerCAmelCase :					str 							=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.'''
    )
    lowerCAmelCase :					Any 							=parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Optional[int] 							={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Optional[Any] 							=[
     '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''WavLMForAudioFrameClassification''',
     '''WavLMForCTC''',
     '''WavLMForSequenceClassification''',
     '''WavLMForXVector''',
     '''WavLMModel''',
     '''WavLMPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Union[str, Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


