





"""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()

__A											=		2






class 					_lowerCAmelCase   :
       """simple docstring"""




       def __init__(    self    , *,  # begin keyword-only arguments
           __UpperCAmelCase="<s>"    , __UpperCAmelCase="<pad>"    , __UpperCAmelCase="</s>"    , __UpperCAmelCase="<unk>"    , __UpperCAmelCase=None    , ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							bos, unk, pad, eos
         lowerCAmelCase__			:int											=							[]
         lowerCAmelCase__			:Any											=							[]
         lowerCAmelCase__			:Dict											=							{}
         lowerCAmelCase__			:Tuple											=							self.add_symbol(__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							self.add_symbol(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							self.add_symbol(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							self.add_symbol(__UpperCAmelCase      )
         if extra_special_symbols:
           for s in extra_special_symbols:
             self.add_symbol(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							len(self.symbols      )






       def __eq__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return self.indices == other.indices






       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def __len__(    self      ):



         '''simple docstring'''

         return len(self.symbols      )






       def __contains__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return sym in self.indices






       @classmethod
       def      snake_case						(    cls    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							cls()
         d.add_from_file(__UpperCAmelCase      )
         return d






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=1    , __UpperCAmelCase=False      ):



         '''simple docstring'''

         if word in self.indices and not overwrite:
           lowerCAmelCase__			:List[Any]											=							self.indices[word]
           lowerCAmelCase__			:str											=							self.count[idx] + n
           return idx
         else:
           lowerCAmelCase__			:List[str]											=							len(self.symbols      )
           lowerCAmelCase__			:Any											=							idx
           self.symbols.append(__UpperCAmelCase      )
           self.count.append(__UpperCAmelCase      )
           return idx






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return 0






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Union[str, Any]											=							f.readlines()
         lowerCAmelCase__			:Dict											=							self._load_meta(__UpperCAmelCase      )

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






def  __A   (_SCREAMING_SNAKE_CASE			)					->Tuple:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							dict((re.sub(r'@@$'     ,   ''     ,   _SCREAMING_SNAKE_CASE			), v) if k.endswith('@@'			) else (re.sub(r'$'     ,   '</w>'     ,   _SCREAMING_SNAKE_CASE			), v) for k, v in d.items()			)
  lowerCAmelCase__			:List[Any]											=							'<s> <pad> </s> <unk>'.split()
  # restore the special tokens
  for k in keep_keys:
    del da[F"{k}</w>"]
    lowerCAmelCase__			:List[Any]											=							d[k]  # restore
  return da
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


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

  # handle various types of models

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

  lowerCAmelCase__			:Any											=							chkpt['cfg']['model']

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

  # merges_file (bpecodes)
  lowerCAmelCase__			:List[str]											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   'bpecodes'			)
  if not os.path.isfile(_SCREAMING_SNAKE_CASE			):
    raise ValueError(F"path to the file {bpecodes_file} does not exist!"			)

  lowerCAmelCase__			:List[Any]											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   VOCAB_FILES_NAMES['merges_file']			)
  shutil.copyfile(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # model config
  lowerCAmelCase__			:Any											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)

  lowerCAmelCase__			:Optional[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': 0.0_2,
      '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(_SCREAMING_SNAKE_CASE     ,   'w'     ,   encoding='utf-8'			) as f:
    f.write(json.dumps(_SCREAMING_SNAKE_CASE     ,   ensure_ascii=_SCREAMING_SNAKE_CASE     ,   indent=_SCREAMING_SNAKE_CASE			)			)

  # tokenizer config
  lowerCAmelCase__			:Optional[Any]											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:str											=							{
      'bos_token': '<s>',
      'eos_token': '</s>',
      'model_max_length': 1024,
      'pad_token': '<pad>',
      'special_tokens_map_file': None,
      'tokenizer_class': 'BioGptTokenizer',
      'unk_token': '<unk>',
  }

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

  # model
  lowerCAmelCase__			:str											=							chkpt['model']

  # remove unneeded keys
  lowerCAmelCase__			:List[Any]											=							[
      'decoder.version',
  ]
  for k in ignore_keys:
    model_state_dict.pop(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:List[Any]											=							list(model_state_dict.keys()			)
  for layer_name in layer_names:
    if layer_name.endswith('output_projection.weight'			):
      lowerCAmelCase__			:List[Any]											=							model_state_dict.pop(_SCREAMING_SNAKE_CASE			)
    else:
      lowerCAmelCase__			:Any											=							model_state_dict.pop(_SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:Tuple											=							BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:int											=							BioGptForCausalLM(_SCREAMING_SNAKE_CASE			)

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

  # save
  lowerCAmelCase__			:Any											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  print(F"Generating {pytorch_weights_dump_path}"			)
  torch.save(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  print('Conversion is done!'			)


if __name__ == "__main__":
  __A											=		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."""
  )
  __A											=		parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""





from __future__ import annotations

__A											=		1.6_021e-19  # units = C
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->tuple[str, float]:





  """simple docstring"""


  if (conductivity, electron_conc, mobility).count(0			) != 1:
    raise ValueError('You cannot supply more or less than 2 values'			)
  elif conductivity < 0:
    raise ValueError('Conductivity cannot be negative'			)
  elif electron_conc < 0:
    raise ValueError('Electron concentration cannot be negative'			)
  elif mobility < 0:
    raise ValueError('mobility cannot be negative'			)
  elif conductivity == 0:
    return (
        "conductivity",
        mobility * electron_conc * ELECTRON_CHARGE,
    )
  elif electron_conc == 0:
    return (
        "electron_conc",
        conductivity / (mobility * ELECTRON_CHARGE),
    )
  else:
    return (
        "mobility",
        conductivity / (electron_conc * ELECTRON_CHARGE),
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
