



"""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    :List[Any]     =					2
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	*,  # begin keyword-only arguments
         A="<s>"       ,	A="<pad>"       ,	A="</s>"       ,	A="<unk>"       ,	A=None       ,	)							->					List[Any]:
         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    bos, unk, pad, eos
         _UpperCAmelCase							:  Union[str, Any]											=    []
         _UpperCAmelCase							:  Tuple											=    []
         _UpperCAmelCase							:  int											=    {}
         _UpperCAmelCase							:  str											=    self.add_symbol(A		)
         _UpperCAmelCase							:  List[str]											=    self.add_symbol(A		)
         _UpperCAmelCase							:  List[str]											=    self.add_symbol(A		)
         _UpperCAmelCase							:  str											=    self.add_symbol(A		)
         if extra_special_symbols:
             for s in extra_special_symbols:
                 self.add_symbol(A		)
         _UpperCAmelCase							:  Dict											=    len(self.symbols		)





     def __eq__(					self       ,	A		)							->					Any:
         return self.indices == other.indices





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





     def __len__(					self		)							->					Optional[int]:
         return len(self.symbols		)





     def __contains__(					self       ,	A		)							->					List[Any]:
         return sym in self.indices





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A		)							->					Tuple:
         _UpperCAmelCase							:  Any											=    cls()
         d.add_from_file(A		)
         return d





     def  __lowerCAmelCase	(					self       ,	A       ,	A=1       ,	A=False		)							->					Dict:
         if word in self.indices and not overwrite:
             _UpperCAmelCase							:  Tuple											=    self.indices[word]
             _UpperCAmelCase							:  Tuple											=    self.count[idx] + n
             return idx
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    len(self.symbols		)
             _UpperCAmelCase							:  str											=    idx
             self.symbols.append(A		)
             self.count.append(A		)
             return idx





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:
         return 0





     def  __lowerCAmelCase	(					self       ,	A		)							->					Union[str, Any]:
         if isinstance(A       ,	A		):
             try:
                 with open(A       ,	'''r'''       ,	encoding='''utf-8'''		) as fd:
                     self.add_from_file(A		)
             except FileNotFoundError as fnfe:
                 raise fnfe
             except UnicodeError:
                 raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(A		)		)
             return

         _UpperCAmelCase							:  Optional[Any]											=    f.readlines()
         _UpperCAmelCase							:  Optional[Any]											=    self._load_meta(A		)

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





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





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		List[str]							):
    # prep
    if not os.path.exists(UpperCamelCase__							):
        raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    print(F'Writing results to {pytorch_dump_folder_path}'							)

    # handle various types of models

    _UpperCAmelCase							:  List[Any]											=    os.path.join(UpperCamelCase__     ,       '''checkpoint.pt'''							)
    if not os.path.isfile(UpperCamelCase__							):
        raise ValueError(F'path to the file {checkpoint_file} does not exist!'							)
    _UpperCAmelCase							:  Optional[int]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    _UpperCAmelCase							:  Optional[int]											=    chkpt['''cfg''']['''model''']

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

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

    _UpperCAmelCase							:  int											=    os.path.join(UpperCamelCase__     ,       VOCAB_FILES_NAMES['''merges_file''']							)
    shutil.copyfile(UpperCamelCase__     ,       UpperCamelCase__							)

    # model config
    _UpperCAmelCase							:  Dict											=    os.path.join(UpperCamelCase__     ,       '''config.json'''							)

    _UpperCAmelCase							:  Tuple											=    {
        '''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(UpperCamelCase__     ,       '''w'''     ,       encoding='''utf-8'''							) as f:
        f.write(json.dumps(UpperCamelCase__     ,       ensure_ascii=UpperCamelCase__     ,       indent=UpperCamelCase__							)							)

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

    _UpperCAmelCase							:  List[Any]											=    {
        '''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(UpperCamelCase__     ,       '''w'''     ,       encoding='''utf-8'''							) as f:
        f.write(json.dumps(UpperCamelCase__     ,       ensure_ascii=UpperCamelCase__     ,       indent=UpperCamelCase__							)							)

    # model
    _UpperCAmelCase							:  Union[str, Any]											=    chkpt['''model''']

    # remove unneeded keys
    _UpperCAmelCase							:  Optional[Any]											=    [
        '''decoder.version''',
    ]
    for k in ignore_keys:
        model_state_dict.pop(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Dict											=    list(model_state_dict.keys()							)
    for layer_name in layer_names:
        if layer_name.endswith('''output_projection.weight'''							):
            _UpperCAmelCase							:  Dict											=    model_state_dict.pop(UpperCamelCase__							)
        else:
            _UpperCAmelCase							:  Optional[Any]											=    model_state_dict.pop(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    BioGptConfig.from_pretrained(UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    BioGptForCausalLM(UpperCamelCase__							)

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

    # save
    _UpperCAmelCase							:  Tuple											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    print(F'Generating {pytorch_weights_dump_path}'							)
    torch.save(UpperCamelCase__     ,       UpperCamelCase__							)

    print('''Conversion is done!'''							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, 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.'
  )
  _lowerCAmelCase    :int     =					parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    if a < 0 or b < 0:
        raise ValueError('''the value of both inputs must be positive'''							)
    _UpperCAmelCase							:  List[str]											=    str(bin(UpperCamelCase__							)							)[2:]  # remove the leading "0b"
    _UpperCAmelCase							:  str											=    str(bin(UpperCamelCase__							)							)[2:]
    _UpperCAmelCase							:  List[str]											=    max(len(UpperCamelCase__							)     ,       len(UpperCamelCase__							)							)
    return "0b" + "".join(
        str(int('''1''' in (char_a, char_b)							)							)
        for char_a, char_b in zip(a_binary.zfill(UpperCamelCase__							)     ,       b_binary.zfill(UpperCamelCase__							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


