






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()

SCREAMING_SNAKE_CASE 							=	2
class 							A_    :






 '''simple docstring'''




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

     """simple docstring"""



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


 def __eq__(	self							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     return self.indices == other.indices


 def __getitem__(	self							,  _A) ->		List[Any]:

     """simple docstring"""



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


 def __len__(	self) ->		Optional[int]:

     """simple docstring"""



     return len(self.symbols)


 def __contains__(	self							,  _A) ->		Dict:

     """simple docstring"""



     return sym in self.indices


 @classmethod
 def  snake_case__				(	cls							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				cls()
     d.add_from_file(_A)
     return d


 def  snake_case__				(	self							,  _A							,  _A=1							,  _A=False) ->		Tuple:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



     return 0


 def  snake_case__				(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     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				:      List[str]			  =				f.readlines()
     _UpperCAmelCase				:      Tuple			  =				self._load_meta(_A)

     for line in lines[indices_start_line:]:
         try:
             _UpperCAmelCase				:      Union[str, Any]			  =				line.rstrip().rsplit(''' '''							,  1)
             if field == "#fairseq:overwrite":
                 _UpperCAmelCase				:      Optional[int]			  =				True
                 _UpperCAmelCase				:      Tuple			  =				line.rsplit(''' '''							,  1)
             else:
                 _UpperCAmelCase				:      Union[str, Any]			  =				False
             _UpperCAmelCase				:      Dict			  =				int(_A)
             _UpperCAmelCase				:      int			  =				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	(						__A    :							Union[str, Any] )      ->					Dict:
    # (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				:      List[Any]			  =				dict((re.sub(r'''@@$'''		,		''''''		,		__A ), v) if k.endswith('''@@''' ) else (re.sub(r'''$'''		,		'''</w>'''		,		__A ), v) for k, v in d.items() )
    _UpperCAmelCase				:      Tuple			  =				'''<s> <pad> </s> <unk>'''.split()
    # restore the special tokens
    for k in keep_keys:
        del da[f'''{k}</w>''']
        _UpperCAmelCase				:      List[Any]			  =				d[k]  # restore
    return da

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Tuple )      ->					Tuple:
    # prep
    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

    _UpperCAmelCase				:      str			  =				os.path.join(__A		,		'''checkpoint.pt''' )
    if not os.path.isfile(__A ):
        raise ValueError(f'''path to the file {checkpoint_file} does not exist!''' )
    _UpperCAmelCase				:      Tuple			  =				torch.load(__A		,		map_location='''cpu''' )

    _UpperCAmelCase				:      List[Any]			  =				chkpt['''cfg''']['''model''']

    # dicts
    _UpperCAmelCase				:      Optional[int]			  =				os.path.join(__A		,		'''dict.txt''' )
    if not os.path.isfile(__A ):
        raise ValueError(f'''path to the file {dict_file} does not exist!''' )
    _UpperCAmelCase				:      List[str]			  =				Dictionary.load(__A )
    _UpperCAmelCase				:      Tuple			  =				rewrite_dict_keys(src_dict.indices )
    _UpperCAmelCase				:      List[Any]			  =				len(__A )
    _UpperCAmelCase				:      Union[str, Any]			  =				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)
    _UpperCAmelCase				:      List[Any]			  =				os.path.join(__A		,		'''bpecodes''' )
    if not os.path.isfile(__A ):
        raise ValueError(f'''path to the file {bpecodes_file} does not exist!''' )

    _UpperCAmelCase				:      Optional[Any]			  =				os.path.join(__A		,		VOCAB_FILES_NAMES['''merges_file'''] )
    shutil.copyfile(__A		,		__A )

    # model config
    _UpperCAmelCase				:      Dict			  =				os.path.join(__A		,		'''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-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(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
        f.write(json.dumps(__A		,		ensure_ascii=__A		,		indent=__A ) )

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

    _UpperCAmelCase				:      int			  =				{
        '''bos_token''': '''<s>''',
        '''eos_token''': '''</s>''',
        '''model_max_length''': 1_024,
        '''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
    _UpperCAmelCase				:      int			  =				chkpt['''model''']

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

    _UpperCAmelCase				:      Union[str, Any]			  =				list(model_state_dict.keys() )
    for layer_name in layer_names:
        if layer_name.endswith('''output_projection.weight''' ):
            _UpperCAmelCase				:      List[str]			  =				model_state_dict.pop(__A )
        else:
            _UpperCAmelCase				:      int			  =				model_state_dict.pop(__A )

    _UpperCAmelCase				:      Optional[int]			  =				BioGptConfig.from_pretrained(__A )
    _UpperCAmelCase				:      List[str]			  =				BioGptForCausalLM(__A )

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

    # save
    _UpperCAmelCase				:      Union[str, Any]			  =				os.path.join(__A		,		__A )
    print(f'''Generating {pytorch_weights_dump_path}''' )
    torch.save(__A		,		__A )

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


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












from __future__ import annotations

def      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    _UpperCAmelCase				:      List[str]			  =				[True] * limit
    _UpperCAmelCase				:      Optional[int]			  =				False
    _UpperCAmelCase				:      Dict			  =				False
    _UpperCAmelCase				:      List[str]			  =				True

    for i in range(3		,		int(limit**0.5 + 1 )		,		2 ):
        _UpperCAmelCase				:      List[str]			  =				i * 2
        while index < limit:
            _UpperCAmelCase				:      Union[str, Any]			  =				False
            _UpperCAmelCase				:      int			  =				index + i

    _UpperCAmelCase				:      Optional[int]			  =				[2]

    for i in range(3		,		__A		,		2 ):
        if is_prime[i]:
            primes.append(__A )

    return primes

def      _lowerCamelCase	(						__A    :							int = 1_000_000 )      ->					int:
    _UpperCAmelCase				:      Any			  =				prime_sieve(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    _UpperCAmelCase				:      Tuple			  =				0

    for i in range(len(__A ) ):
        for j in range(i + length		,		len(__A ) ):
            _UpperCAmelCase				:      List[Any]			  =				sum(primes[i:j] )
            if sol >= ceiling:
                break

            if sol in primes:
                _UpperCAmelCase				:      List[str]			  =				j - i
                _UpperCAmelCase				:      Optional[Any]			  =				sol

    return largest


if __name__ == "__main__":
  print(F'{solution() = }')




