

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      :					Optional[int]					    =      2





class        lowerCAmelCase__  :



       '''simple docstring'''







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







             '''simple docstring'''



             __A    ,		__A    ,		__A    ,		__A    =bos, unk, pad, eos
             __A    =[]
             __A    =[]
             __A    ={}
             __A    =self.add_symbol(__lowerCAmelCase     )
             __A    =self.add_symbol(__lowerCAmelCase     )
             __A    =self.add_symbol(__lowerCAmelCase     )
             __A    =self.add_symbol(__lowerCAmelCase     )
             if extra_special_symbols:
                   for s in extra_special_symbols:
                         self.add_symbol(__lowerCAmelCase     )
             __A    =len(self.symbols     )

       def __eq__(							self ,   lowercase__     ):







             '''simple docstring'''



             return self.indices == other.indices

       def __getitem__(							self ,   lowercase__     ):







             '''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 ,   lowercase__     ):







             '''simple docstring'''



             return sym in self.indices

       @classmethod
       def     __UpperCamelCase     (							cls ,   lowercase__     ):







             '''simple docstring'''



             __A    =cls()
             d.add_from_file(__lowerCAmelCase     )
             return d

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=1 ,   lowercase__=False     ):







             '''simple docstring'''



             if word in self.indices and not overwrite:
                   __A    =self.indices[word]
                   __A    =self.count[idx] + n
                   return idx
             else:
                   __A    =len(self.symbols     )
                   __A    =idx
                   self.symbols.append(__lowerCAmelCase     )
                   self.count.append(__lowerCAmelCase     )
                   return idx

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return 0


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



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

             __A    =f.readlines()
             __A    =self._load_meta(__lowerCAmelCase     )

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





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





def      A__					(			__A  :						Tuple     ,				__A  :						int			)					->str:
      # 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

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

      __A    =chkpt['''cfg''']['''model''']

      # dicts
      __A    =os.path.join(__A     ,				'''dict.txt'''			)
      if not os.path.isfile(__A			):
            raise ValueError(F'''path to the file {dict_file} does not exist!'''			)
      __A    =Dictionary.load(__A			)
      __A    =rewrite_dict_keys(src_dict.indices			)
      __A    =len(__A			)
      __A    =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)
      __A    =os.path.join(__A     ,				'''bpecodes'''			)
      if not os.path.isfile(__A			):
            raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''			)

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

      # model config
      __A    =os.path.join(__A     ,				'''config.json'''			)

      __A    ={
          '''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
      __A    =os.path.join(__A     ,				__A			)

      __A    ={
          '''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
      __A    =chkpt['''model''']

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

      __A    =list(model_state_dict.keys()			)
      for layer_name in layer_names:
            if layer_name.endswith('''output_projection.weight'''			):
                  __A    =model_state_dict.pop(__A			)
            else:
                  __A    =model_state_dict.pop(__A			)

      __A    =BioGptConfig.from_pretrained(__A			)
      __A    =BioGptForCausalLM(__A			)

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

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

      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      :					str					    =      parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

