


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		:				Tuple									=       2



class        a					:





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





     def __eq__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        return self.indices == other.indices





     def __getitem__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):







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





     def __len__(    self					:				str							):
        return len(self.symbols							)





     def __contains__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return sym in self.indices





     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	cls()
        d.add_from_file(_lowerCAmelCase							)
        return d





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=1    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False							):







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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        return 0







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):







        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

        __lowerCamelCase:				List[Any]     =	f.readlines()
        __lowerCamelCase:				Optional[int]     =	self._load_meta(_lowerCAmelCase							)

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



def     __lowerCAmelCase						(							snake_case	:    Optional[int]						,      snake_case	:    Tuple )       ->							Dict:







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

   # handle various types of models

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

   __lowerCamelCase:				Optional[Any]     =	chkpt["""cfg"""]["""model"""]

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

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

   __lowerCamelCase:				List[Any]     =	os.path.join(snake_case						,      VOCAB_FILES_NAMES["""merges_file"""] )
   shutil.copyfile(snake_case						,      snake_case )

   # model config
   __lowerCamelCase:				Optional[Any]     =	os.path.join(snake_case						,      """config.json""" )

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

   # tokenizer config
   __lowerCamelCase:				int     =	os.path.join(snake_case						,      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(snake_case						,      """w"""						,      encoding="""utf-8""" ) as f:
      f.write(json.dumps(snake_case						,      ensure_ascii=snake_case						,      indent=snake_case ) )

   # model
   __lowerCamelCase:				int     =	chkpt["""model"""]

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

   __lowerCamelCase:				Any     =	list(model_state_dict.keys() )
   for layer_name in layer_names:
      if layer_name.endswith("""output_projection.weight""" ):
         __lowerCamelCase:				int     =	model_state_dict.pop(snake_case )
      else:
         __lowerCamelCase:				str     =	model_state_dict.pop(snake_case )

   __lowerCamelCase:				int     =	BioGptConfig.from_pretrained(snake_case )
   __lowerCamelCase:				List[str]     =	BioGptForCausalLM(snake_case )

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

   # save
   __lowerCamelCase:				Any     =	os.path.join(snake_case						,      snake_case )
   print(f'Generating {pytorch_weights_dump_path}' )
   torch.save(snake_case						,      snake_case )

   print("""Conversion is done!""" )


if __name__ == "__main__":
   _A		:				int									=       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		:				Optional[Any]									=       parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






def     __lowerCAmelCase						(							snake_case	:    int = 100 )       ->							int:
   __lowerCamelCase:				List[Any]     =	0
   __lowerCamelCase:				int     =	0
   for i in range(1						,      n + 1 ):
      sum_of_squares += i**2
      sum_of_ints += i
   return sum_of_ints**2 - sum_of_squares


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


