'''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			:      Optional[int]					      =				2







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       *,  # begin keyword-only arguments
     SCREAMING_SNAKE_CASE__   :	Optional[int]="<s>"      ,       SCREAMING_SNAKE_CASE__   :	str="<pad>"      ,       SCREAMING_SNAKE_CASE__   :	Dict="</s>"      ,       SCREAMING_SNAKE_CASE__   :	Dict="<unk>"      ,       SCREAMING_SNAKE_CASE__   :	Any=None      ,       )  ->    Tuple:
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						bos, unk, pad, eos
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						{}
  __lowerCAmelCase											=						self.add_symbol(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.add_symbol(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.add_symbol(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.add_symbol(SCREAMING_SNAKE_CASE__							)
  if extra_special_symbols:
   for s in extra_special_symbols:
    self.add_symbol(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						len(self.symbols							)
 def __eq__(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    Any:
  return self.indices == other.indices
 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    int:
  if idx < len(self.symbols							):
   return self.symbols[idx]
  return self.unk_word
 def __len__(      self   :	str							)  ->    Optional[Any]:
  return len(self.symbols							)
 def __contains__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    int:
  return sym in self.indices
 @classmethod
 def a					(      cls   :	Any      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    List[Any]:
  __lowerCAmelCase											=						cls()
  d.add_from_file(SCREAMING_SNAKE_CASE__							)
  return d
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Dict=1      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    List[Any]:
  if word in self.indices and not overwrite:
   __lowerCAmelCase											=						self.indices[word]
   __lowerCAmelCase											=						self.count[idx] + n
   return idx
  else:
   __lowerCAmelCase											=						len(self.symbols							)
   __lowerCAmelCase											=						idx
   self.symbols.append(SCREAMING_SNAKE_CASE__							)
   self.count.append(SCREAMING_SNAKE_CASE__							)
   return idx
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    List[str]:
  return 0





 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Dict:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   try:
    with open(SCREAMING_SNAKE_CASE__      ,       """r"""      ,       encoding="""utf-8"""							) as fd:
     self.add_from_file(SCREAMING_SNAKE_CASE__							)
   except FileNotFoundError as fnfe:
    raise fnfe
   except UnicodeError:
    raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(SCREAMING_SNAKE_CASE__							)							)
   return

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

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







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						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											=						"""<s> <pad> </s> <unk>""".split()
 # restore the special tokens
 for k in keep_keys:
  del da[f"""{k}</w>"""]
  __lowerCAmelCase											=						d[k]  # restore
 return da







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Any      )					->     List[Any]:






 '''simple docstring'''

 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											=						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											=						torch.load(snake_case_						,		map_location="""cpu"""      )

 __lowerCAmelCase											=						chkpt["""cfg"""]["""model"""]

 # dicts
 __lowerCAmelCase											=						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											=						Dictionary.load(snake_case_      )
 __lowerCAmelCase											=						rewrite_dict_keys(src_dict.indices      )
 __lowerCAmelCase											=						len(snake_case_      )
 __lowerCAmelCase											=						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											=						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											=						os.path.join(snake_case_						,		VOCAB_FILES_NAMES["""merges_file"""]      )
 shutil.copyfile(snake_case_						,		snake_case_      )

 # model config
 __lowerCAmelCase											=						os.path.join(snake_case_						,		"""config.json"""      )

 __lowerCAmelCase											=						{
     """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(snake_case_						,		"""w"""						,		encoding="""utf-8"""      ) as f:
  f.write(json.dumps(snake_case_						,		ensure_ascii=snake_case_						,		indent=snake_case_      )      )

 # tokenizer config
 __lowerCAmelCase											=						os.path.join(snake_case_						,		snake_case_      )

 __lowerCAmelCase											=						{
     """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(snake_case_						,		"""w"""						,		encoding="""utf-8"""      ) as f:
  f.write(json.dumps(snake_case_						,		ensure_ascii=snake_case_						,		indent=snake_case_      )      )

 # model
 __lowerCAmelCase											=						chkpt["""model"""]

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

 __lowerCAmelCase											=						list(model_state_dict.keys()      )
 for layer_name in layer_names:
  if layer_name.endswith("""output_projection.weight"""      ):
   __lowerCAmelCase											=						model_state_dict.pop(snake_case_      )
  else:
   __lowerCAmelCase											=						model_state_dict.pop(snake_case_      )

 __lowerCAmelCase											=						BioGptConfig.from_pretrained(snake_case_      )
 __lowerCAmelCase											=						BioGptForCausalLM(snake_case_      )

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

 # save
 __lowerCAmelCase											=						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			:      Tuple					      =				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			:      Any					      =				parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case_      )      )






