

'''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_       						=							2





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







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      int							):
     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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					f.readlines()
     SCREAMING_SNAKE_CASE:int						      =					self._load_meta(SCREAMING_SNAKE_CASE__							)

     for line in lines[indices_start_line:]:
       try:
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					line.rstrip().rsplit(" "     ,1							)
         if field == "#fairseq:overwrite":
           SCREAMING_SNAKE_CASE:Optional[int]						      =					True
           SCREAMING_SNAKE_CASE:Optional[Any]						      =					line.rsplit(" "     ,1							)
         else:
           SCREAMING_SNAKE_CASE:Dict						      =					False
         SCREAMING_SNAKE_CASE:str						      =					int(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE: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(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    A_  ( snake_case ):
  # (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}
  SCREAMING_SNAKE_CASE:List[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() )
  SCREAMING_SNAKE_CASE:List[Any]						      =					"<s> <pad> </s> <unk>".split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'''{k}</w>''']
    SCREAMING_SNAKE_CASE:List[Any]						      =					d[k]  # restore
  return da





def    A_  ( snake_case      ,       snake_case ):
  # prep
  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

  SCREAMING_SNAKE_CASE:List[Any]						      =					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!''' )
  SCREAMING_SNAKE_CASE:str						      =					torch.load(snake_case      ,       map_location="cpu" )

  SCREAMING_SNAKE_CASE:List[Any]						      =					chkpt["cfg"]["model"]

  # dicts
  SCREAMING_SNAKE_CASE:List[str]						      =					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!''' )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					Dictionary.load(snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					rewrite_dict_keys(src_dict.indices )
  SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
  SCREAMING_SNAKE_CASE:Tuple						      =					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)
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					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!''' )

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

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

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

  # tokenizer config
  SCREAMING_SNAKE_CASE:Optional[int]						      =					os.path.join(snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:Tuple						      =					{
      "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
  SCREAMING_SNAKE_CASE:Dict						      =					chkpt["model"]

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

  SCREAMING_SNAKE_CASE:Tuple						      =					list(model_state_dict.keys() )
  for layer_name in layer_names:
    if layer_name.endswith("output_projection.weight" ):
      SCREAMING_SNAKE_CASE:Dict						      =					model_state_dict.pop(snake_case )
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					model_state_dict.pop(snake_case )

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					BioGptConfig.from_pretrained(snake_case )
  SCREAMING_SNAKE_CASE:Tuple						      =					BioGptForCausalLM(snake_case )

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

  # save
  SCREAMING_SNAKE_CASE:Tuple						      =					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_       						=							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_       						=							parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

from importlib import import_module

from .logging import get_logger


A_       						=							get_logger(__name__)





class 							_snake_case :
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Dict=None							):
     SCREAMING_SNAKE_CASE:Any						      =					attrs or []
     if module is not None:
       for key in module.__dict__:
         if key in attrs or not key.startswith("__"							):
           setattr(self     ,SCREAMING_SNAKE_CASE__     ,getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:Any						      =					module._original_module if isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) else module





class 							_snake_case :
   _A       :     Any       =					[]
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None							):
     SCREAMING_SNAKE_CASE:Any						      =					obj
     SCREAMING_SNAKE_CASE:Any						      =					target
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					new
     SCREAMING_SNAKE_CASE:Any						      =					target.split("."							)[0]
     SCREAMING_SNAKE_CASE:str						      =					{}
     SCREAMING_SNAKE_CASE:List[Any]						      =					attrs or []
   def __enter__( self			:      Tuple							):
     *SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.target.split("."							)

     # Patch modules:
     # it's used to patch attributes of submodules like "os.path.join";
     # in this case we need to patch "os" and "os.path"

     for i in range(len(SCREAMING_SNAKE_CASE__							)							):
       try:
         SCREAMING_SNAKE_CASE:int						      =					import_module(".".join(submodules[: i + 1]							)							)
       except ModuleNotFoundError:
         continue
       # We iterate over all the globals in self.obj in case we find "os" or "os.path"
       for attr in self.obj.__dir__():
         SCREAMING_SNAKE_CASE:str						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
         # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
         # This allows to patch renamed modules like "from os import path as ospath".
         if obj_attr is submodule or (
             (isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) and obj_attr._original_module is submodule)
         ):
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					obj_attr
           # patch at top level
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(SCREAMING_SNAKE_CASE__     ,attrs=self.attrs							)							)
           SCREAMING_SNAKE_CASE:int						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           # construct lower levels patches
           for key in submodules[i + 1 :]:
             setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)     ,attrs=self.attrs							)							)
             SCREAMING_SNAKE_CASE:Dict						      =					getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
           # finally set the target attribute
           setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,self.new							)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

     if submodules:  # if it's an attribute of a submodule like "os.path.join"
       try:
         SCREAMING_SNAKE_CASE:Any						      =					getattr(import_module(".".join(SCREAMING_SNAKE_CASE__							)							)     ,SCREAMING_SNAKE_CASE__							)
       except (AttributeError, ModuleNotFoundError):
         return
       # We iterate over all the globals in self.obj in case we find "os.path.join"
       for attr in self.obj.__dir__():
         # We don't check for the name of the global, but rather if its value *is* "os.path.join".
         # This allows to patch renamed attributes like "from os.path import join as pjoin".
         if getattr(self.obj     ,SCREAMING_SNAKE_CASE__							) is attr_value:
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
       SCREAMING_SNAKE_CASE:int						      =					globals()["__builtins__"][target_attr]
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     else:
       raise RuntimeError(F'''Tried to patch attribute {target_attr} instead of a submodule.'''							)
   def __exit__( self			:      List[Any]     ,*SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     for attr in list(self.original							):
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.original.pop(SCREAMING_SNAKE_CASE__							)							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     self.__enter__()
     self._active_patches.append(self							)







   def     __UpperCamelCase						( self			:      Dict							):
     try:
       self._active_patches.remove(self							)
     except ValueError:
       # If the patch hasn't been started this will fail
       return None

     return self.__exit__()






