

'''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()

_snake_case         =  2


class        a__  :


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





      """simple docstring"""





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


   def __eq__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.indices == other.indices


   def __getitem__(			self      ,    _UpperCamelCase  ):





      """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      ,    _UpperCamelCase  ):





      """simple docstring"""





      return sym in self.indices


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       cls()
      d.add_from_file(_UpperCamelCase  )
      return d


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=1      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      if word in self.indices and not overwrite:
         _lowercase      :				Optional[Any]      					=       self.indices[word]
         _lowercase      :				List[str]      					=       self.count[idx] + n
         return idx
      else:
         _lowercase      :				int      					=       len(self.symbols  )
         _lowercase      :				Dict      					=       idx
         self.symbols.append(_UpperCamelCase  )
         self.count.append(_UpperCamelCase  )
         return idx


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return 0






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

      _lowercase      :				Any      					=       f.readlines()
      _lowercase      :				Union[str, Any]      					=       self._load_meta(_UpperCamelCase  )

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







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







def  _A		(     snake_case		,							snake_case					)      ->     int:
   # 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

   _lowercase      :				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!'''					)
   _lowercase      :				str      					=       torch.load(snake_case		,							map_location="cpu"					)

   _lowercase      :				Optional[int]      					=       chkpt["cfg"]["model"]

   # dicts
   _lowercase      :				Dict      					=       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!'''					)
   _lowercase      :				int      					=       Dictionary.load(snake_case					)
   _lowercase      :				int      					=       rewrite_dict_keys(src_dict.indices					)
   _lowercase      :				Dict      					=       len(snake_case					)
   _lowercase      :				Optional[int]      					=       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)
   _lowercase      :				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!'''					)

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

   # model config
   _lowercase      :				Dict      					=       os.path.join(snake_case		,							"config.json"					)

   _lowercase      :				Union[str, Any]      					=       {
       "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
   _lowercase      :				Optional[Any]      					=       os.path.join(snake_case		,							snake_case					)

   _lowercase      :				Optional[int]      					=       {
       "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
   _lowercase      :				str      					=       chkpt["model"]

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

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

   _lowercase      :				Dict      					=       BioGptConfig.from_pretrained(snake_case					)
   _lowercase      :				List[str]      					=       BioGptForCausalLM(snake_case					)

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

   # save
   _lowercase      :				str      					=       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__":
    _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.'
    )
    _snake_case         =  parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






