





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    :       Union[str, Any]		       =    2



class 							_UpperCAmelCase       :

  """simple docstring"""


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






  def __eq__(							self					:						Dict   ,    lowerCAmelCase_					:						Dict	)				->		str:
     return self.indices == other.indices






  def __getitem__(							self					:						List[Any]   ,    lowerCAmelCase_					:						int	)				->		Union[str, Any]:
     if idx < len(self.symbols	):
        return self.symbols[idx]
     return self.unk_word






  def __len__(							self					:						Tuple	)				->		List[Any]:
     return len(self.symbols	)






  def __contains__(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						Dict	)				->		Optional[int]:
     return sym in self.indices






  @classmethod
  def 							lowercase						(							cls					:						Dict   ,    lowerCAmelCase_					:						str	)				->		str:
     __lowerCAmelCase				      =      cls()
     d.add_from_file(lowerCAmelCase_	)
     return d






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						int=1   ,    lowerCAmelCase_					:						Any=False	)				->		Optional[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(lowerCAmelCase_	)
        self.count.append(lowerCAmelCase_	)
        return idx






  def 							lowercase						(							self					:						str   ,    lowerCAmelCase_					:						Union[str, Any]	)				->		Dict:
     return 0






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Union[str, Any]	)				->		int:
     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				      =      f.readlines()
     __lowerCAmelCase				      =      self._load_meta(lowerCAmelCase_	)

     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(lowerCAmelCase_	)
           __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(lowerCAmelCase_	)	)
           self.add_symbol(lowerCAmelCase_   ,    n=lowerCAmelCase_   ,    overwrite=lowerCAmelCase_	)
        except ValueError:
           raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''	)





def     a_       (					lowerCAmelCase_ :     List[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}
   __lowerCAmelCase				      =      dict((re.sub(R'@@$',      '',      lowerCAmelCase_      ), v) if k.endswith('@@'      ) else (re.sub(R'$',      '</w>',      lowerCAmelCase_      ), 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     a_       (					lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     List[str]      ):
   # prep
   if not os.path.exists(lowerCAmelCase_      ):
      raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!"""      )
   os.makedirs(lowerCAmelCase_,      exist_ok=lowerCAmelCase_      )
   print(F"""Writing results to {pytorch_dump_folder_path}"""      )

   # handle various types of models

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

   __lowerCAmelCase				      =      chkpt['cfg']['model']

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

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

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

   # model config
   __lowerCAmelCase				      =      os.path.join(lowerCAmelCase_,      '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': 0.02,
       '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(lowerCAmelCase_,      'w',      encoding='utf-8'      ) as f:
      f.write(json.dumps(lowerCAmelCase_,      ensure_ascii=lowerCAmelCase_,      indent=lowerCAmelCase_      )      )

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

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

   # model
   __lowerCAmelCase				      =      chkpt['model']

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

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

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

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

   # save
   __lowerCAmelCase				      =      os.path.join(lowerCAmelCase_,      lowerCAmelCase_      )
   print(F"""Generating {pytorch_weights_dump_path}"""      )
   torch.save(lowerCAmelCase_,      lowerCAmelCase_      )

   print('Conversion is done!'      )


if __name__ == "__main__":
     _snake_case    :       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.'
     )
     _snake_case    :       int		       =    parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
