



"""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()

lowerCamelCase  			=		2
class  lowercase__  :







       '''simple docstring'''


       def __init__(				self     :    Union[str, Any]						,							*,  # begin keyword-only arguments
           _UpperCAmelCase     :    Optional[Any]="<s>"						,							_UpperCAmelCase     :    List[Any]="<pad>"						,							_UpperCAmelCase     :    List[Any]="</s>"						,							_UpperCAmelCase     :    Union[str, Any]="<unk>"						,							_UpperCAmelCase     :    Union[str, Any]=None						,							)					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				bos, unk, pad, eos
             UpperCAmelCase_     					=				[]
             UpperCAmelCase_     					=				[]
             UpperCAmelCase_     					=				{}
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             if extra_special_symbols:
                   for s in extra_special_symbols:
                         self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				len(self.symbols       )



       def __eq__(				self     :    int						,							_UpperCAmelCase     :    int       )					->		Optional[int]:


             '''simple docstring'''




             return self.indices == other.indices



       def __getitem__(				self     :    Optional[int]						,							_UpperCAmelCase     :    str       )					->		Tuple:


             '''simple docstring'''




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



       def __len__(				self     :    Tuple       )					->		str:


             '''simple docstring'''




             return len(self.symbols       )



       def __contains__(				self     :    List[str]						,							_UpperCAmelCase     :    Optional[int]       )					->		Optional[Any]:


             '''simple docstring'''




             return sym in self.indices



       @classmethod
       def 	lowercase__							(				cls     :    Tuple						,							_UpperCAmelCase     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				cls()
             d.add_from_file(_UpperCAmelCase       )
             return d



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Dict=1						,							_UpperCAmelCase     :    List[str]=False       )					->		List[str]:


             '''simple docstring'''




             if word in self.indices and not overwrite:
                   UpperCAmelCase_     					=				self.indices[word]
                   UpperCAmelCase_     					=				self.count[idx] + n
                   return idx
             else:
                   UpperCAmelCase_     					=				len(self.symbols       )
                   UpperCAmelCase_     					=				idx
                   self.symbols.append(_UpperCAmelCase       )
                   self.count.append(_UpperCAmelCase       )
                   return idx



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             return 0







       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Dict       )					->		str:


             '''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

             UpperCAmelCase_     					=				f.readlines()
             UpperCAmelCase_     					=				self._load_meta(_UpperCAmelCase       )

             for line in lines[indices_start_line:]:
                   try:
                         UpperCAmelCase_					,       UpperCAmelCase_     					=				line.rstrip().rsplit(" "						,							1       )
                         if field == "#fairseq:overwrite":
                               UpperCAmelCase_     					=				True
                               UpperCAmelCase_					,       UpperCAmelCase_     					=				line.rsplit(" "						,							1       )
                         else:
                               UpperCAmelCase_     					=				False
                         UpperCAmelCase_     					=				int(_UpperCAmelCase       )
                         UpperCAmelCase_     					=				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__      ( lowerCAmelCase__ ):
      # (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}
      UpperCAmelCase_     					=				dict((re.sub(r"@@$"      ,     ""      ,     lowerCAmelCase__ ), v) if k.endswith("@@" ) else (re.sub(r"$"      ,     "</w>"      ,     lowerCAmelCase__ ), v) for k, v in d.items() )
      UpperCAmelCase_     					=				"<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
            del da[f"""{k}</w>"""]
            UpperCAmelCase_     					=				d[k]  # restore
      return da
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # 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

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

      UpperCAmelCase_     					=				chkpt["cfg"]["model"]

      # dicts
      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     "dict.txt" )
      if not os.path.isfile(lowerCAmelCase__ ):
            raise ValueError(f"""path to the file {dict_file} does not exist!""" )
      UpperCAmelCase_     					=				Dictionary.load(lowerCAmelCase__ )
      UpperCAmelCase_     					=				rewrite_dict_keys(src_dict.indices )
      UpperCAmelCase_     					=				len(lowerCAmelCase__ )
      UpperCAmelCase_     					=				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)
      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     "bpecodes" )
      if not os.path.isfile(lowerCAmelCase__ ):
            raise ValueError(f"""path to the file {bpecodes_file} does not exist!""" )

      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     VOCAB_FILES_NAMES["merges_file"] )
      shutil.copyfile(lowerCAmelCase__      ,     lowerCAmelCase__ )

      # model config
      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     "config.json" )

      UpperCAmelCase_     					=				{
          "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
      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     lowerCAmelCase__ )

      UpperCAmelCase_     					=				{
          "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
      UpperCAmelCase_     					=				chkpt["model"]

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

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

      UpperCAmelCase_     					=				BioGptConfig.from_pretrained(lowerCAmelCase__ )
      UpperCAmelCase_     					=				BioGptForCausalLM(lowerCAmelCase__ )

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

      # save
      UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     lowerCAmelCase__ )
      print(f"""Generating {pytorch_weights_dump_path}""" )
      torch.save(lowerCAmelCase__      ,     lowerCAmelCase__ )

      print("Conversion is done!" )


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











class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  {}






			def   a_			(     self  :       Any  )      ->      None:







					"""simple docstring"""






					print(self.vertex  )
					for i in self.vertex:
							print(__lowerCAmelCase				,      """ -> """				,      """ -> """.join([str(__lowerCAmelCase  ) for j in self.vertex[i]]  )  )






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int  )      ->      None:







					"""simple docstring"""






					if from_vertex in self.vertex:
							self.vertex[from_vertex].append(__lowerCAmelCase  )
					else:
							# else make a new vertex
							A__     =  [to_vertex]






			def   a_			(     self  :       List[str]  )      ->      None:







					"""simple docstring"""






					A__     =  [False] * len(self.vertex  )

					# call the recursive helper function
					for i in range(len(self.vertex  )  ):
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       list  )      ->      None:







					"""simple docstring"""






					A__     =  True

					print(__lowerCAmelCase				,      end=""" """  )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )


if __name__ == "__main__":
					A       :   Tuple					= Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print('''DFS:''')
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3



