
'''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 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						*,  # begin keyword-only arguments
         __SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE=None						,						)  ->   Optional[int]:




           """simple docstring"""







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






     def __eq__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           return self.indices == other.indices






     def __getitem__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







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






     def __len__(							self  )  ->   str:




           """simple docstring"""







           return len(self.symbols  )






     def __contains__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           return sym in self.indices






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				cls()
           d.add_from_file(__UpperCamelCase  )
           return d






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=False  )  ->   Any:




           """simple docstring"""







           if word in self.indices and not overwrite:
                 UpperCamelCase__    :   Optional[Any]					=				self.indices[word]
                 UpperCamelCase__    :   Union[str, Any]					=				self.count[idx] + n
                 return idx
           else:
                 UpperCamelCase__    :   int					=				len(self.symbols  )
                 UpperCamelCase__    :   Optional[Any]					=				idx
                 self.symbols.append(__UpperCamelCase  )
                 self.count.append(__UpperCamelCase  )
                 return idx






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           return 0






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """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__    :   Any					=				f.readlines()
           UpperCamelCase__    :   Any					=				self._load_meta(__UpperCamelCase  )

           for line in lines[indices_start_line:]:
                 try:
                       UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				line.rstrip().rsplit(''' '''						,						1  )
                       if field == "#fairseq:overwrite":
                             UpperCamelCase__    :   Any					=				True
                             UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				line.rsplit(''' '''						,						1  )
                       else:
                             UpperCamelCase__    :   str					=				False
                       UpperCamelCase__    :   Any					=				int(__UpperCamelCase  )
                       UpperCamelCase__    :   Tuple					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # (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__    :   Any					=				dict((re.sub(R'''@@$''' ,		'''''' ,		UpperCAmelCase__		), v) if k.endswith('''@@'''		) else (re.sub(R'''$''' ,		'''</w>''' ,		UpperCAmelCase__		), v) for k, v in d.items()		)
      UpperCamelCase__    :   str					=				'''<s> <pad> </s> <unk>'''.split()
      # restore the special tokens
      for k in keep_keys:
            del da[f'''{k}</w>''']
            UpperCamelCase__    :   str					=				d[k]  # restore
      return da


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      # prep
      if not os.path.exists(UpperCAmelCase__		):
            raise ValueError(f'''path {biogpt_checkpoint_path} does not exist!'''		)
      os.makedirs(UpperCAmelCase__ ,		exist_ok=UpperCAmelCase__		)
      print(f'''Writing results to {pytorch_dump_folder_path}'''		)

      # handle various types of models

      UpperCamelCase__    :   List[str]					=				os.path.join(UpperCAmelCase__ ,		'''checkpoint.pt'''		)
      if not os.path.isfile(UpperCAmelCase__		):
            raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''		)
      UpperCamelCase__    :   List[str]					=				torch.load(UpperCAmelCase__ ,		map_location='''cpu'''		)

      UpperCamelCase__    :   Optional[int]					=				chkpt['''cfg''']['''model''']

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

      # merges_file (bpecodes)
      UpperCamelCase__    :   str					=				os.path.join(UpperCAmelCase__ ,		'''bpecodes'''		)
      if not os.path.isfile(UpperCAmelCase__		):
            raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''		)

      UpperCamelCase__    :   Any					=				os.path.join(UpperCAmelCase__ ,		VOCAB_FILES_NAMES['''merges_file''']		)
      shutil.copyfile(UpperCAmelCase__ ,		UpperCAmelCase__		)

      # model config
      UpperCamelCase__    :   List[Any]					=				os.path.join(UpperCAmelCase__ ,		'''config.json'''		)

      UpperCamelCase__    :   List[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(UpperCAmelCase__ ,		'''w''' ,		encoding='''utf-8'''		) as f:
            f.write(json.dumps(UpperCAmelCase__ ,		ensure_ascii=UpperCAmelCase__ ,		indent=UpperCAmelCase__		)		)

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

      UpperCamelCase__    :   List[Any]					=				{
          '''bos_token''': '''<s>''',
          '''eos_token''': '''</s>''',
          '''model_max_length''': 1_0_2_4,
          '''pad_token''': '''<pad>''',
          '''special_tokens_map_file''': None,
          '''tokenizer_class''': '''BioGptTokenizer''',
          '''unk_token''': '''<unk>''',
      }

      print(f'''Generating {biogpt_tokenizer_config_file}'''		)
      with open(UpperCAmelCase__ ,		'''w''' ,		encoding='''utf-8'''		) as f:
            f.write(json.dumps(UpperCAmelCase__ ,		ensure_ascii=UpperCAmelCase__ ,		indent=UpperCAmelCase__		)		)

      # model
      UpperCamelCase__    :   Any					=				chkpt['''model''']

      # remove unneeded keys
      UpperCamelCase__    :   Optional[int]					=				[
          '''decoder.version''',
      ]
      for k in ignore_keys:
            model_state_dict.pop(UpperCAmelCase__ ,		UpperCAmelCase__		)

      UpperCamelCase__    :   List[str]					=				list(model_state_dict.keys()		)
      for layer_name in layer_names:
            if layer_name.endswith('''output_projection.weight'''		):
                  UpperCamelCase__    :   List[Any]					=				model_state_dict.pop(UpperCAmelCase__		)
            else:
                  UpperCamelCase__    :   Optional[Any]					=				model_state_dict.pop(UpperCAmelCase__		)

      UpperCamelCase__    :   Tuple					=				BioGptConfig.from_pretrained(UpperCAmelCase__		)
      UpperCamelCase__    :   str					=				BioGptForCausalLM(UpperCAmelCase__		)

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

      # save
      UpperCamelCase__    :   str					=				os.path.join(UpperCAmelCase__ ,		UpperCAmelCase__		)
      print(f'''Generating {pytorch_weights_dump_path}'''		)
      torch.save(UpperCAmelCase__ ,		UpperCAmelCase__		)

      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)




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


