

'''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    A__ :



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





           '''simple docstring'''
           A_   ,     A_   ,     A_   ,     A_		   = bos, unk, pad, eos
           A_		   = []
           A_		   = []
           A_		   = {}
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(UpperCamelCase__    )
           A_		   = len(self.symbols    )



    def __eq__(   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return self.indices == other.indices



    def __getitem__(   self						,			UpperCamelCase__    )  -> List[Any]:





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



    def __len__(   self    )  -> List[str]:





           '''simple docstring'''
           return len(self.symbols    )



    def __contains__(   self						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           return sym in self.indices



    @classmethod
    def    snake_case_     (   cls						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = cls()
           d.add_from_file(UpperCamelCase__    )
           return d



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=1						,			UpperCamelCase__=False    )  -> Tuple:





           '''simple docstring'''
           if word in self.indices and not overwrite:
                  A_		   = self.indices[word]
                  A_		   = self.count[idx] + n
                  return idx
           else:
                  A_		   = len(self.symbols    )
                  A_		   = idx
                  self.symbols.append(UpperCamelCase__    )
                  self.count.append(UpperCamelCase__    )
                  return idx



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           return 0







    def    snake_case_     (   self						,			UpperCamelCase__    )  -> 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

           A_		   = f.readlines()
           A_		   = self._load_meta(UpperCamelCase__    )

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

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

       A_		   = chkpt["""cfg"""]["""model"""]

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

       A_		   = os.path.join(UpperCAmelCase__,    VOCAB_FILES_NAMES["""merges_file"""]			)
       shutil.copyfile(UpperCAmelCase__,    UpperCAmelCase__			)

       # model config
       A_		   = os.path.join(UpperCAmelCase__,    """config.json"""			)

       A_		   = {
           """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
       A_		   = os.path.join(UpperCAmelCase__,    UpperCAmelCase__			)

       A_		   = {
           """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(UpperCAmelCase__,    """w""",    encoding="""utf-8"""			) as f:
              f.write(json.dumps(UpperCAmelCase__,    ensure_ascii=UpperCAmelCase__,    indent=UpperCAmelCase__			)			)

       # model
       A_		   = chkpt["""model"""]

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

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

       A_		   = BioGptConfig.from_pretrained(UpperCAmelCase__			)
       A_		   = BioGptForCausalLM(UpperCAmelCase__			)

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

       # save
       A_		   = 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)








'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
