





'''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()

lowercase__    :      Any						=    2


class  SCREAMING_SNAKE_CASE    :


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

          '''simple docstring'''






          __A ,__A ,__A ,__A						: Union[str, Any]      =       bos, unk, pad, eos
          __A						: int      =       []
          __A						: Dict      =       []
          __A						: Union[str, Any]      =       {}
          __A						: int      =       self.add_symbol(_UpperCAmelCase)
          __A						: List[str]      =       self.add_symbol(_UpperCAmelCase)
          __A						: Dict      =       self.add_symbol(_UpperCAmelCase)
          __A						: Any      =       self.add_symbol(_UpperCAmelCase)
          if extra_special_symbols:
               for s in extra_special_symbols:
                    self.add_symbol(_UpperCAmelCase)
          __A						: List[Any]      =       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 							SCREAMING_SNAKE_CASE  (						cls						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       cls()
          d.add_from_file(_UpperCAmelCase)
          return d


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase=1						,	_UpperCAmelCase=False):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          return 0


     def 							SCREAMING_SNAKE_CASE  (						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

          __A						: Optional[int]      =       f.readlines()
          __A						: Dict      =       self._load_meta(_UpperCAmelCase)

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






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[Any]			)     ->     Optional[Any]:
     # 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

     __A						: 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!'			)
     __A						: str      =       torch.load(__snake_case ,							map_location='cpu'			)

     __A						: Optional[Any]      =       chkpt['cfg']['model']

     # dicts
     __A						: List[str]      =       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!'			)
     __A						: List[str]      =       Dictionary.load(__snake_case			)
     __A						: str      =       rewrite_dict_keys(src_dict.indices			)
     __A						: List[Any]      =       len(__snake_case			)
     __A						: str      =       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)
     __A						: 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!'			)

     __A						: Optional[int]      =       os.path.join(__snake_case ,							VOCAB_FILES_NAMES['merges_file']			)
     shutil.copyfile(__snake_case ,							__snake_case			)

     # model config
     __A						: Dict      =       os.path.join(__snake_case ,							'config.json'			)

     __A						: 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-1_2,
         '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
     __A						: Any      =       os.path.join(__snake_case ,							__snake_case			)

     __A						: Optional[Any]      =       {
         '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
     __A						: Any      =       chkpt['model']

     # remove unneeded keys
     __A						: Union[str, Any]      =       [
         'decoder.version',
     ]
     for k in ignore_keys:
          model_state_dict.pop(__snake_case ,							__snake_case			)

     __A						: List[Any]      =       list(model_state_dict.keys()			)
     for layer_name in layer_names:
          if layer_name.endswith('output_projection.weight'			):
               __A						: Dict      =       model_state_dict.pop(__snake_case			)
          else:
               __A						: int      =       model_state_dict.pop(__snake_case			)

     __A						: int      =       BioGptConfig.from_pretrained(__snake_case			)
     __A						: Optional[int]      =       BioGptForCausalLM(__snake_case			)

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

     # save
     __A						: Optional[int]      =       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__":
  lowercase__    :      List[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.'''
  )
  lowercase__    :      Optional[Any]						=    parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  SCREAMING_SNAKE_CASE    (datasets.BuilderConfig							):
     lowerCAmelCase					=       None




class  SCREAMING_SNAKE_CASE    (datasets.ArrowBasedBuilder							):
     lowerCAmelCase					=       PandasConfig


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.DatasetInfo(features=self.config.features)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          __A						: Dict      =       dl_manager.download_and_extract(self.config.data_files)
          if isinstance(_UpperCAmelCase						,	(str, list, tuple)):
               __A						: Union[str, Any]      =       data_files
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Optional[int]      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={'files': files})]
          __A						: Tuple      =       []
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Dict      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase						,	gen_kwargs={'files': files}))
          return splits


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __A						: List[str]      =       table_cast(_UpperCAmelCase						,	self.config.features.arrow_schema)
          return pa_table


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
               with open(_UpperCAmelCase						,	'rb') as f:
                    __A						: Optional[int]      =       pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
                    yield i, self._cast_table(_UpperCAmelCase)