





'''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__   :				str      =   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						:			Tuple								=	bos, unk, pad, eos
          __A						:			Tuple								=	[]
          __A						:			List[str]								=	[]
          __A						:			Any								=	{}
          __A						:			Tuple								=	self.add_symbol(_UpperCAmelCase)
          __A						:			int								=	self.add_symbol(_UpperCAmelCase)
          __A						:			Optional[int]								=	self.add_symbol(_UpperCAmelCase)
          __A						:			List[Any]								=	self.add_symbol(_UpperCAmelCase)
          if extra_special_symbols:
               for s in extra_special_symbols:
                    self.add_symbol(_UpperCAmelCase)
          __A						:			Optional[int]								=	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						:			Dict								=	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						:			Any								=	self.indices[word]
               __A						:			Optional[int]								=	self.count[idx] + n
               return idx
          else:
               __A						:			Union[str, Any]								=	len(self.symbols)
               __A						:			Tuple								=	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						:			Union[str, Any]								=	f.readlines()
          __A						:			int								=	self._load_meta(_UpperCAmelCase)

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






def 	_lowerCAmelCase      (  __snake_case  :			List[Any]			,							__snake_case  :			Dict      )					->					int:
     # 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						:			Dict								=	torch.load(__snake_case			,							map_location='cpu'      )

     __A						:			Dict								=	chkpt['cfg']['model']

     # dicts
     __A						:			List[Any]								=	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						:			Union[str, Any]								=	Dictionary.load(__snake_case      )
     __A						:			List[Any]								=	rewrite_dict_keys(src_dict.indices      )
     __A						:			str								=	len(__snake_case      )
     __A						:			int								=	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						:			int								=	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						:			Any								=	os.path.join(__snake_case			,							VOCAB_FILES_NAMES['merges_file']      )
     shutil.copyfile(__snake_case			,							__snake_case      )

     # model config
     __A						:			List[str]								=	os.path.join(__snake_case			,							'config.json'      )

     __A						:			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-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						:			Tuple								=	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						:			Dict								=	chkpt['model']

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

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

     __A						:			str								=	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						:			Union[str, Any]								=	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__   :				Any      =   parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))