"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a      :		int           =   logging.get_logger(__name__)

_a      :		Tuple           =   '▁'

_a      :		int           =   {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a      :		Any           =   {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a      :		List[Any]           =   {'vinai/bartpho-syllable': 1_024}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			str							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Optional[Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[Any]							=		["input_ids", "attention_mask"]






     def __init__(		self				,     a__				,     a__				,     a__="<s>"				,     a__="</s>"				,     a__="</s>"				,     a__="<s>"				,     a__="<unk>"				,     a__="<pad>"				,     a__="<mask>"				,     a__ = None				,     **a__				,     ):
       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase						:       List[str]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else mask_token

       _lowerCAmelCase						:       str  			=     {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a__				,     eos_token=a__				,     unk_token=a__				,     sep_token=a__				,     cls_token=a__				,     pad_token=a__				,     mask_token=a__				,     sp_model_kwargs=self.sp_model_kwargs				,     **a__				,     )

       _lowerCAmelCase						:       List[str]  			=     vocab_file
       _lowerCAmelCase						:       str  			=     monolingual_vocab_file
       _lowerCAmelCase						:       List[str]  			=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(a__  )  )

       # Load the reduced vocab

       # Keep order of special tokens for backward compatibility
       _lowerCAmelCase						:       Optional[Any]  			=     {}
       _lowerCAmelCase						:       Tuple  			=     0
       for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(a__  ) not in self.fairseq_tokens_to_ids:
           _lowerCAmelCase						:       Dict  			=     cnt
           cnt += 1
       with open(a__				,     """r"""				,     encoding="""utf-8"""  ) as f:
         for line in f.readlines():
           _lowerCAmelCase						:       str  			=     line.strip().split()[0]
           _lowerCAmelCase						:       Any  			=     len(self.fairseq_tokens_to_ids  )
       if str(a__  ) not in self.fairseq_tokens_to_ids:
         _lowerCAmelCase						:       str  			=     len(self.fairseq_tokens_to_ids  )

       _lowerCAmelCase						:       List[Any]  			=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






     def __getstate__(		self  ):
       _lowerCAmelCase						:       Dict  			=     self.__dict__.copy()
       _lowerCAmelCase						:       str  			=     None
       _lowerCAmelCase						:       Dict  			=     self.sp_model.serialized_model_proto()
       return state






     def __setstate__(		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     d

       # for backward compatibility
       if not hasattr(self				,     """sp_model_kwargs"""  ):
         _lowerCAmelCase						:       int  			=     {}

       _lowerCAmelCase						:       Any  			=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






     def     __A  (		self				,     a__				,     a__ = None  ):

       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _lowerCAmelCase						:       Optional[int]  			=     [self.cls_token_id]
       _lowerCAmelCase						:       Any  			=     [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = False  ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a__				,     token_ids_a=a__				,     already_has_special_tokens=a__  )

       if token_ids_a is None:
         return [1] + ([0] * len(a__  )) + [1]
       return [1] + ([0] * len(a__  )) + [1, 1] + ([0] * len(a__  )) + [1]






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     [self.sep_token_id]
       _lowerCAmelCase						:       int  			=     [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]






     @property
     def     __A  (		self  ):
       return len(self.fairseq_ids_to_tokens  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     {self.convert_ids_to_tokens(a__  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab






     def     __A  (		self				,     a__  ):
       return self.sp_model.encode(a__				,     out_type=a__  )






     def     __A  (		self				,     a__  ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       else:
         return self.unk_token_id






     def     __A  (		self				,     a__  ):
       return self.fairseq_ids_to_tokens[index]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """""".join(a__  ).replace(a__				,     """ """  ).strip()
       return out_string






     def     __A  (		self				,     a__				,     a__ = None  ):
       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:       Optional[int]  			=     os.path.join(
           a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:       Optional[Any]  			=     os.path.join(
           a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]				,     )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(a__  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file				,     a__  )
       elif not os.path.isfile(self.vocab_file  ):
         with open(a__				,     """wb"""  ) as fi:
           _lowerCAmelCase						:       List[Any]  			=     self.sp_model.serialized_model_proto()
           fi.write(a__  )

       if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
           a__  ) and os.path.isfile(self.monolingual_vocab_file  ):
         copyfile(self.monolingual_vocab_file				,     a__  )
       elif not os.path.isfile(self.monolingual_vocab_file  ):
         with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as fp:
           for token in self.fairseq_tokens_to_ids:
             if token not in self.all_special_tokens:
               fp.write(F"{str(a__  )} \n"  )

       return out_vocab_file, out_monolingual_vocab_file












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()

_a      	=  2





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    *,  # begin keyword-only arguments
	    UpperCAmelCase="<s>"       ,    UpperCAmelCase="<pad>"       ,    UpperCAmelCase="</s>"       ,    UpperCAmelCase="<unk>"       ,    UpperCAmelCase=None       ,    ):
							"""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       ,    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        UpperCamelCase    (							cls       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1       ,    UpperCAmelCase=False    ):
							"""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        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return 0

	def        UpperCamelCase    (							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

							_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	)->  str:





						"""simple docstring"""



						_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	)->  str:





						"""simple docstring"""



						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': 1_024,
						    '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__":
		_a      	=  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.'''
		)
		_a      	=  parser.parse_args()
		convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
