
"""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()

a		:       Optional[Any]     						=       2







class     lowercase:



     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     ,				)		->     Tuple:







         """simple docstring"""



         a__	,     a__	,     a__	,     a__		=			bos, unk, pad, eos
         a__		=			[]
         a__		=			[]
         a__		=			{}
         a__		=			self.add_symbol(lowerCAmelCase_   )
         a__		=			self.add_symbol(lowerCAmelCase_   )
         a__		=			self.add_symbol(lowerCAmelCase_   )
         a__		=			self.add_symbol(lowerCAmelCase_   )
         if extra_special_symbols:
             for s in extra_special_symbols:
                 self.add_symbol(lowerCAmelCase_   )
         a__		=			len(self.symbols   )



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







         """simple docstring"""



         return self.indices == other.indices



     def __getitem__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[int]:







         """simple docstring"""



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



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







         """simple docstring"""



         return len(self.symbols   )



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







         """simple docstring"""



         return sym in self.indices



     @classmethod
     def 			lowercase__  (							cls     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         a__		=			cls()
         d.add_from_file(lowerCAmelCase_   )
         return d



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=1     ,				__SCREAMING_SNAKE_CASE=False   )		->     Optional[int]:







         """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(lowerCAmelCase_   )
             self.count.append(lowerCAmelCase_   )
             return idx



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return 0




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         if isinstance(lowerCAmelCase_     ,				lowerCAmelCase_   ):
             try:
                 with open(lowerCAmelCase_     ,				'r'     ,				encoding='utf-8'   ) as fd:
                     self.add_from_file(lowerCAmelCase_   )
             except FileNotFoundError as fnfe:
                 raise fnfe
             except UnicodeError:
                 raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowerCAmelCase_   )   )
             return

         a__		=			f.readlines()
         a__		=			self._load_meta(lowerCAmelCase_   )

         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(lowerCAmelCase_   )
                 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(lowerCAmelCase_   )   )
                 self.add_symbol(lowerCAmelCase_     ,				n=lowerCAmelCase_     ,				overwrite=lowerCAmelCase_   )
             except ValueError:
                 raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''   )




def     __magic_name__		(					UpperCamelCase     :		List[Any]     )     ->						Union[str, Any]:
    a__		=			dict((re.sub(r'@@$'							,		''							,		_SCREAMING_SNAKE_CASE     ), v) if k.endswith('@@'     ) else (re.sub(r'$'							,		'</w>'							,		_SCREAMING_SNAKE_CASE     ), 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     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		List[str]     )     ->						int:




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

    # handle various types of models

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

    a__		=			chkpt['cfg']['model']

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

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

    a__		=			os.path.join(_SCREAMING_SNAKE_CASE							,		VOCAB_FILES_NAMES['merges_file']     )
    shutil.copyfile(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     )

    # model config
    a__		=			os.path.join(_SCREAMING_SNAKE_CASE							,		'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(_SCREAMING_SNAKE_CASE							,		'w'							,		encoding='utf-8'     ) as f:
        f.write(json.dumps(_SCREAMING_SNAKE_CASE							,		ensure_ascii=_SCREAMING_SNAKE_CASE							,		indent=_SCREAMING_SNAKE_CASE     )     )

    # tokenizer config
    a__		=			os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     )

    a__		=			{
        'bos_token': '<s>',
        'eos_token': '</s>',
        'model_max_length': 1024,
        'pad_token': '<pad>',
        'special_tokens_map_file': None,
        'tokenizer_class': 'BioGptTokenizer',
        'unk_token': '<unk>',
    }

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

    # model
    a__		=			chkpt['model']

    # remove unneeded keys
    a__		=			[
        'decoder.version',
    ]
    for k in ignore_keys:
        model_state_dict.pop(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     )

    a__		=			list(model_state_dict.keys()     )
    for layer_name in layer_names:
        if layer_name.endswith('output_projection.weight'     ):
            a__		=			model_state_dict.pop(_SCREAMING_SNAKE_CASE     )
        else:
            a__		=			model_state_dict.pop(_SCREAMING_SNAKE_CASE     )

    a__		=			BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
    a__		=			BioGptForCausalLM(_SCREAMING_SNAKE_CASE     )

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

    # save
    a__		=			os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     )
    print(f'Generating {pytorch_weights_dump_path}'     )
    torch.save(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE     )

    print('Conversion is done!'     )


if __name__ == "__main__":
    a		:       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.'
    )
    a		:       Union[str, Any]     						=       parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  			=				re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__  			=				re.compile(r'''([a-z\d])([A-Z])''')

UpperCAmelCase__  			=				re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__  			=				re.compile(r'''(_{2,})''')

UpperCAmelCase__  			=				r'''^\w+(\.\w+)*$'''

UpperCAmelCase__  			=				r'''<>:/\|?*'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	_uppercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			__A=	_lowercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			return name.lower()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	_single_underscore_re.split(_SCREAMING_SNAKE_CASE )
			__A=	[_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != '' )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			if not re.match(_split_re,_SCREAMING_SNAKE_CASE ):
						raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
			return f"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Union[str, Any]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			if filetype_suffix:
						prefix += f""".{filetype_suffix}"""
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			return f"""{filepath}*"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[Any]=None,_SCREAMING_SNAKE_CASE					: List[str]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if shard_lengths:
						__A=	len(_SCREAMING_SNAKE_CASE )
						__A=	[f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
						if filetype_suffix:
									__A=	[filename + f""".{filetype_suffix}""" for filename in filenames]
						return filenames
			else:
						__A=	prefix
						if filetype_suffix:
									filename += f""".{filetype_suffix}"""
						return [filename]



