


'''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	:	List[str]     =     2
class 						__UpperCamelCase	:





							def __init__(  self		:Tuple  ,*,  # begin keyword-only arguments
							    _UpperCamelCase		:Tuple="<s>"  ,_UpperCamelCase		:List[str]="<pad>"  ,_UpperCamelCase		:str="</s>"  ,_UpperCamelCase		:Union[str, Any]="<unk>"  ,_UpperCamelCase		:List[str]=None  ,):
									snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							List[Any]			       =						bos, unk, pad, eos
									snake_case_		:							Tuple			       =						[]
									snake_case_		:							Optional[Any]			       =						[]
									snake_case_		:							Tuple			       =						{}
									snake_case_		:							str			       =						self.add_symbol(_UpperCamelCase			)
									snake_case_		:							str			       =						self.add_symbol(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						self.add_symbol(_UpperCamelCase			)
									snake_case_		:							Dict			       =						self.add_symbol(_UpperCamelCase			)
									if extra_special_symbols:
											for s in extra_special_symbols:
													self.add_symbol(_UpperCamelCase			)
									snake_case_		:							str			       =						len(self.symbols			)





							def __eq__(  self		:str  ,_UpperCamelCase		:Any			):
									return self.indices == other.indices





							def __getitem__(  self		:int  ,_UpperCamelCase		:str			):
									if idx < len(self.symbols			):
											return self.symbols[idx]
									return self.unk_word





							def __len__(  self		:Optional[int]			):
									return len(self.symbols			)





							def __contains__(  self		:List[str]  ,_UpperCamelCase		:Optional[Any]			):
									return sym in self.indices





							@classmethod
							def 		a__   (  cls		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							List[Any]			       =						cls()
									d.add_from_file(_UpperCamelCase			)
									return d





							def 		a__   (  self		:int  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[Any]=1  ,_UpperCamelCase		:List[Any]=False			):
									if word in self.indices and not overwrite:
											snake_case_		:							Optional[Any]			       =						self.indices[word]
											snake_case_		:							Optional[Any]			       =						self.count[idx] + n
											return idx
									else:
											snake_case_		:							List[Any]			       =						len(self.symbols			)
											snake_case_		:							Optional[Any]			       =						idx
											self.symbols.append(_UpperCamelCase			)
											self.count.append(_UpperCamelCase			)
											return idx





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Union[str, Any]			):
									return 0





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Union[str, Any]			):
									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

									snake_case_		:							List[Any]			       =						f.readlines()
									snake_case_		:							Tuple			       =						self._load_meta(_UpperCamelCase			)

									for line in lines[indices_start_line:]:
											try:
													snake_case_       ,      snake_case_		:							List[str]			       =						line.rstrip().rsplit(""" """  ,1			)
													if field == "#fairseq:overwrite":
															snake_case_		:							int			       =						True
															snake_case_       ,      snake_case_		:							Union[str, Any]			       =						line.rsplit(""" """  ,1			)
													else:
															snake_case_		:							Tuple			       =						False
													snake_case_		:							Any			       =						int(_UpperCamelCase			)
													snake_case_		:							Tuple			       =						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      (		lowerCamelCase_  :int ):







		'''simple docstring'''
		# (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}
		snake_case_		:							Union[str, Any]			       =						dict((re.sub(R"""@@$"""				,      """"""				,      lowerCamelCase_ ), v) if k.endswith("""@@""" ) else (re.sub(R"""$"""				,      """</w>"""				,      lowerCamelCase_ ), v) for k, v in d.items() )
		snake_case_		:							Optional[int]			       =						"""<s> <pad> </s> <unk>""".split()
		# restore the special tokens
		for k in keep_keys:
				del da[F'''{k}</w>''']
				snake_case_		:							Union[str, Any]			       =						d[k]  # restore
		return da





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		# prep
		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

		snake_case_		:							Optional[int]			       =						os.path.join(lowerCamelCase_				,      """checkpoint.pt""" )
		if not os.path.isfile(lowerCamelCase_ ):
				raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' )
		snake_case_		:							Tuple			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )

		snake_case_		:							int			       =						chkpt["""cfg"""]["""model"""]

		# dicts
		snake_case_		:							Optional[int]			       =						os.path.join(lowerCamelCase_				,      """dict.txt""" )
		if not os.path.isfile(lowerCamelCase_ ):
				raise ValueError(F'''path to the file {dict_file} does not exist!''' )
		snake_case_		:							int			       =						Dictionary.load(lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						rewrite_dict_keys(src_dict.indices )
		snake_case_		:							Any			       =						len(lowerCamelCase_ )
		snake_case_		:							Any			       =						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)
		snake_case_		:							int			       =						os.path.join(lowerCamelCase_				,      """bpecodes""" )
		if not os.path.isfile(lowerCamelCase_ ):
				raise ValueError(F'''path to the file {bpecodes_file} does not exist!''' )

		snake_case_		:							List[Any]			       =						os.path.join(lowerCamelCase_				,      VOCAB_FILES_NAMES["""merges_file"""] )
		shutil.copyfile(lowerCamelCase_				,      lowerCamelCase_ )

		# model config
		snake_case_		:							Any			       =						os.path.join(lowerCamelCase_				,      """config.json""" )

		snake_case_		:							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-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
		snake_case_		:							Tuple			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )

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

		# model
		snake_case_		:							Optional[int]			       =						chkpt["""model"""]

		# remove unneeded keys
		snake_case_		:							List[str]			       =						[
		    """decoder.version""",
		]
		for k in ignore_keys:
				model_state_dict.pop(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_		:							List[Any]			       =						list(model_state_dict.keys() )
		for layer_name in layer_names:
				if layer_name.endswith("""output_projection.weight""" ):
						snake_case_		:							Optional[int]			       =						model_state_dict.pop(lowerCamelCase_ )
				else:
						snake_case_		:							Optional[int]			       =						model_state_dict.pop(lowerCamelCase_ )

		snake_case_		:							Dict			       =						BioGptConfig.from_pretrained(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						BioGptForCausalLM(lowerCamelCase_ )

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

		# save
		snake_case_		:							Optional[Any]			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		print(F'''Generating {pytorch_weights_dump_path}''' )
		torch.save(lowerCamelCase_				,      lowerCamelCase_ )

		print("""Conversion is done!""" )


if __name__ == "__main__":
							__A	:	Tuple     =     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	:	Dict     =     parser.parse_args()
							convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A	:	Optional[int]     =     logging.get_logger(__name__)

__A	:	List[Any]     =     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'luke'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Tuple=5_0_2_6_7  ,_UpperCamelCase		:Tuple=5_0_0_0_0_0  ,_UpperCamelCase		:str=7_6_8  ,_UpperCamelCase		:List[Any]=2_5_6  ,_UpperCamelCase		:List[str]=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:Union[str, Any]=3_0_7_2  ,_UpperCamelCase		:int="gelu"  ,_UpperCamelCase		:int=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Dict=5_1_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:int=0.02  ,_UpperCamelCase		:Any=1E-1_2  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Any=1  ,_UpperCamelCase		:int=0  ,_UpperCamelCase		:List[Any]=2  ,**_UpperCamelCase		:str  ,):
									super().__init__(pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Any			       =						vocab_size
									snake_case_		:							List[Any]			       =						entity_vocab_size
									snake_case_		:							Optional[Any]			       =						hidden_size
									snake_case_		:							List[Any]			       =						entity_emb_size
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Union[str, Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						hidden_act
									snake_case_		:							Dict			       =						intermediate_size
									snake_case_		:							Any			       =						hidden_dropout_prob
									snake_case_		:							List[Any]			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[Any]			       =						max_position_embeddings
									snake_case_		:							Any			       =						type_vocab_size
									snake_case_		:							int			       =						initializer_range
									snake_case_		:							Union[str, Any]			       =						layer_norm_eps
									snake_case_		:							Tuple			       =						use_entity_aware_attention
									snake_case_		:							Tuple			       =						classifier_dropout