



"""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()

snake_case_    					=	2







class 				A_      :



						"""simple docstring"""






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






						def __eq__( self			:Optional[Any]       ,  lowercase_			:Optional[int] )    ->      Dict:
												return self.indices == other.indices






						def __getitem__( self			:List[Any]       ,  lowercase_			:Dict )    ->      Optional[int]:




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






						def __len__( self			:int )    ->      str:
												return len(self.symbols )






						def __contains__( self			:List[str]       ,  lowercase_			:Tuple )    ->      Any:
												return sym in self.indices






						@classmethod
						def      UpperCAmelCase__					( cls			:Optional[Any]       ,  lowercase_			:Dict )    ->      List[Any]:
												UpperCAmelCase					=			cls()
												d.add_from_file(_snake_case )
												return d






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Tuple       ,  lowercase_			:Tuple=1       ,  lowercase_			:Union[str, Any]=False )    ->      List[Any]:




												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(_snake_case )
																		self.count.append(_snake_case )
																		return idx






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Union[str, Any] )    ->      List[Any]:
												return 0






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:List[str] )    ->      Tuple:




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

												UpperCAmelCase					=			f.readlines()
												UpperCAmelCase					=			self._load_meta(_snake_case )

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


def 			_lowerCAmelCase				(       lowercase_						):
						# (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}
						UpperCAmelCase					=			dict((re.sub(R'@@$'		,     ''		,     a__						), v) if k.endswith('@@'						) else (re.sub(R'$'		,     '</w>'		,     a__						), 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 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						# prep
						if not os.path.exists(a__						):
												raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!"""						)
						os.makedirs(a__		,     exist_ok=a__						)
						print(F"""Writing results to {pytorch_dump_folder_path}"""						)

						# handle various types of models

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

						UpperCAmelCase					=			chkpt['cfg']['model']

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

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

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

						# model config
						UpperCAmelCase					=			os.path.join(a__		,     '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': 0.0_2,
						    '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(a__		,     'w'		,     encoding='utf-8'						) as f:
												f.write(json.dumps(a__		,     ensure_ascii=a__		,     indent=a__						)						)

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

						UpperCAmelCase					=			{
						    '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(a__		,     'w'		,     encoding='utf-8'						) as f:
												f.write(json.dumps(a__		,     ensure_ascii=a__		,     indent=a__						)						)

						# model
						UpperCAmelCase					=			chkpt['model']

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

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

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

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

						# save
						UpperCAmelCase					=			os.path.join(a__		,     a__						)
						print(F"""Generating {pytorch_weights_dump_path}"""						)
						torch.save(a__		,     a__						)

						print('Conversion is done!'						)


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










"""simple docstring"""


import operator as op


snake_case_    					=	"""scaler.pt"""
snake_case_    					=	"""pytorch_model"""
snake_case_    					=	"""random_states"""
snake_case_    					=	"""optimizer"""
snake_case_    					=	"""scheduler"""
snake_case_    					=	"""pytorch_model.bin"""
snake_case_    					=	"""pytorch_model.bin.index.json"""
snake_case_    					=	"""model.safetensors"""
snake_case_    					=	"""model.safetensors.index.json"""
snake_case_    					=	"""1.10.2"""
snake_case_    					=	"""py38"""
snake_case_    					=	"""4.17.0"""
snake_case_    					=	["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
snake_case_    					=	["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
snake_case_    					=	["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
snake_case_    					=	["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
snake_case_    					=	["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
snake_case_    					=	"""2.0.1"""
snake_case_    					=	["""pdsh""", """standard""", """openmpi""", """mvapich"""]
snake_case_    					=	["""default""", """reduce-overhead""", """max-autotune"""]

snake_case_    					=	{""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
snake_case_    					=	[
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

snake_case_    					=	["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
snake_case_    					=	["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]





