"""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  =				2
class  _snake_case     :






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






						def __eq__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													return self.indices == other.indices






						def __getitem__(       self      :			Dict							,					UpperCAmelCase      :			Optional[int]    ):
													if idx < len(self.symbols    ):
																				return self.symbols[idx]
													return self.unk_word






						def __len__(       self      :			Dict    ):
													return len(self.symbols    )






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






						@classmethod
						def       lowerCamelCase__     (       cls      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		List[Any]							   =     cls()
													d.add_from_file(lowerCamelCase_    )
													return d






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






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Union[str, Any]    ):
													return 0







						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			Dict    ):
													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

													__lowerCamelCase					:		List[Any]							   =     f.readlines()
													__lowerCamelCase					:		Tuple							   =     self._load_meta(lowerCamelCase_    )

													for line in lines[indices_start_line:]:
																				try:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int							   =     line.rstrip().rsplit(" "							,					1    )
																											if field == "#fairseq:overwrite":
																																		__lowerCamelCase					:		Union[str, Any]							   =     True
																																		__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]							   =     line.rsplit(" "							,					1    )
																											else:
																																		__lowerCamelCase					:		List[str]							   =     False
																											__lowerCamelCase					:		Union[str, Any]							   =     int(lowerCamelCase_    )
																											__lowerCamelCase					:		int							   =     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    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Optional[int]:



							'''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Union[str, Any]      ) ->      Optional[int]:



							'''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

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

							__lowerCamelCase					:		Optional[Any]							   =     chkpt["cfg"]["model"]

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

							__lowerCamelCase					:		int							   =     os.path.join(_lowerCamelCase						,   VOCAB_FILES_NAMES["merges_file"]      )
							shutil.copyfile(_lowerCamelCase						,   _lowerCamelCase      )

							# model config
							__lowerCamelCase					:		Union[str, Any]							   =     os.path.join(_lowerCamelCase						,   "config.json"      )

							__lowerCamelCase					:		List[str]							   =     {
							    "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
							__lowerCamelCase					:		Union[str, Any]							   =     os.path.join(_lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		List[Any]							   =     {
							    "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(_lowerCamelCase						,   "w"						,   encoding="utf-8"      ) as f:
														f.write(json.dumps(_lowerCamelCase						,   ensure_ascii=_lowerCamelCase						,   indent=_lowerCamelCase      )      )

							# model
							__lowerCamelCase					:		Optional[Any]							   =     chkpt["model"]

							# remove unneeded keys
							__lowerCamelCase					:		List[Any]							   =     [
							    "decoder.version",
							]
							for k in ignore_keys:
														model_state_dict.pop(_lowerCamelCase						,   _lowerCamelCase      )

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

							__lowerCamelCase					:		Optional[Any]							   =     BioGptConfig.from_pretrained(_lowerCamelCase      )
							__lowerCamelCase					:		str							   =     BioGptForCausalLM(_lowerCamelCase      )

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

							# save
							__lowerCamelCase					:		List[str]							   =     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)
"""simple docstring"""





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class  _snake_case     :
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			True
						snake_case__			  =			None
						snake_case__			  =			1
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None






						def       lowerCamelCase__     (       self      :			Any    ):
													return self.__class__(**{k: copy.deepcopy(UpperCAmelCase    ) for k, v in self.__dict__.items()}    )