


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()

__lowerCAmelCase      :					Union[str, Any]    							=2
class 			_lowercase :






				'''simple docstring'''



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


				def __eq__(			self     :Any     ,     lowerCAmelCase__     :List[str]      )						->     int:
										return self.indices == other.indices


				def __getitem__(			self     :List[str]     ,     lowerCAmelCase__     :Any      )						->     str:
										if idx < len(self.symbols      ):
																return self.symbols[idx]
										return self.unk_word


				def __len__(			self     :str      )						->     List[Any]:
										return len(self.symbols      )


				def __contains__(			self     :Optional[int]     ,     lowerCAmelCase__     :List[Any]      )						->     Union[str, Any]:
										return sym in self.indices


				@classmethod
				def 						__magic_name__(			cls     :int     ,     lowerCAmelCase__     :Tuple      )						->     List[str]:
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						cls()
										d.add_from_file(snake_case_      )
										return d


				def 						__magic_name__(			self     :int     ,     lowerCAmelCase__     :Any     ,     lowerCAmelCase__     :Union[str, Any]=1     ,     lowerCAmelCase__     :Optional[Any]=False      )						->     Any:
										if word in self.indices and not overwrite:
																__SCREAMING_SNAKE_CASE		:	Tuple						     =						self.indices[word]
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						self.count[idx] + n
																return idx
										else:
																__SCREAMING_SNAKE_CASE		:	Any						     =						len(self.symbols      )
																__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						idx
																self.symbols.append(snake_case_      )
																self.count.append(snake_case_      )
																return idx


				def 						__magic_name__(			self     :Optional[int]     ,     lowerCAmelCase__     :Optional[int]      )						->     Dict:
										return 0



				def 						__magic_name__(			self     :str     ,     lowerCAmelCase__     :Dict      )						->     List[Any]:
										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

										__SCREAMING_SNAKE_CASE		:	int						     =						f.readlines()
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						self._load_meta(snake_case_      )

										for line in lines[indices_start_line:]:
																try:
																						__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE		:	Optional[int]						     =						line.rstrip().rsplit(''' '''     ,     1      )
																						if field == "#fairseq:overwrite":
																												__SCREAMING_SNAKE_CASE		:	Any						     =						True
																												__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						line.rsplit(''' '''     ,     1      )
																						else:
																												__SCREAMING_SNAKE_CASE		:	List[Any]						     =						False
																						__SCREAMING_SNAKE_CASE		:	int						     =						int(snake_case_      )
																						__SCREAMING_SNAKE_CASE		:	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(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 			_UpperCamelCase				( 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}
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						dict((re.sub(R'''@@$'''       ,			''''''       ,			_lowerCAmelCase ), v) if k.endswith('''@@''' ) else (re.sub(R'''$'''       ,			'''</w>'''       ,			_lowerCAmelCase ), v) for k, v in d.items() )
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						'''<s> <pad> </s> <unk>'''.split()
						# restore the special tokens
						for k in keep_keys:
												del da[F'''{k}</w>''']
												__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						d[k]  # restore
						return da


def 			_UpperCamelCase				( lowercase__       ,			lowercase__ ):
						# 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

						__SCREAMING_SNAKE_CASE		:	List[str]						     =						os.path.join(_lowerCAmelCase       ,			'''checkpoint.pt''' )
						if not os.path.isfile(_lowerCAmelCase ):
												raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' )
						__SCREAMING_SNAKE_CASE		:	Dict						     =						torch.load(_lowerCAmelCase       ,			map_location='''cpu''' )

						__SCREAMING_SNAKE_CASE		:	str						     =						chkpt['''cfg''']['''model''']

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

						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						os.path.join(_lowerCAmelCase       ,			VOCAB_FILES_NAMES['''merges_file'''] )
						shutil.copyfile(_lowerCAmelCase       ,			_lowerCAmelCase )

						# model config
						__SCREAMING_SNAKE_CASE		:	str						     =						os.path.join(_lowerCAmelCase       ,			'''config.json''' )

						__SCREAMING_SNAKE_CASE		:	Optional[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
						__SCREAMING_SNAKE_CASE		:	str						     =						os.path.join(_lowerCAmelCase       ,			_lowerCAmelCase )

						__SCREAMING_SNAKE_CASE		:	int						     =						{
						    '''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
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						chkpt['''model''']

						# remove unneeded keys
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						[
						    '''decoder.version''',
						]
						for k in ignore_keys:
												model_state_dict.pop(_lowerCAmelCase       ,			_lowerCAmelCase )

						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						list(model_state_dict.keys() )
						for layer_name in layer_names:
												if layer_name.endswith('''output_projection.weight''' ):
																		__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						model_state_dict.pop(_lowerCAmelCase )
												else:
																		__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						model_state_dict.pop(_lowerCAmelCase )

						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						BioGptConfig.from_pretrained(_lowerCAmelCase )
						__SCREAMING_SNAKE_CASE		:	int						     =						BioGptForCausalLM(_lowerCAmelCase )

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

						# save
						__SCREAMING_SNAKE_CASE		:	str						     =						os.path.join(_lowerCAmelCase       ,			_lowerCAmelCase )
						print(F'''Generating {pytorch_weights_dump_path}''' )
						torch.save(_lowerCAmelCase       ,			_lowerCAmelCase )

						print('''Conversion is done!''' )


if __name__ == "__main__":
						__lowerCAmelCase      :					int    							=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.'
						)
						__lowerCAmelCase      :					str    							=parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
