


'''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()

__UpperCAmelCase					       =							2




class 	a__       :





							'''simple docstring'''






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







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







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







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







							def __contains__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														return sym in self.indices







							@classmethod
							def 						__SCREAMING_SNAKE_CASE							(	cls      ,					lowerCamelCase_     ) ->							int:
														lowerCAmelCase__      =			cls()
														d.add_from_file(lowerCamelCase_     )
														return d







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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														return 0







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														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__      =			f.readlines()
														lowerCAmelCase__      =			self._load_meta(lowerCamelCase_     )

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


def       _snake_case     (  A				,					A )		->					Optional[int]:
							# 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

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

							lowerCAmelCase__      =			chkpt['''cfg''']['''model''']

							# dicts
							lowerCAmelCase__      =			os.path.join(A				,					'''dict.txt''' )
							if not os.path.isfile(A ):
														raise ValueError(F"""path to the file {dict_file} does not exist!""" )
							lowerCAmelCase__      =			Dictionary.load(A )
							lowerCAmelCase__      =			rewrite_dict_keys(src_dict.indices )
							lowerCAmelCase__      =			len(A )
							lowerCAmelCase__      =			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)
							lowerCAmelCase__      =			os.path.join(A				,					'''bpecodes''' )
							if not os.path.isfile(A ):
														raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )

							lowerCAmelCase__      =			os.path.join(A				,					VOCAB_FILES_NAMES['''merges_file'''] )
							shutil.copyfile(A				,					A )

							# model config
							lowerCAmelCase__      =			os.path.join(A				,					'''config.json''' )

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

							# tokenizer config
							lowerCAmelCase__      =			os.path.join(A				,					A )

							lowerCAmelCase__      =			{
							    '''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
							lowerCAmelCase__      =			chkpt['''model''']

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

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

							lowerCAmelCase__      =			BioGptConfig.from_pretrained(A )
							lowerCAmelCase__      =			BioGptForCausalLM(A )

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

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

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


if __name__ == "__main__":
		__UpperCAmelCase					       =							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.'''
		)
		__UpperCAmelCase					       =							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


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Dict					=   "ctrl"
							lowercase__    :     str					=   ["past_key_values"]
							lowercase__    :     Optional[int]					=   {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}







							def __init__(	self      ,					lowerCamelCase_=24_65_34      ,					lowerCamelCase_=2_56      ,					lowerCamelCase_=12_80      ,					lowerCamelCase_=81_92      ,					lowerCamelCase_=48      ,					lowerCamelCase_=16      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=1e-6      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=True      ,					**lowerCamelCase_      ,					) ->							Union[str, Any]:
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			n_positions
														lowerCAmelCase__      =			n_embd
														lowerCAmelCase__      =			n_layer
														lowerCAmelCase__      =			n_head
														lowerCAmelCase__      =			dff
														lowerCAmelCase__      =			resid_pdrop
														lowerCAmelCase__      =			embd_pdrop
														lowerCAmelCase__      =			layer_norm_epsilon
														lowerCAmelCase__      =			initializer_range

														lowerCAmelCase__      =			use_cache

														super().__init__(**lowerCamelCase_     )