
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  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							*,  # begin keyword-only arguments
	    SCREAMING_SNAKE_CASE_  :			Optional[int]="<s>"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="<pad>"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="</s>"       ,							SCREAMING_SNAKE_CASE_  :			Tuple="<unk>"       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							)       ->     Optional[int]:
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					bos, unk, pad, eos
					lowercase_       =					[]
					lowercase_       =					[]
					lowercase_       =					{}
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					if extra_special_symbols:
									for s in extra_special_symbols:
													self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					len(self.symbols       )
	def __eq__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[Any]:
					return self.indices == other.indices
	def __getitem__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Dict:
					if idx < len(self.symbols       ):
									return self.symbols[idx]
					return self.unk_word
	def __len__(       self  :			int       )       ->     int:
					return len(self.symbols       )
	def __contains__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[Any]:
					return sym in self.indices
	@classmethod
	def 						_lowercase  (       cls  :			str       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[int]:
					lowercase_       =					cls()
					d.add_from_file(SCREAMING_SNAKE_CASE_       )
					return d
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       )       ->     Optional[Any]:
					if word in self.indices and not overwrite:
									lowercase_       =					self.indices[word]
									lowercase_       =					self.count[idx] + n
									return idx
					else:
									lowercase_       =					len(self.symbols       )
									lowercase_       =					idx
									self.symbols.append(SCREAMING_SNAKE_CASE_       )
									self.count.append(SCREAMING_SNAKE_CASE_       )
									return idx
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     Tuple:
					return 0




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Tuple:
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									try:
													with open(SCREAMING_SNAKE_CASE_       ,							'''r'''       ,							encoding='''utf-8'''       ) as fd:
																	self.add_from_file(SCREAMING_SNAKE_CASE_       )
									except FileNotFoundError as fnfe:
													raise fnfe
									except UnicodeError:
													raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(SCREAMING_SNAKE_CASE_       )       )
									return

					lowercase_       =					f.readlines()
					lowercase_       =					self._load_meta(SCREAMING_SNAKE_CASE_       )

					for line in lines[indices_start_line:]:
									try:
													lowercase_  ,	lowercase_       =					line.rstrip().rsplit(''' '''       ,							1       )
													if field == "#fairseq:overwrite":
																	lowercase_       =					True
																	lowercase_  ,	lowercase_       =					line.rsplit(''' '''       ,							1       )
													else:
																	lowercase_       =					False
													lowercase_       =					int(SCREAMING_SNAKE_CASE_       )
													lowercase_       =					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(SCREAMING_SNAKE_CASE_       )       )
													self.add_symbol(SCREAMING_SNAKE_CASE_       ,							n=SCREAMING_SNAKE_CASE_       ,							overwrite=SCREAMING_SNAKE_CASE_       )
									except ValueError:
													raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''       )




def 				a						(							snake_case__:					Tuple					):


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




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Tuple					):


				'''simple docstring'''
				# prep
				if not os.path.exists(snake_case__					):
								raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''					)
				os.makedirs(snake_case__						,   exist_ok=snake_case__					)
				print(F'''Writing results to {pytorch_dump_folder_path}'''					)

				# handle various types of models

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

				lowercase_       =					chkpt['''cfg''']['''model''']

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

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

				lowercase_       =					os.path.join(snake_case__						,   VOCAB_FILES_NAMES['''merges_file''']					)
				shutil.copyfile(snake_case__						,   snake_case__					)

				# model config
				lowercase_       =					os.path.join(snake_case__						,   '''config.json'''					)

				lowercase_       =					{
				    '''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-1_2,
				    '''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(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
								f.write(json.dumps(snake_case__						,   ensure_ascii=snake_case__						,   indent=snake_case__					)					)

				# tokenizer config
				lowercase_       =					os.path.join(snake_case__						,   snake_case__					)

				lowercase_       =					{
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				    '''model_max_length''': 1_024,
				    '''pad_token''': '''<pad>''',
				    '''special_tokens_map_file''': None,
				    '''tokenizer_class''': '''BioGptTokenizer''',
				    '''unk_token''': '''<unk>''',
				}

				print(F'''Generating {biogpt_tokenizer_config_file}'''					)
				with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
								f.write(json.dumps(snake_case__						,   ensure_ascii=snake_case__						,   indent=snake_case__					)					)

				# model
				lowercase_       =					chkpt['''model''']

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

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

				lowercase_       =					BioGptConfig.from_pretrained(snake_case__					)
				lowercase_       =					BioGptForCausalLM(snake_case__					)

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

				# save
				lowercase_       =					os.path.join(snake_case__						,   snake_case__					)
				print(F'''Generating {pytorch_weights_dump_path}'''					)
				torch.save(snake_case__						,   snake_case__					)

				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)






import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




