






'''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				:    Optional[Any]     						=2



class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Optional[Any]     ,  *,  # begin keyword-only arguments
					    UpperCamelCase_				:	Optional[Any]="<s>"     ,  UpperCamelCase_				:	Union[str, Any]="<pad>"     ,  UpperCamelCase_				:	Dict="</s>"     ,  UpperCamelCase_				:	Optional[Any]="<unk>"     ,  UpperCamelCase_				:	int=None     ,  )  ->       List[str]:



						'''simple docstring'''





						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		bos, unk, pad, eos
						_lowercase					:       List[Any]       =		[]
						_lowercase					:       Dict       =		[]
						_lowercase					:       List[str]       =		{}
						_lowercase					:       Union[str, Any]       =		self.add_symbol(_A					)
						_lowercase					:       Tuple       =		self.add_symbol(_A					)
						_lowercase					:       Optional[Any]       =		self.add_symbol(_A					)
						_lowercase					:       Tuple       =		self.add_symbol(_A					)
						if extra_special_symbols:
							for s in extra_special_symbols:
								self.add_symbol(_A					)
						_lowercase					:       Optional[Any]       =		len(self.symbols					)




					def __eq__(  self				:	Optional[Any]     ,  UpperCamelCase_				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						return self.indices == other.indices




					def __getitem__(  self				:	Tuple     ,  UpperCamelCase_				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





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




					def __len__(  self				:	List[Any]					)  ->       List[Any]:



						'''simple docstring'''





						return len(self.symbols					)




					def __contains__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[Any]					)  ->       Tuple:



						'''simple docstring'''





						return sym in self.indices




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[str]     ,  UpperCamelCase_				:	Any					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		cls()
						d.add_from_file(_A					)
						return d




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Union[str, Any]=1     ,  UpperCamelCase_				:	Union[str, Any]=False					)  ->       Union[str, Any]:



						'''simple docstring'''





						if word in self.indices and not overwrite:
							_lowercase					:       str       =		self.indices[word]
							_lowercase					:       Union[str, Any]       =		self.count[idx] + n
							return idx
						else:
							_lowercase					:       int       =		len(self.symbols					)
							_lowercase					:       Any       =		idx
							self.symbols.append(_A					)
							self.count.append(_A					)
							return idx




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						return 0




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict					)  ->       List[str]:



						'''simple docstring'''





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

						_lowercase					:       Optional[int]       =		f.readlines()
						_lowercase					:       Optional[Any]       =		self._load_meta(_A					)

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







def 						__UpperCamelCase				(							_lowercase					) ->			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}
	_lowercase					:       Tuple       =		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					:       Dict       =		'<s> <pad> </s> <unk>'.split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'''{k}</w>''']
		_lowercase					:       Dict       =		d[k]  # restore
	return da







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Any:
	# 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					:       Any       =		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					:       Dict       =		torch.load(__snake_case,				map_location='cpu'					)

	_lowercase					:       Dict       =		chkpt['cfg']['model']

	# dicts
	_lowercase					:       Tuple       =		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					:       int       =		Dictionary.load(__snake_case					)
	_lowercase					:       int       =		rewrite_dict_keys(src_dict.indices					)
	_lowercase					:       str       =		len(__snake_case					)
	_lowercase					:       Optional[int]       =		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					:       Any       =		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					:       List[Any]       =		os.path.join(__snake_case,				VOCAB_FILES_NAMES['merges_file']					)
	shutil.copyfile(__snake_case,				__snake_case					)

	# model config
	_lowercase					:       List[Any]       =		os.path.join(__snake_case,				'config.json'					)

	_lowercase					:       Optional[int]       =		{
	    '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					:       List[Any]       =		os.path.join(__snake_case,				__snake_case					)

	_lowercase					:       Optional[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(__snake_case,				'w',				encoding='utf-8'					) as f:
		f.write(json.dumps(__snake_case,				ensure_ascii=__snake_case,				indent=__snake_case					)					)

	# model
	_lowercase					:       int       =		chkpt['model']

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

	_lowercase					:       Optional[int]       =		list(model_state_dict.keys()					)
	for layer_name in layer_names:
		if layer_name.endswith('output_projection.weight'					):
			_lowercase					:       Dict       =		model_state_dict.pop(__snake_case					)
		else:
			_lowercase					:       str       =		model_state_dict.pop(__snake_case					)

	_lowercase					:       Optional[Any]       =		BioGptConfig.from_pretrained(__snake_case					)
	_lowercase					:       List[Any]       =		BioGptForCausalLM(__snake_case					)

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

	# save
	_lowercase					:       Any       =		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				:    List[str]     						=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				:    Tuple     						=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


_A				:    Dict     						=logging.get_logger(__name__)

_A				:    Dict     						={
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""megatron-bert"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int=2_9056     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=24     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=4096     ,  UpperCamelCase_				:	Optional[Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	int=512     ,  UpperCamelCase_				:	Dict=2     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	Any=1E-12     ,  UpperCamelCase_				:	Tuple=0     ,  UpperCamelCase_				:	Optional[int]="absolute"     ,  UpperCamelCase_				:	Optional[Any]=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[Any]:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Union[str, Any]       =		num_hidden_layers
						_lowercase					:       Dict       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Optional[Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       str       =		type_vocab_size
						_lowercase					:       Optional[Any]       =		initializer_range
						_lowercase					:       List[str]       =		layer_norm_eps
						_lowercase					:       List[Any]       =		position_embedding_type
						_lowercase					:       Optional[Any]       =		use_cache
