






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			:	Union[str, Any]								=2






class 						UpperCAmelCase__ :







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







		def __eq__(					self       ,A__		):
						return self.indices == other.indices







		def __getitem__(					self       ,A__		):
						if idx < len(self.symbols		):
										return self.symbols[idx]
						return self.unk_word







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







		def __contains__(					self       ,A__		):
						return sym in self.indices







		@classmethod
		def  A__      (					cls       ,A__		):
						_A	:				List[Any]   =      cls()
						d.add_from_file(A__		)
						return d







		def  A__      (					self       ,A__       ,A__=1       ,A__=False		):
						if word in self.indices and not overwrite:
										_A	:				Tuple   =      self.indices[word]
										_A	:				Union[str, Any]   =      self.count[idx] + n
										return idx
						else:
										_A	:				Optional[Any]   =      len(self.symbols		)
										_A	:				str   =      idx
										self.symbols.append(A__		)
										self.count.append(A__		)
										return idx







		def  A__      (					self       ,A__		):
						return 0







		def  A__      (					self       ,A__		):
						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

						_A	:				List[str]   =      f.readlines()
						_A	:				int   =      self._load_meta(A__		)

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



def  a__ (__lowercase							:List[Any]					,  __lowercase							:Union[str, Any]   )		-> Optional[Any]:
				# prep
				if not os.path.exists(__lowercase   ):
								raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""   )
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				print(f"""Writing results to {pytorch_dump_folder_path}"""   )

				# handle various types of models

				_A	:				Union[str, Any]   =      os.path.join(__lowercase					,  '''checkpoint.pt'''   )
				if not os.path.isfile(__lowercase   ):
								raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""   )
				_A	:				Tuple   =      torch.load(__lowercase					,  map_location='''cpu'''   )

				_A	:				Optional[int]   =      chkpt['''cfg''']['''model''']

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

				# merges_file (bpecodes)
				_A	:				int   =      os.path.join(__lowercase					,  '''bpecodes'''   )
				if not os.path.isfile(__lowercase   ):
								raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""   )

				_A	:				Any   =      os.path.join(__lowercase					,  VOCAB_FILES_NAMES['''merges_file''']   )
				shutil.copyfile(__lowercase					,  __lowercase   )

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

				_A	:				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''': 0.02,
				    '''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(__lowercase					,  '''w'''					,  encoding='''utf-8'''   ) as f:
								f.write(json.dumps(__lowercase					,  ensure_ascii=__lowercase					,  indent=__lowercase   )   )

				# tokenizer config
				_A	:				List[str]   =      os.path.join(__lowercase					,  __lowercase   )

				_A	:				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(__lowercase					,  '''w'''					,  encoding='''utf-8'''   ) as f:
								f.write(json.dumps(__lowercase					,  ensure_ascii=__lowercase					,  indent=__lowercase   )   )

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

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

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

				_A	:				Any   =      BioGptConfig.from_pretrained(__lowercase   )
				_A	:				Dict   =      BioGptForCausalLM(__lowercase   )

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

				# save
				_A	:				Optional[Any]   =      os.path.join(__lowercase					,  __lowercase   )
				print(f"""Generating {pytorch_weights_dump_path}"""   )
				torch.save(__lowercase					,  __lowercase   )

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


if __name__ == "__main__":
	_UpperCamelCase			:	Optional[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.'
	)
	_UpperCamelCase			:	Optional[int]								=parser.parse_args()
	convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

	_UpperCamelCase			:	str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



