




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 					snake_case__							:
				"""simple docstring"""



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





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





				def __getitem__(   self       :  Any      ,   __lowerCamelCase       :  Optional[int]							)      ->  Dict:
							if idx < len(self.symbols							):
										return self.symbols[idx]
							return self.unk_word





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





				def __contains__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  Optional[Any]:
							return sym in self.indices





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  Union[str, Any]      ,   __lowerCamelCase       :  List[str]							)      ->  Tuple:
							a					=    cls()
							d.add_from_file(__lowerCamelCase							)
							return d





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[Any]=1      ,   __lowerCamelCase       :  int=False							)      ->  int:
							if word in self.indices and not overwrite:
										a					=    self.indices[word]
										a					=    self.count[idx] + n
										return idx
							else:
										a					=    len(self.symbols							)
										a					=    idx
										self.symbols.append(__lowerCamelCase							)
										self.count.append(__lowerCamelCase							)
										return idx





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Any							)      ->  Dict:
							return 0





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

							a					=    f.readlines()
							a					=    self._load_meta(__lowerCamelCase							)

							for line in lines[indices_start_line:]:
										try:
													a					,	a					=    line.rstrip().rsplit(" "      ,   1							)
													if field == "#fairseq:overwrite":
																a					=    True
																a					,	a					=    line.rsplit(" "      ,   1							)
													else:
																a					=    False
													a					=    int(__lowerCamelCase							)
													a					=    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     __magic_name__       (				A	:      Optional[Any]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Optional[Any],						A	:      Any     ):






			'''simple docstring'''







			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

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

			a					=    chkpt["cfg"]["model"]

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

			a					=    os.path.join(A,						VOCAB_FILES_NAMES["merges_file"]     )
			shutil.copyfile(A,						A     )

			# model config
			a					=    os.path.join(A,						"config.json"     )

			a					=    {
			    "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
			a					=    os.path.join(A,						A     )

			a					=    {
			    "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
			a					=    chkpt["model"]

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

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

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

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

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

			print("Conversion is done!"     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Optional[Any]     =	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	:       Tuple     =	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_tokenizers_available, is_torch_available


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

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






