



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()

snake_case    :						Tuple  =						2




class 							lowerCAmelCase__  :







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







					def __eq__(       self      :     Dict			,		_A      :     Union[str, Any]):
											return self.indices == other.indices







					def __getitem__(       self      :     Dict			,		_A      :     Tuple):






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







					def __len__(       self      :     Optional[Any]):
											return len(self.symbols)







					def __contains__(       self      :     Tuple			,		_A      :     str):
											return sym in self.indices







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







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     str			,		_A      :     List[Any]=1			,		_A      :     Any=False):






											if word in self.indices and not overwrite:
																	A__	:							Dict						    = self.indices[word]
																	A__	:							Dict						    = self.count[idx] + n
																	return idx
											else:
																	A__	:							Any						    = len(self.symbols)
																	A__	:							Dict						    = idx
																	self.symbols.append(UpperCamelCase__)
																	self.count.append(UpperCamelCase__)
																	return idx







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     int):
											return 0







					def  _lowercase			(       self      :     str			,		_A      :     Dict):






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

											A__	:							List[str]						    = f.readlines()
											A__	:							Tuple						    = self._load_meta(UpperCamelCase__)

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




def 	snake_case__       (					__lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

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







						"""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__	:							Optional[int]						    = 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__	:							Optional[Any]						    = torch.load(A_							,     map_location="cpu"       )

						A__	:							List[str]						    = chkpt["cfg"]["model"]

						# dicts
						A__	:							Any						    = 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__	:							Optional[int]						    = Dictionary.load(A_       )
						A__	:							str						    = rewrite_dict_keys(src_dict.indices       )
						A__	:							List[str]						    = len(A_       )
						A__	:							str						    = 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__	:							Tuple						    = os.path.join(A_							,     "bpecodes"       )
						if not os.path.isfile(A_       ):
												raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

						A__	:							str						    = os.path.join(A_							,     VOCAB_FILES_NAMES["merges_file"]       )
						shutil.copyfile(A_							,     A_       )

						# model config
						A__	:							List[str]						    = os.path.join(A_							,     "config.json"       )

						A__	:							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": 0.02,
						    "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__	:							str						    = os.path.join(A_							,     A_       )

						A__	:							int						    = {
						    "bos_token": "<s>",
						    "eos_token": "</s>",
						    "model_max_length": 1_0_2_4,
						    "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__	:							int						    = chkpt["model"]

						# remove unneeded keys
						A__	:							Union[str, Any]						    = [
						    "decoder.version",
						]
						for k in ignore_keys:
												model_state_dict.pop(A_							,     A_       )

						A__	:							List[str]						    = list(model_state_dict.keys()       )
						for layer_name in layer_names:
												if layer_name.endswith("output_projection.weight"       ):
																		A__	:							Union[str, Any]						    = model_state_dict.pop(A_       )
												else:
																		A__	:							str						    = model_state_dict.pop(A_       )

						A__	:							Tuple						    = BioGptConfig.from_pretrained(A_       )
						A__	:							Dict						    = BioGptForCausalLM(A_       )

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

						# save
						A__	:							List[str]						    = os.path.join(A_							,     A_       )
						print(F'Generating {pytorch_weights_dump_path}'       )
						torch.save(A_							,     A_       )

						print("Conversion is done!"       )


if __name__ == "__main__":
		snake_case    :						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.'
		)
		snake_case    :						List[str]  =						parser.parse_args()
		convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
		import ctypes
		import msvcrt  # noqa




		class 							lowerCAmelCase__  (	ctypes.Structure						):
							# _fields is a specific attr expected by ctypes
							__A     :    Optional[Any]           =						[('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 	snake_case__       (					)		->		List[Any]:







						"""simple docstring"""

						if os.name == "nt":
												A__	:							Optional[Any]						    = CursorInfo()
												A__	:							Tuple						    = ctypes.windll.kernelaa.GetStdHandle(-1_1       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
												A__	:							Any						    = False
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
						elif os.name == "posix":
												sys.stdout.write("\033[?25l"       )
												sys.stdout.flush()
def 	snake_case__       (					)		->		Dict:







						"""simple docstring"""

						if os.name == "nt":
												A__	:							List[str]						    = CursorInfo()
												A__	:							Optional[int]						    = ctypes.windll.kernelaa.GetStdHandle(-1_1       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
												A__	:							int						    = True
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
						elif os.name == "posix":
												sys.stdout.write("\033[?25h"       )
												sys.stdout.flush()




@contextmanager
def 	snake_case__       (					)		->		Optional[int]:







						"""simple docstring"""

						try:
												hide_cursor()
												yield
						finally:
												show_cursor()