

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_       : Union[str, Any]         =			2



class     _a						:


					'''simple docstring'''






					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__							:    Dict        =		[]
						A__							:    str        =		[]
						A__							:    List[Any]        =		{}
						A__							:    Optional[Any]        =		self.add_symbol(A__			)
						A__							:    Tuple        =		self.add_symbol(A__			)
						A__							:    Tuple        =		self.add_symbol(A__			)
						A__							:    Optional[int]        =		self.add_symbol(A__			)
						if extra_special_symbols:
							for s in extra_special_symbols:
								self.add_symbol(A__			)
						A__							:    List[str]        =		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__							:    Union[str, 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__							:    Dict        =		self.indices[word]
							A__							:    str        =		self.count[idx] + n
							return idx
						else:
							A__							:    List[str]        =		len(self.symbols			)
							A__							:    Union[str, Any]        =		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__							:    int        =		f.readlines()
						A__							:    List[Any]        =		self._load_meta(A__			)

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

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Any					)     ->      Tuple:
	# 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__							:    List[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__							:    Any        =		chkpt["""cfg"""]["""model"""]

	# dicts
	A__							:    List[Any]        =		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__							:    str        =		Dictionary.load(lowercase_					)
	A__							:    Union[str, Any]        =		rewrite_dict_keys(src_dict.indices					)
	A__							:    Any        =		len(lowercase_					)
	A__							:    Optional[int]        =		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__							:    Any        =		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__							:    str        =		os.path.join(lowercase_    ,      """config.json"""					)

	A__							:    List[str]        =		{
	    """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__							:    Optional[int]        =		os.path.join(lowercase_    ,      lowercase_					)

	A__							:    List[Any]        =		{
	    """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__							:    List[Any]        =		chkpt["""model"""]

	# remove unneeded keys
	A__							:    Optional[int]        =		[
	    """decoder.version""",
	]
	for k in ignore_keys:
		model_state_dict.pop(lowercase_    ,      lowercase_					)

	A__							:    int        =		list(model_state_dict.keys()					)
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""					):
			A__							:    Tuple        =		model_state_dict.pop(lowercase_					)
		else:
			A__							:    Any        =		model_state_dict.pop(lowercase_					)

	A__							:    List[Any]        =		BioGptConfig.from_pretrained(lowercase_					)
	A__							:    Dict        =		BioGptForCausalLM(lowercase_					)

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

	# save
	A__							:    Union[str, Any]        =		os.path.join(lowercase_    ,      lowercase_					)
	print(f"""Generating {pytorch_weights_dump_path}"""					)
	torch.save(lowercase_    ,      lowercase_					)

	print("""Conversion is done!"""					)


if __name__ == "__main__":
	A_       : Dict         =			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_       : str         =			parser.parse_args()
	convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



