code: 
  
  import json
  import os
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCAmelCase     :			Tuple    =logging.get_logger(__name__)
  
  _lowerCAmelCase     :			List[Any]    ={
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  _lowerCAmelCase     :			Any    ={
      """vocab_file""": {
          """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
      },
      """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
  }
  
  _lowerCAmelCase     :			List[Any]    ={
      """microsoft/biogpt""": 10_24,
  }
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Tuple		       =   set()
  	UpperCAmelCase__:  Any		       =   word[0]
  	for char in word[1:]:
  		pairs.add((prev_char, char)			)
  		UpperCAmelCase__:  Union[str, Any]		       =   char
  	return pairs
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      VOCAB_FILES_NAMES
  					__magic_name__     			=      PRETRAINED_VOCAB_FILES_MAP
  					__magic_name__     			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					__magic_name__     			=      ["input_ids", "attention_mask"]
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__="<unk>"							,			lowerCamelCase__="<s>"							,			lowerCamelCase__="</s>"							,			lowerCamelCase__="</s>"							,			lowerCamelCase__="<pad>"							,			**lowerCamelCase__							,			):
  						super().__init__(
  						    bos_token=lowerCamelCase__							,			eos_token=lowerCamelCase__							,			sep_token=lowerCamelCase__							,			unk_token=lowerCamelCase__							,			pad_token=lowerCamelCase__							,			**lowerCamelCase__							,			)
  
  						try:
  							import sacremoses
  						except ImportError:
  							raise ImportError(
  							    "You need to install sacremoses to use BioGptTokenizer. "
  							    "See https://pypi.org/project/sacremoses/ for installation."	)
  
  						UpperCAmelCase__:  List[str]		       =   "en"
  						UpperCAmelCase__:  str		       =   sacremoses
  						# cache of sm.MosesTokenizer instance
  						UpperCAmelCase__:  int		       =   {}
  						UpperCAmelCase__:  Optional[int]		       =   {}
  						with open(lowerCamelCase__							,			encoding="utf-8"	) as vocab_handle:
  							UpperCAmelCase__:  Tuple		       =   json.load(lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   {v: k for k, v in self.encoder.items()}
  						with open(lowerCamelCase__							,			encoding="utf-8"	) as merges_handle:
  							UpperCAmelCase__:  Union[str, Any]		       =   merges_handle.read().split("\n"	)[:-1]
  						UpperCAmelCase__:  Optional[Any]		       =   [tuple(merge.split()[:2]	) for merge in merges]
  						UpperCAmelCase__:  Tuple		       =   dict(zip(lowerCamelCase__							,			range(len(lowerCamelCase__	)	)	)	)
  						UpperCAmelCase__:  str		       =   {}
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return len(self.encoder	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return dict(self.encoder							,			**self.added_tokens_encoder	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						if lang not in self.cache_moses_tokenizer:
  							UpperCAmelCase__:  Any		       =   self.sm.MosesTokenizer(lang=lowerCamelCase__	)
  							UpperCAmelCase__:  Union[str, Any]		       =   moses_tokenizer
  						return self.cache_moses_tokenizer[lang].tokenize(
  						    lowerCamelCase__							,			aggressive_dash_splits=lowerCamelCase__							,			return_str=lowerCamelCase__							,			escape=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						if lang not in self.cache_moses_detokenizer:
  							UpperCAmelCase__:  str		       =   self.sm.MosesDetokenizer(lang=lowerCamelCase__	)
  							UpperCAmelCase__:  List[Any]		       =   moses_detokenizer
  						return self.cache_moses_detokenizer[lang].detokenize(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[Any]		       =   tuple(token[:-1]	) + (token[-1] + "</w>",)
  						if token in self.cache:
  							return self.cache[token]
  						UpperCAmelCase__:  Optional[Any]		       =   get_pairs(lowerCamelCase__	)
  
  						if not pairs:
  							return token + "</w>"
  
  						while True:
  							UpperCAmelCase__:  Union[str, Any]		       =   min(lowerCamelCase__							,			key=lambda lowerCamelCase__	: self.bpe_ranks.get(lowerCamelCase__							,			float("inf"	)	)	)
  							if bigram not in self.bpe_ranks:
  								break
  							UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   bigram
  							UpperCAmelCase__:  Optional[Any]		       =   []
  							UpperCAmelCase__:  List[str]		       =   0
  							while i < len(lowerCamelCase__	):
  								try:
  									UpperCAmelCase__:  int		       =   word.index(lowerCamelCase__							,			lowerCamelCase__	)
  								except ValueError:
  									new_word.extend(word[i:]	)
  									break
  								else:
  									new_word.extend(word[i:j]	)
  									UpperCAmelCase__:  int		       =   j
  
  								if word[i] == first and i < len(lowerCamelCase__	) - 1 and word[i + 1] == second:
  									new_word.append(first + second	)
  									i += 2
  								else:
  									new_word.append(word[i]	)
  									i += 1
  							UpperCAmelCase__:  Any		       =   tuple(lowerCamelCase__	)
  							UpperCAmelCase__:  Any		       =   new_word
  							if len(lowerCamelCase__	) == 1:
  								break
  							else:
  								UpperCAmelCase__:  Any		       =   get_pairs(lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   " ".join(lowerCamelCase__	)
  						if word == "\n  </w>":
  							UpperCAmelCase__:  Tuple		       =   "\n</w>"
  						UpperCAmelCase__:  Optional[int]		       =   word
  						return word
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__=False	):
  						if bypass_tokenizer:
  							UpperCAmelCase__:  List[str]		       =   text.split()
  						else:
  							UpperCAmelCase__:  int		       =   self.moses_tokenize(lowerCamelCase__							,			self.lang	)
  
  						UpperCAmelCase__:  str		       =   []
  						for token in text:
  							if token:
  								split_tokens.extend(list(self.bpe(lowerCamelCase__	).split(" "	)	)	)
  
  						return split_tokens
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						return self.encoder.get(lowerCamelCase__							,			self.encoder.get(self.unk_token	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						return self.decoder.get(lowerCamelCase__							,			self.unk_token	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[Any]		       =   [t.replace(" "							,			""	).replace("</w>"							,			" "	) for t in tokens]
  						UpperCAmelCase__:  Any		       =   "".join(lowerCamelCase__	).split()
  						# detokenize
  						UpperCAmelCase__:  Any		       =   self.moses_detokenize(lowerCamelCase__							,			self.lang	)
  						return text
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None	):
  						if token_ids_a is None:
  							return [self.sep_token_id] + token_ids_a
  						UpperCAmelCase__:  str		       =   [self.sep_token_id]
  						return sep + token_ids_a + sep + token_ids_a
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None							,			lowerCamelCase__ = False	):
  						if already_has_special_tokens:
  							return super().get_special_tokens_mask(
  							    token_ids_a=lowerCamelCase__							,			token_ids_a=lowerCamelCase__							,			already_has_special_tokens=lowerCamelCase__	)
  						# no bos used in fairseq
  						if token_ids_a is not None:
  							return [1] + ([0] * len(lowerCamelCase__	)) + [1] + ([0] * len(lowerCamelCase__	))
  						return [1] + ([0] * len(lowerCamelCase__	))
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None	):
  						UpperCAmelCase__:  List[str]		       =   [self.sep_token_id]
  
  						# no bos used in fairseq
  						if token_ids_a is None:
  							return len(token_ids_a + sep	) * [0]
  						return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None	):
  						if not os.path.isdir(lowerCamelCase__	):
  							logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
  							return
  						UpperCAmelCase__:  Optional[int]		       =   os.path.join(
  						    lowerCamelCase__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  						UpperCAmelCase__:  Union[str, Any]		       =   os.path.join(
  						    lowerCamelCase__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)
  
  						with open(lowerCamelCase__							,			"w"							,			encoding="utf-8"	) as f:
  							f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=lowerCamelCase__							,			ensure_ascii=lowerCamelCase__	) + "\n"	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   0
  						with open(lowerCamelCase__							,			"w"							,			encoding="utf-8"	) as writer:
  							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda lowerCamelCase__	: kv[1]	):
  								if index != token_index:
  									logger.warning(
  									    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
  									    " Please check that the tokenizer is not corrupted!"	)
  									UpperCAmelCase__:  Optional[Any]		       =   token_index
  								writer.write(" ".join(lowerCamelCase__	) + "\n"	)
  								index += 1
  
  						return vocab_file, merge_file
  
  
  
  
  
  					def __getstate__(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.__dict__.copy()
  						UpperCAmelCase__:  Union[str, Any]		       =   None
  						return state
  
  
  
  
  
  					def __setstate__(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  List[str]		       =   d
  
  						try:
  							import sacremoses
  						except ImportError:
  							raise ImportError(
  							    "You need to install sacremoses to use XLMTokenizer. "
  							    "See https://pypi.org/project/sacremoses/ for installation."	)
  
  						UpperCAmelCase__:  List[str]		       =   sacremoses
style_context: 
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  _lowerCAmelCase     :			int    ="""1.5.0"""
  
  _lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	if is_optimum_available():
  		from optimum.version import __version__ as optimum_version
  
  		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
  		if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE			):
  			raise RuntimeError(
  			    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
  			    "can upgrade optimum by running: pip install -U optimum[exporters]"			)
  	else:
  		raise RuntimeError(
  		    "transformers.onnx requires optimum to run, you can install the library by running: pip install "
  		    "optimum[exporters]"			)
  	UpperCAmelCase__:  Any		       =   [
  	    sys.executable,
  	    "-m",
  	    "optimum.exporters.onnx",
  	    f"--model {args.model}",
  	    f"--task {args.feature}",
  	    f"--framework {args.framework}" if args.framework is not None else "",
  	    f"{args.output}",
  	]
  	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_SNAKE_CASE			)
  	proc.wait()
  
  	logger.info(
  	    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
  	    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
  	    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   args.output if args.output.is_file() else args.output.joinpath("model.onnx"			)
  	if not args.output.parent.exists():
  		args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE			)
  
  	# Allocate the model
  	UpperCAmelCase__:  int		       =   FeaturesManager.get_model_from_feature(
  	    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir			)
  
  	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
  	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)
  
  	if model_kind in ENCODER_DECODER_MODELS:
  		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
  		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()
  
  		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
  		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
  		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)
  
  		if args.opset is None:
  			UpperCAmelCase__:  Tuple		       =   max(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)
  
  		if args.opset < min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			):
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
  			    f" {min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)} is required."			)
  
  		UpperCAmelCase__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)
  
  		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else decoder_onnx_config.atol_for_validation  ,)
  		logger.info(
  		    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"
  		    f" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"			)
  
  	else:
  		# Instantiate the appropriate preprocessor
  		if args.preprocessor == "auto":
  			UpperCAmelCase__:  str		       =   get_preprocessor(args.model			)
  		elif args.preprocessor == "tokenizer":
  			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
  		elif args.preprocessor == "image_processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
  		elif args.preprocessor == "feature_extractor":
  			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  		elif args.preprocessor == "processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoProcessor.from_pretrained(args.model			)
  		else:
  			raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"			)
  
  		# Ensure the requested opset is sufficient
  		if args.opset is None:
  			UpperCAmelCase__:  List[str]		       =   onnx_config.default_onnx_opset
  
  		if args.opset < onnx_config.default_onnx_opset:
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. "
  			    f"At least  {onnx_config.default_onnx_opset} is required."			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)
  
  		if args.atol is None:
  			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation
  
  		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_SNAKE_CASE  ,args.atol			)
  		logger.info(f"All good, model saved at: {args.output.as_posix()}"			)
  		warnings.warn(
  		    "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
  		    " using optimum.exporters.onnx in future. You can find more information here:"
  		    " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."  ,SCREAMING_SNAKE_CASE  ,)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  str		       =   ArgumentParser("Hugging Face Transformers ONNX exporter"			)
  	parser.add_argument(
  	    "-m"  ,"--model"  ,type=SCREAMING_SNAKE_CASE  ,required=SCREAMING_SNAKE_CASE  ,help="Model ID on huggingface.co or path on disk to load model from."			)
  	parser.add_argument(
  	    "--feature"  ,default="default"  ,help="The type of features to export the model with."  ,)
  	parser.add_argument("--opset"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="ONNX opset version to export the model with."			)
  	parser.add_argument(
  	    "--atol"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Absolute difference tolerance when validating the model."			)
  	parser.add_argument(
  	    "--framework"  ,type=SCREAMING_SNAKE_CASE  ,choices=["pt", "tf"]  ,default=SCREAMING_SNAKE_CASE  ,help=(
  	        "The framework to use for the ONNX export."
  	        " If not provided, will attempt to use the local checkpoint's original framework"
  	        " or what is available in the environment."
  	    )  ,)
  	parser.add_argument("output"  ,type=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store generated ONNX model."			)
  	parser.add_argument("--cache_dir"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store cache."			)
  	parser.add_argument(
  	    "--preprocessor"  ,type=SCREAMING_SNAKE_CASE  ,choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]  ,default="auto"  ,help="Which type of preprocessor to use. 'auto' tries to automatically detect it."  ,)
  	parser.add_argument(
  	    "--export_with_transformers"  ,action="store_true"  ,help=(
  	        "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
  	        "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
  	        "recommended."
  	    )  ,)
  
  	UpperCAmelCase__:  Optional[int]		       =   parser.parse_args()
  	if args.export_with_transformers or not is_optimum_available():
  		export_with_transformers(SCREAMING_SNAKE_CASE			)
  	else:
  		export_with_optimum(SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			int    =logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  					logger.setLevel(logging.INFO)
  					main()