code: 
  
  import os
  import unittest
  
  from transformers import ElectraTokenizerFast
  from transformers.models.electra.tokenization_electra import (
      VOCAB_FILES_NAMES,
      BasicTokenizer,
      ElectraTokenizer,
      WordpieceTokenizer,
      _is_control,
      _is_punctuation,
      _is_whitespace,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
  
  @require_tokenizers
  class __UpperCamelCase    (							_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      ElectraTokenizer
  					__magic_name__     			=      ElectraTokenizerFast
  					__magic_name__     			=      True
  					__magic_name__     			=      True
  					__magic_name__     			=      filter_non_english
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						super().setUp()
  
  						UpperCAmelCase__:  Union[str, Any]		       =   [
  						    "[UNK]",
  						    "[CLS]",
  						    "[SEP]",
  						    "[PAD]",
  						    "[MASK]",
  						    "want",
  						    "##want",
  						    "##ed",
  						    "wa",
  						    "un",
  						    "runn",
  						    "##ing",
  						    ",",
  						    "low",
  						    "lowest",
  						]
  						UpperCAmelCase__:  Optional[Any]		       =   os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]	)
  						with open(self.vocab_file							,			"w"							,			encoding="utf-8"	) as vocab_writer:
  							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   "UNwant\u00E9d,running"
  						UpperCAmelCase__:  int		       =   "unwanted, running"
  						return input_text, output_text
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   self.tokenizer_class(self.vocab_file	)
  
  						UpperCAmelCase__:  Optional[int]		       =   tokenizer.tokenize("UNwant\u00E9d,running"	)
  						self.assertListEqual(lowerCamelCase__							,			["un", "##want", "##ed", ",", "runn", "##ing"]	)
  						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__	)							,			[9, 6, 7, 1_2, 1_0, 1_1]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						if not self.test_rust_tokenizer:
  							return
  
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  						UpperCAmelCase__:  List[str]		       =   self.get_rust_tokenizer()
  
  						UpperCAmelCase__:  Optional[int]		       =   "UNwant\u00E9d,running"
  
  						UpperCAmelCase__:  Any		       =   tokenizer.tokenize(lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   rust_tokenizer.tokenize(lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  Tuple		       =   tokenizer.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   rust_tokenizer.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_rust_tokenizer()
  						UpperCAmelCase__:  List[Any]		       =   tokenizer.encode(lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   rust_tokenizer.encode(lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  						# With lower casing
  						UpperCAmelCase__:  int		       =   self.get_tokenizer(do_lower_case=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_rust_tokenizer(do_lower_case=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Any		       =   "UNwant\u00E9d,running"
  
  						UpperCAmelCase__:  Optional[int]		       =   tokenizer.tokenize(lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   rust_tokenizer.tokenize(lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Any		       =   rust_tokenizer.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_rust_tokenizer()
  						UpperCAmelCase__:  Tuple		       =   tokenizer.encode(lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   rust_tokenizer.encode(lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   BasicTokenizer()
  
  						self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"	)							,			["ah", "\u535A", "\u63A8", "zz"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   BasicTokenizer(do_lower_case=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)							,			["hello", "!", "how", "are", "you", "?"]	)
  						self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)							,			["hello"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   BasicTokenizer(do_lower_case=lowerCamelCase__							,			strip_accents=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)							,			["hällo", "!", "how", "are", "you", "?"]	)
  						self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)							,			["h\u00E9llo"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   BasicTokenizer(do_lower_case=lowerCamelCase__							,			strip_accents=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)							,			["hallo", "!", "how", "are", "you", "?"]	)
  						self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)							,			["hello"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   BasicTokenizer(do_lower_case=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)							,			["hallo", "!", "how", "are", "you", "?"]	)
  						self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)							,			["hello"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   BasicTokenizer(do_lower_case=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)							,			["HeLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   BasicTokenizer(do_lower_case=lowerCamelCase__							,			strip_accents=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)							,			["HäLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   BasicTokenizer(do_lower_case=lowerCamelCase__							,			strip_accents=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)							,			["HaLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   BasicTokenizer(do_lower_case=lowerCamelCase__							,			never_split=["[UNK]"]	)
  
  						self.assertListEqual(
  						    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"	)							,			["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
  
  						UpperCAmelCase__:  int		       =   {}
  						for i, token in enumerate(lowerCamelCase__	):
  							UpperCAmelCase__:  Optional[int]		       =   i
  						UpperCAmelCase__:  Any		       =   WordpieceTokenizer(vocab=lowerCamelCase__							,			unk_token="[UNK]"	)
  
  						self.assertListEqual(tokenizer.tokenize(""	)							,			[]	)
  
  						self.assertListEqual(tokenizer.tokenize("unwanted running"	)							,			["un", "##want", "##ed", "runn", "##ing"]	)
  
  						self.assertListEqual(tokenizer.tokenize("unwantedX running"	)							,			["[UNK]", "runn", "##ing"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.assertTrue(_is_whitespace(" "	)	)
  						self.assertTrue(_is_whitespace("\t"	)	)
  						self.assertTrue(_is_whitespace("\r"	)	)
  						self.assertTrue(_is_whitespace("\n"	)	)
  						self.assertTrue(_is_whitespace("\u00A0"	)	)
  
  						self.assertFalse(_is_whitespace("A"	)	)
  						self.assertFalse(_is_whitespace("-"	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.assertTrue(_is_control("\u0005"	)	)
  
  						self.assertFalse(_is_control("A"	)	)
  						self.assertFalse(_is_control(" "	)	)
  						self.assertFalse(_is_control("\t"	)	)
  						self.assertFalse(_is_control("\r"	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.assertTrue(_is_punctuation("-"	)	)
  						self.assertTrue(_is_punctuation("$"	)	)
  						self.assertTrue(_is_punctuation("`"	)	)
  						self.assertTrue(_is_punctuation("."	)	)
  
  						self.assertFalse(_is_punctuation("A"	)	)
  						self.assertFalse(_is_punctuation(" "	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.get_tokenizer()
  						UpperCAmelCase__:  List[Any]		       =   self.get_rust_tokenizer()
  
  						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
  						self.assertListEqual([tokenizer.tokenize(lowerCamelCase__	) for t in ["Test", "\xad", "test"]]							,			[["[UNK]"], [], ["[UNK]"]]	)
  
  						self.assertListEqual(
  						    [rust_tokenizer.tokenize(lowerCamelCase__	) for t in ["Test", "\xad", "test"]]							,			[["[UNK]"], [], ["[UNK]"]]	)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.tokenizer_class.from_pretrained("google/electra-base-discriminator"	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer.encode("sequence builders"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer.encode("multi-sequence build"							,			add_special_tokens=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[int]		       =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__							,			lowerCamelCase__	)
  
  						assert encoded_sentence == [1_0_1] + text + [1_0_2]
  						assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  							with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
  								UpperCAmelCase__:  Optional[Any]		       =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  
  								UpperCAmelCase__:  List[Any]		       =   F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
  								UpperCAmelCase__:  List[str]		       =   tokenizer_r.encode_plus(
  								    lowerCamelCase__							,			return_attention_mask=lowerCamelCase__							,			return_token_type_ids=lowerCamelCase__							,			return_offsets_mapping=lowerCamelCase__							,			add_special_tokens=lowerCamelCase__							,			)
  
  								UpperCAmelCase__:  Optional[int]		       =   tokenizer_r.do_lower_case if hasattr(lowerCamelCase__							,			"do_lower_case"	) else False
  								UpperCAmelCase__:  Tuple		       =   (
  								    [
  								        ((0, 0), tokenizer_r.cls_token),
  								        ((0, 1), "A"),
  								        ((1, 2), ","),
  								        ((3, 5), "na"),
  								        ((5, 6), "##ï"),
  								        ((6, 8), "##ve"),
  								        ((9, 1_5), tokenizer_r.mask_token),
  								        ((1_6, 2_1), "Allen"),
  								        ((2_1, 2_3), "##NL"),
  								        ((2_3, 2_4), "##P"),
  								        ((2_5, 3_3), "sentence"),
  								        ((3_3, 3_4), "."),
  								        ((0, 0), tokenizer_r.sep_token),
  								    ]
  								    if not do_lower_case
  								    else [
  								        ((0, 0), tokenizer_r.cls_token),
  								        ((0, 1), "a"),
  								        ((1, 2), ","),
  								        ((3, 8), "naive"),
  								        ((9, 1_5), tokenizer_r.mask_token),
  								        ((1_6, 2_1), "allen"),
  								        ((2_1, 2_3), "##nl"),
  								        ((2_3, 2_4), "##p"),
  								        ((2_5, 3_3), "sentence"),
  								        ((3_3, 3_4), "."),
  								        ((0, 0), tokenizer_r.sep_token),
  								    ]
  								)
  
  								self.assertEqual(
  								    [e[1] for e in expected_results]							,			tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]	)	)
  								self.assertEqual([e[0] for e in expected_results]							,			tokens["offset_mapping"]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   ["的", "人", "有"]
  						UpperCAmelCase__:  Tuple		       =   "".join(lowerCamelCase__	)
  						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  							with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
  								UpperCAmelCase__:  Dict		       =   True
  								UpperCAmelCase__:  List[str]		       =   self.tokenizer_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  								UpperCAmelCase__:  Optional[int]		       =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  
  								UpperCAmelCase__:  int		       =   tokenizer_p.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  								UpperCAmelCase__:  str		       =   tokenizer_r.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  
  								UpperCAmelCase__:  Optional[Any]		       =   tokenizer_r.convert_ids_to_tokens(lowerCamelCase__	)
  								UpperCAmelCase__:  str		       =   tokenizer_p.convert_ids_to_tokens(lowerCamelCase__	)
  
  								# it is expected that each Chinese character is not preceded by "##"
  								self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  								self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  								UpperCAmelCase__:  Any		       =   False
  								UpperCAmelCase__:  Union[str, Any]		       =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  								UpperCAmelCase__:  Dict		       =   self.tokenizer_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  
  								UpperCAmelCase__:  Tuple		       =   tokenizer_r.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  								UpperCAmelCase__:  List[str]		       =   tokenizer_p.encode(lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  
  								UpperCAmelCase__:  Any		       =   tokenizer_r.convert_ids_to_tokens(lowerCamelCase__	)
  								UpperCAmelCase__:  Dict		       =   tokenizer_p.convert_ids_to_tokens(lowerCamelCase__	)
  
  								# it is expected that only the first Chinese character is not preceded by "##".
  								UpperCAmelCase__:  Union[str, Any]		       =   [
  								    F"##{token}" if idx != 0 else token for idx, token in enumerate(lowerCamelCase__	)
  								]
  								self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  								self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
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()