

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__	)


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()