code: 
  
  from __future__ import annotations
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  					import tensorflow as tf
  
  					from transformers import TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import ConvNextImageProcessor
  
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_3							,			lowerCamelCase__=3_2							,			lowerCamelCase__=3							,			lowerCamelCase__=4							,			lowerCamelCase__=[1_0, 2_0, 3_0, 4_0]							,			lowerCamelCase__=[2, 2, 3, 2]							,			lowerCamelCase__=True							,			lowerCamelCase__=True							,			lowerCamelCase__=3_7							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=1_0							,			lowerCamelCase__=0.02							,			lowerCamelCase__=3							,			lowerCamelCase__=None							,			):
  						UpperCAmelCase__:  Union[str, Any]		       =   parent
  						UpperCAmelCase__:  List[Any]		       =   batch_size
  						UpperCAmelCase__:  Optional[int]		       =   image_size
  						UpperCAmelCase__:  Optional[Any]		       =   num_channels
  						UpperCAmelCase__:  Tuple		       =   num_stages
  						UpperCAmelCase__:  List[str]		       =   hidden_sizes
  						UpperCAmelCase__:  int		       =   depths
  						UpperCAmelCase__:  Dict		       =   is_training
  						UpperCAmelCase__:  Union[str, Any]		       =   use_labels
  						UpperCAmelCase__:  int		       =   intermediate_size
  						UpperCAmelCase__:  int		       =   hidden_act
  						UpperCAmelCase__:  List[Any]		       =   type_sequence_label_size
  						UpperCAmelCase__:  Optional[Any]		       =   initializer_range
  						UpperCAmelCase__:  Dict		       =   scope
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[int]		       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  						UpperCAmelCase__:  Optional[int]		       =   None
  						if self.use_labels:
  							UpperCAmelCase__:  Union[str, Any]		       =   ids_tensor([self.batch_size]							,			self.type_sequence_label_size	)
  
  						UpperCAmelCase__:  Dict		       =   self.get_config()
  
  						return config, pixel_values, labels
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return ConvNextConfig(
  						    num_channels=self.num_channels							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			num_stages=self.num_stages							,			hidden_act=self.hidden_act							,			is_decoder=lowerCamelCase__							,			initializer_range=self.initializer_range							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   TFConvNextModel(config=lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   model(lowerCamelCase__							,			training=lowerCamelCase__	)
  						# expected last hidden states: B, C, H // 32, W // 32
  						self.parent.assertEqual(
  						    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  str		       =   self.type_sequence_label_size
  						UpperCAmelCase__:  Any		       =   TFConvNextForImageClassification(lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__							,			training=lowerCamelCase__	)
  						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.prepare_config_and_inputs()
  						UpperCAmelCase__:  List[str]		       =   config_and_inputs
  						UpperCAmelCase__:  Dict		       =   {"pixel_values": pixel_values}
  						return config, inputs_dict
  
  @require_tf
  class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  					__magic_name__     			=      (
  					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
  					    if is_tf_available()
  					    else {}
  					)
  
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   TFConvNextModelTester(self	)
  						UpperCAmelCase__:  Optional[Any]		       =   ConfigTester(
  						    self							,			config_class=lowerCamelCase__							,			has_text_modality=lowerCamelCase__							,			hidden_size=3_7							,			)
  
  
  
  
  
  					@unittest.skip(reason="ConvNext does not use inputs_embeds"	)
  					def       _UpperCAmelCase						(							self	):
  						pass
  
  
  
  
  
  					@unittest.skipIf(
  					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0							,			reason="TF does not support backprop for grouped convolutions on CPU."							,			)
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						super().test_keras_fit()
  
  
  
  
  
  					@unittest.skip(reason="ConvNext does not support input and output embeddings"	)
  					def       _UpperCAmelCase						(							self	):
  						pass
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
  							UpperCAmelCase__:  Tuple		       =   inspect.signature(model.call	)
  							# signature.parameters is an OrderedDict => so arg_names order is deterministic
  							UpperCAmelCase__:  List[Any]		       =   [*signature.parameters.keys()]
  
  							UpperCAmelCase__:  Optional[int]		       =   ["pixel_values"]
  							self.assertListEqual(arg_names[:1]							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*lowerCamelCase__	)
  
  
  
  
  
  					@unittest.skipIf(
  					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0							,			reason="TF does not support backprop for grouped convolutions on CPU."							,			)
  					def       _UpperCAmelCase						(							self	):
  						super().test_dataset_conversion()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						def check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  							UpperCAmelCase__:  Any		       =   model_class(lowerCamelCase__	)
  
  							UpperCAmelCase__:  int		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
  							UpperCAmelCase__:  Optional[int]		       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  							UpperCAmelCase__:  Dict		       =   self.model_tester.num_stages
  							self.assertEqual(len(lowerCamelCase__	)							,			expected_num_stages + 1	)
  
  							# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
  							self.assertListEqual(
  							    list(hidden_states[0].shape[-2:]	)							,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,			)
  
  						UpperCAmelCase__:  Dict		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  str		       =   True
  							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  							# check that output_hidden_states also work using config
  							del inputs_dict["output_hidden_states"]
  							UpperCAmelCase__:  Optional[int]		       =   True
  
  							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						def check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__={}	):
  							UpperCAmelCase__:  Optional[Any]		       =   model(lowerCamelCase__							,			return_dict=lowerCamelCase__							,			**lowerCamelCase__	)
  							UpperCAmelCase__:  Union[str, Any]		       =   model(lowerCamelCase__							,			return_dict=lowerCamelCase__							,			**lowerCamelCase__	).to_tuple()
  
  							def recursive_check(lowerCamelCase__							,			lowerCamelCase__	):
  								if isinstance(lowerCamelCase__							,			(List, Tuple)	):
  									for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase__							,			lowerCamelCase__	):
  										recursive_check(lowerCamelCase__							,			lowerCamelCase__	)
  								elif tuple_object is None:
  									return
  								else:
  									self.assertTrue(
  									    all(tf.equal(lowerCamelCase__							,			lowerCamelCase__	)	)							,			msg=(
  									        "Tuple and dict output are not equal. Difference:"
  									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object	)	)}"
  									    )							,			)
  
  								recursive_check(lowerCamelCase__							,			lowerCamelCase__	)
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  Tuple		       =   model_class(lowerCamelCase__	)
  
  							UpperCAmelCase__:  Tuple		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
  							UpperCAmelCase__:  int		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
  							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  							UpperCAmelCase__:  Tuple		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							UpperCAmelCase__:  Any		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  							UpperCAmelCase__:  Any		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
  							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
  							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			{"output_hidden_states": True}	)
  
  							UpperCAmelCase__:  Union[str, Any]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							UpperCAmelCase__:  int		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			{"output_hidden_states": True}	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"	)
  						self.assertIsNotNone(lowerCamelCase__	)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  Optional[Any]		       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  	return image
  
  @require_tf
  @require_vision
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@cached_property
  					def       _UpperCAmelCase						(							self	):
  						return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"	) if is_vision_available() else None
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"	)
  
  						UpperCAmelCase__:  List[str]		       =   self.default_image_processor
  						UpperCAmelCase__:  Union[str, Any]		       =   prepare_img()
  						UpperCAmelCase__:  Any		       =   image_processor(images=lowerCamelCase__							,			return_tensors="tf"	)
  
  						# forward pass
  						UpperCAmelCase__:  List[str]		       =   model(**lowerCamelCase__	)
  
  						# verify the logits
  						UpperCAmelCase__:  Tuple		       =   tf.TensorShape((1, 1_0_0_0)	)
  						self.assertEqual(outputs.logits.shape							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   tf.constant([-0.0_260, -0.4_739, 0.1_911]	)
  
  						tf.debugging.assert_near(outputs.logits[0, :3]							,			lowerCamelCase__							,			atol=1e-4	)
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()