


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE : Union[str, Any]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[str]          =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 					snake_case__  ( __A       ):

	'''simple docstring'''





	UpperCAmelCase   :							Dict         = """deberta"""





	def __init__(  self				,    UpperCamelCase_=50265				,    UpperCamelCase_=768				,    UpperCamelCase_=12				,    UpperCamelCase_=12				,    UpperCamelCase_=3072				,    UpperCamelCase_="gelu"				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.1				,    UpperCamelCase_=512				,    UpperCamelCase_=0				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-7				,    UpperCamelCase_=False				,    UpperCamelCase_=-1				,    UpperCamelCase_=0				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    UpperCamelCase_=0				,    UpperCamelCase_="gelu"				,    **UpperCamelCase_				,    )		->			int:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	hidden_size
								a_       :   str			=	num_hidden_layers
								a_       :   Tuple			=	num_attention_heads
								a_       :   List[Any]			=	intermediate_size
								a_       :   Union[str, Any]			=	hidden_act
								a_       :   str			=	hidden_dropout_prob
								a_       :   str			=	attention_probs_dropout_prob
								a_       :   Optional[int]			=	max_position_embeddings
								a_       :   Dict			=	type_vocab_size
								a_       :   str			=	initializer_range
								a_       :   Dict			=	relative_attention
								a_       :   Dict			=	max_relative_positions
								a_       :   Union[str, Any]			=	pad_token_id
								a_       :   List[str]			=	position_biased_input

								# Backwards compatibility
								if type(UpperCamelCase_							) == str:
															a_       :   Tuple			=	[x.strip() for x in pos_att_type.lower().split("""|"""							)]

								a_       :   Any			=	pos_att_type
								a_       :   List[str]			=	vocab_size
								a_       :   Tuple			=	layer_norm_eps

								a_       :   List[Any]			=	kwargs.get("""pooler_hidden_size"""				,    UpperCamelCase_							)
								a_       :   Any			=	pooler_dropout
								a_       :   Union[str, Any]			=	pooler_hidden_act





class 					snake_case__  ( __A       ):

	'''simple docstring'''





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "multiple-choice":
															a_       :   List[str]			=	{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
															a_       :   Optional[int]			=	{0: """batch""", 1: """sequence"""}
								if self._config.type_vocab_size > 0:
															return OrderedDict(
															    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]							)
								else:
															return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]							)





	@property
	def     A				(  self							)		->			int:
								"""simple docstring"""






								return 12





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 3				,    UpperCamelCase_ = 40				,    UpperCamelCase_ = 40				,    UpperCamelCase_ = None				,    )		->			Mapping[str, Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	super().generate_dummy_inputs(preprocessor=UpperCamelCase_				,    framework=UpperCamelCase_							)
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
															del dummy_inputs["token_type_ids"]
								return dummy_inputs






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


SCREAMING_SNAKE_CASE : str          =    "1.5.0"

SCREAMING_SNAKE_CASE : Dict          =    ["vision-encoder-decoder"]





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]      ):






							"""simple docstring"""



							if is_optimum_available():
														from optimum.version import __version__ as optimum_version

														a_       :   List[Any]			=	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]"""      )
							a_       :   Tuple			=	[
							    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}""",
							]
							a_       :   List[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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple      ):






							"""simple docstring"""



							a_       :   Any			=	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
							a_       :   int			=	FeaturesManager.get_model_from_feature(
							    args.feature	,	args.model	,	framework=args.framework	,	cache_dir=args.cache_dir      )

							a_		,				a_       :   str			=	FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE_	,	feature=args.feature      )
							a_       :   Dict			=	model_onnx_config(model.config      )

							if model_kind in ENCODER_DECODER_MODELS:
														a_       :   Optional[Any]			=	model.get_encoder()
														a_       :   Dict			=	model.get_decoder()

														a_       :   List[Any]			=	onnx_config.get_encoder_config(encoder_model.config      )
														a_       :   List[str]			=	onnx_config.get_decoder_config(
														    encoder_model.config	,	decoder_model.config	,	feature=args.feature      )

														if args.opset is None:
																					a_       :   str			=	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."""      )

														a_       :   List[Any]			=	AutoFeatureExtractor.from_pretrained(args.model      )

														a_		,				a_       :   List[str]			=	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	,	)

														a_       :   Union[str, Any]			=	AutoTokenizer.from_pretrained(args.model      )

														a_		,				a_       :   List[Any]			=	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":
																					a_       :   List[str]			=	get_preprocessor(args.model      )
														elif args.preprocessor == "tokenizer":
																					a_       :   Optional[int]			=	AutoTokenizer.from_pretrained(args.model      )
														elif args.preprocessor == "image_processor":
																					a_       :   List[str]			=	AutoImageProcessor.from_pretrained(args.model      )
														elif args.preprocessor == "feature_extractor":
																					a_       :   Optional[Any]			=	AutoFeatureExtractor.from_pretrained(args.model      )
														elif args.preprocessor == "processor":
																					a_       :   Union[str, Any]			=	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:
																					a_       :   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."""      )

														a_		,				a_       :   Optional[Any]			=	export(
														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.opset	,	args.output	,	)

														if args.atol is None:
																					a_       :   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 						_lowerCamelCase		(							):






							"""simple docstring"""



							a_       :   List[Any]			=	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."""
							    )	,	)

							a_       :   Tuple			=	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__":
	SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
	logger.setLevel(logging.INFO)
	main()


