


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[Any]          =    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 					snake_case__  ( __A       ):

	'''simple docstring'''





	UpperCAmelCase   :							Tuple         = """oneformer"""
	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}





	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
								"""simple docstring"""






								if backbone_config is None:
															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
															a_       :   str			=	backbone_config.get("""model_type"""							)
															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)

								a_       :   str			=	backbone_config

								a_       :   List[str]			=	ignore_value
								a_       :   Optional[int]			=	num_queries
								a_       :   Optional[Any]			=	no_object_weight
								a_       :   List[Any]			=	class_weight
								a_       :   Union[str, Any]			=	mask_weight
								a_       :   Union[str, Any]			=	dice_weight
								a_       :   Optional[Any]			=	contrastive_weight
								a_       :   Any			=	contrastive_temperature
								a_       :   Optional[int]			=	train_num_points
								a_       :   Tuple			=	oversample_ratio
								a_       :   str			=	importance_sample_ratio
								a_       :   List[Any]			=	init_std
								a_       :   int			=	init_xavier_std
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   Optional[int]			=	is_training
								a_       :   List[Any]			=	use_auxiliary_loss
								a_       :   str			=	output_auxiliary_logits
								a_       :   Dict			=	strides
								a_       :   List[Any]			=	task_seq_len
								a_       :   int			=	text_encoder_width
								a_       :   List[Any]			=	text_encoder_context_length
								a_       :   List[str]			=	text_encoder_num_layers
								a_       :   Union[str, Any]			=	text_encoder_vocab_size
								a_       :   Optional[Any]			=	text_encoder_proj_layers
								a_       :   Optional[int]			=	text_encoder_n_ctx
								a_       :   Optional[Any]			=	conv_dim
								a_       :   str			=	mask_dim
								a_       :   Optional[Any]			=	hidden_dim
								a_       :   Any			=	encoder_feedforward_dim
								a_       :   Dict			=	norm
								a_       :   List[Any]			=	encoder_layers
								a_       :   int			=	decoder_layers
								a_       :   Any			=	use_task_norm
								a_       :   Union[str, Any]			=	num_attention_heads
								a_       :   List[str]			=	dropout
								a_       :   List[Any]			=	dim_feedforward
								a_       :   Union[str, Any]			=	pre_norm
								a_       :   Dict			=	enforce_input_proj
								a_       :   Tuple			=	query_dec_layers
								a_       :   Any			=	common_stride
								a_       :   List[str]			=	decoder_layers

								super().__init__(**UpperCamelCase_							)





	def     A				(  self							)		->			Dict[str, any]:
								"""simple docstring"""






								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
								a_       :   Any			=	self.backbone_config.to_dict()
								a_       :   Union[str, Any]			=	self.__class__.model_type
								return output






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()


