code: 
  
  
  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
  
  
  
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
  
  
  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()
  
  
  