code: 
  
  
  SCREAMING_SNAKE_CASE : str          =    float("inf")
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	[0] * n
  								a_       :   Union[str, Any]			=	[0] * n
  								a_       :   Tuple			=	[0] * n
  								a_       :   Dict			=	[[] for _ in range(UpperCamelCase_							)]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_=0							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.adj[a].append([b, len(self.adj[b]							), c, 0]							)
  								self.adj[b].append([a, len(self.adj[a]							) - 1, rcap, 0]							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if vertex == sink or not flow:
  															return flow
  
  								for i in range(self.ptr[vertex]				,    len(self.adj[vertex]							)							):
  															a_       :   Dict			=	self.adj[vertex][i]
  															if self.lvl[e[0]] == self.lvl[vertex] + 1:
  																						a_       :   Optional[int]			=	self.depth_first_search(e[0]				,    UpperCamelCase_				,    min(UpperCamelCase_				,    e[2] - e[3]							)							)
  																						if p:
  																													self.adj[vertex][i][3] += p
  																													self.adj[e[0]][e[1]][3] -= p
  																													return p
  															a_       :   Any			=	self.ptr[vertex] + 1
  								return 0
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Dict			=	0, source
  								for l in range(31							):  # noqa: E741  l = 30 maybe faster for random data
  															while True:
  																						a_		,				a_       :   Any			=	[0] * len(self.q							), [0] * len(self.q							)
  																						a_		,				a_		,				a_       :   Any			=	0, 1, 1
  																						while qi < qe and not self.lvl[sink]:
  																													a_       :   str			=	self.q[qi]
  																													qi += 1
  																													for e in self.adj[v]:
  																																				if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
  																																											a_       :   List[Any]			=	e[0]
  																																											qe += 1
  																																											a_       :   str			=	self.lvl[v] + 1
  
  																						a_       :   Union[str, Any]			=	self.depth_first_search(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  																						while p:
  																													flow += p
  																													a_       :   Optional[int]			=	self.depth_first_search(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  
  																						if not self.lvl[sink]:
  																													break
  
  								return flow
  SCREAMING_SNAKE_CASE : Optional[Any]          =    Dinic(10)
  SCREAMING_SNAKE_CASE : List[str]          =    0
  SCREAMING_SNAKE_CASE : List[Any]          =    9
  for vertex in range(1, 5):
  	graph.add_edge(source, vertex, 1)
  for vertex in range(5, 9):
  	graph.add_edge(vertex, sink, 1)
  for vertex in range(1, 5):
  	graph.add_edge(vertex, vertex + 4, 1)
  
  # Now we can know that is the maximum flow(source -> sink)
  print(graph.max_flow(source, sink))
  
  
  
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()
  
  
  