code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import Any, Dict, List, Union
  
  import numpy as np
  
  from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from ..image_utils import load_image
  
  if is_torch_available():
  				from ..models.auto.modeling_auto import (
  				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
  				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
  				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
  				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
  				)
  
  
  _lowerCamelCase				:	Union[str, Any]				    =       logging.get_logger(__name__)
  
  
  _lowerCamelCase				:	int				    =       Dict[str, Any]
  _lowerCamelCase				:	Tuple				    =       List[Prediction]
  
  
  
  @add_end_docstrings(A__						)
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ,      *_lowerCAmelCase						:      List[str]      ,      **_lowerCAmelCase						:      Tuple      ):
  			super().__init__(*_lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  			if self.framework == "tf":
  				raise ValueError(F"""The {self.__class__} is only available in PyTorch."""      )
  
  			requires_backends(self      ,      """vision"""      )
  			self.check_model_type(
  			    dict(
  			        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
  			        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
  			        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
  			        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      **_lowerCAmelCase						:      List[str]      ):
  			A							  =      {}
  			A							  =      {}
  			if "subtask" in kwargs:
  				A							  =      kwargs["""subtask"""]
  				A							  =      kwargs["""subtask"""]
  			if "threshold" in kwargs:
  				A							  =      kwargs["""threshold"""]
  			if "mask_threshold" in kwargs:
  				A							  =      kwargs["""mask_threshold"""]
  			if "overlap_mask_area_threshold" in kwargs:
  				A							  =      kwargs["""overlap_mask_area_threshold"""]
  
  			return preprocess_kwargs, {}, postprocess_kwargs
  
  
  
  
  
  
  
  		def __call__(self						:      str      ,      _lowerCAmelCase						:      Dict      ,      **_lowerCAmelCase						:      Tuple      ):
  			return super().__call__(_lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Dict=None      ):
  			A							  =      load_image(_lowerCAmelCase      )
  			A							  =      [(image.height, image.width)]
  			if self.model.config.__class__.__name__ == "OneFormerConfig":
  				if subtask is None:
  					A							  =      {}
  				else:
  					A							  =      {"""task_inputs""": [subtask]}
  				A							  =      self.image_processor(images=[image]      ,      return_tensors="""pt"""      ,      **_lowerCAmelCase      )
  				A							  =      self.tokenizer(
  				    inputs["""task_inputs"""]      ,      padding="""max_length"""      ,      max_length=self.model.config.task_seq_len      ,      return_tensors=self.framework      ,      )["""input_ids"""]
  			else:
  				A							  =      self.image_processor(images=[image]      ,      return_tensors="""pt"""      )
  			A							  =      target_size
  			return inputs
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      str      ):
  			A							  =      model_inputs.pop("""target_size"""      )
  			A							  =      self.model(**_lowerCAmelCase      )
  			A							  =      target_size
  			return model_outputs
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      Any      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      _lowerCAmelCase						:      List[Any]=0.9      ,      _lowerCAmelCase						:      Union[str, Any]=0.5      ,      _lowerCAmelCase						:      List[str]=0.5      ):
  			A							  =      None
  			if subtask in {"panoptic", None} and hasattr(self.image_processor      ,      """post_process_panoptic_segmentation"""      ):
  				A							  =      self.image_processor.post_process_panoptic_segmentation
  			elif subtask in {"instance", None} and hasattr(self.image_processor      ,      """post_process_instance_segmentation"""      ):
  				A							  =      self.image_processor.post_process_instance_segmentation
  
  			if fn is not None:
  				A							  =      fn(
  				    _lowerCAmelCase      ,      threshold=_lowerCAmelCase      ,      mask_threshold=_lowerCAmelCase      ,      overlap_mask_area_threshold=_lowerCAmelCase      ,      target_sizes=model_outputs["""target_size"""]      ,      )[0]
  
  				A							  =      []
  				A							  =      outputs["""segmentation"""]
  
  				for segment in outputs["segments_info"]:
  					A							  =      (segmentation == segment["""id"""]) * 255
  					A							  =      Image.fromarray(mask.numpy().astype(np.uinta      )      ,      mode="""L"""      )
  					A							  =      self.model.config.idalabel[segment["""label_id"""]]
  					A							  =      segment["""score"""]
  					annotation.append({"""score""": score, """label""": label, """mask""": mask}      )
  
  			elif subtask in {"semantic", None} and hasattr(self.image_processor      ,      """post_process_semantic_segmentation"""      ):
  				A							  =      self.image_processor.post_process_semantic_segmentation(
  				    _lowerCAmelCase      ,      target_sizes=model_outputs["""target_size"""]      )[0]
  
  				A							  =      []
  				A							  =      outputs.numpy()
  				A							  =      np.unique(_lowerCAmelCase      )
  
  				for label in labels:
  					A							  =      (segmentation == label) * 255
  					A							  =      Image.fromarray(mask.astype(np.uinta      )      ,      mode="""L"""      )
  					A							  =      self.model.config.idalabel[label]
  					annotation.append({"""score""": None, """label""": label, """mask""": mask}      )
  			else:
  				raise ValueError(F"""Subtask {subtask} is not supported for model {type(self.model      )}"""      )
  			return annotation
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  _lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
  			A							  =      name
  			A							  =      []
  			A							  =      []
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
  			self.inbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			self.outbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def __repr__(self						:      Tuple      ):
  			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      1
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      len(node.outbound    )
  
  	for i in range(UpperCAmelCase    ):
  		print(f"""======= Iteration {i + 1} ======="""    )
  		for _, node in enumerate(UpperCAmelCase    ):
  			A							  =      (1 - d) + d * sum(
  			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
  		print(UpperCAmelCase    )
  
  
  
  
  def    __a (     )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )
  
  	A							  =      [Node(UpperCAmelCase    ) for name in names]
  
  	for ri, row in enumerate(UpperCAmelCase    ):
  		for ci, col in enumerate(UpperCAmelCase    ):
  			if col == 1:
  				nodes[ci].add_inbound(names[ri]    )
  				nodes[ri].add_outbound(names[ci]    )
  
  	print("""======= Nodes ======="""    )
  	for node in nodes:
  		print(UpperCAmelCase    )
  
  	page_rank(UpperCAmelCase    )
  
  
  if __name__ == "__main__":
  				main()
  
  