

"""simple docstring"""




from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__UpperCAmelCase			=					logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase       )
class   __lowercase      (     __lowerCamelCase       ):

  def __init__(	self		:      List[str]       ,**A		:      Optional[int]     ):






   '''simple docstring'''
   super().__init__(**lowerCamelCase_     )
   requires_backends(self       ,"""vision"""     )
   requires_backends(self       ,"""torch"""     )

   if self.framework != "pt":
    raise ValueError(f"The {self.__class__} is only available in PyTorch."     )

   self.check_model_type(lowerCamelCase_     )

  def  __lowercase							(	self		:      str       ,**A		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       {}
   UpperCAmelCase__       :	Optional[Any]        =       {}
   UpperCAmelCase__       :	Optional[Any]        =       {}
   # preprocess args
   if "points_per_batch" in kwargs:
    UpperCAmelCase__       :	Optional[Any]        =       kwargs["""points_per_batch"""]
   if "points_per_crop" in kwargs:
    UpperCAmelCase__       :	Tuple        =       kwargs["""points_per_crop"""]
   if "crops_n_layers" in kwargs:
    UpperCAmelCase__       :	Tuple        =       kwargs["""crops_n_layers"""]
   if "crop_overlap_ratio" in kwargs:
    UpperCAmelCase__       :	List[Any]        =       kwargs["""crop_overlap_ratio"""]
   if "crop_n_points_downscale_factor" in kwargs:
    UpperCAmelCase__       :	Any        =       kwargs["""crop_n_points_downscale_factor"""]
   # postprocess args
   if "pred_iou_thresh" in kwargs:
    UpperCAmelCase__       :	Optional[int]        =       kwargs["""pred_iou_thresh"""]
   if "stability_score_offset" in kwargs:
    UpperCAmelCase__       :	Any        =       kwargs["""stability_score_offset"""]
   if "mask_threshold" in kwargs:
    UpperCAmelCase__       :	int        =       kwargs["""mask_threshold"""]
   if "stability_score_thresh" in kwargs:
    UpperCAmelCase__       :	Tuple        =       kwargs["""stability_score_thresh"""]
   if "crops_nms_thresh" in kwargs:
    UpperCAmelCase__       :	Tuple        =       kwargs["""crops_nms_thresh"""]
   if "output_rle_mask" in kwargs:
    UpperCAmelCase__       :	int        =       kwargs["""output_rle_mask"""]
   if "output_bboxes_mask" in kwargs:
    UpperCAmelCase__       :	List[str]        =       kwargs["""output_bboxes_mask"""]
   return preprocess_kwargs, forward_params, postprocess_kwargs

  def __call__(	self		:      str       ,A		:      Union[str, Any]       ,*A		:      Tuple       ,A		:      Optional[Any]=None       ,A		:      Union[str, Any]=None       ,**A		:      Optional[int]     ):






   '''simple docstring'''
   return super().__call__(lowerCamelCase_       ,*lowerCamelCase_       ,num_workers=lowerCamelCase_       ,batch_size=lowerCamelCase_       ,**lowerCamelCase_     )

  def  __lowercase							(	self		:      Union[str, Any]       ,A		:      List[Any]       ,A		:      List[str]=64       ,A		:      Any = 0       ,A		:      List[str] = 512 / 1_500       ,A		:      str = 32       ,A		:      Union[str, Any] = 1       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       load_image(lowerCamelCase_     )
   UpperCAmelCase__       :	List[Any]        =       self.image_processor.size["""longest_edge"""]
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Any        =       self.image_processor.generate_crop_boxes(
       lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_     )
   UpperCAmelCase__       :	int        =       self.image_processor(images=lowerCamelCase_       ,return_tensors="""pt"""     )

   with self.device_placement():
    if self.framework == "pt":
     UpperCAmelCase__       :	Any        =       self.get_inference_context()
     with inference_context():
      UpperCAmelCase__       :	List[Any]        =       self._ensure_tensor_on_device(lowerCamelCase_       ,device=self.device     )
      UpperCAmelCase__       :	Any        =       self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""     )     )
      UpperCAmelCase__       :	Dict        =       image_embeddings

   UpperCAmelCase__       :	Union[str, Any]        =       grid_points.shape[1]
   UpperCAmelCase__       :	Optional[int]        =       points_per_batch if points_per_batch is not None else n_points

   if points_per_batch <= 0:
    raise ValueError(
        """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
        """To return all points at once, set points_per_batch to None"""     )

   for i in range(0       ,lowerCamelCase_       ,lowerCamelCase_     ):
    UpperCAmelCase__       :	Tuple        =       grid_points[:, i : i + points_per_batch, :, :]
    UpperCAmelCase__       :	Dict        =       input_labels[:, i : i + points_per_batch]
    UpperCAmelCase__       :	str        =       i == n_points - points_per_batch
    yield {
        "input_points": batched_points,
        "input_labels": labels,
        "input_boxes": crop_boxes,
        "is_last": is_last,
        **model_inputs,
    }

  def  __lowercase							(	self		:      List[Any]       ,A		:      List[Any]       ,A		:      Tuple=0.8_8       ,A		:      List[str]=0.9_5       ,A		:      Union[str, Any]=0       ,A		:      Any=1       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       model_inputs.pop("""input_boxes"""     )
   UpperCAmelCase__       :	Any        =       model_inputs.pop("""is_last"""     )
   UpperCAmelCase__       :	Union[str, Any]        =       model_inputs.pop("""original_sizes"""     ).tolist()
   UpperCAmelCase__       :	Dict        =       model_inputs.pop("""reshaped_input_sizes"""     ).tolist()

   UpperCAmelCase__       :	Optional[Any]        =       self.model(**lowerCamelCase_     )

   # post processing happens here in order to avoid CPU GPU copies of ALL the masks
   UpperCAmelCase__       :	Optional[Any]        =       model_outputs["""pred_masks"""]
   UpperCAmelCase__       :	List[Any]        =       self.image_processor.post_process_masks(
       lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,binarize=lowerCamelCase_     )
   UpperCAmelCase__       :	Dict        =       model_outputs["""iou_scores"""]
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[Any]        =       self.image_processor.filter_masks(
       masks[0]       ,iou_scores[0]       ,original_sizes[0]       ,input_boxes[0]       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,)
   return {
       "masks": masks,
       "is_last": is_last,
       "boxes": boxes,
       "iou_scores": iou_scores,
   }




  def  __lowercase							(	self		:      Optional[Any]       ,A		:      Any       ,A		:      str=False       ,A		:      Optional[int]=False       ,A		:      Optional[Any]=0.7       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       []
   UpperCAmelCase__       :	Optional[int]        =       []
   UpperCAmelCase__       :	str        =       []
   for model_output in model_outputs:
    all_scores.append(model_output.pop("""iou_scores"""     )     )
    all_masks.extend(model_output.pop("""masks"""     )     )
    all_boxes.append(model_output.pop("""boxes"""     )     )

   UpperCAmelCase__       :	Dict        =       torch.cat(lowerCamelCase_     )
   UpperCAmelCase__       :	str        =       torch.cat(lowerCamelCase_     )
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[str]        =       self.image_processor.post_process_for_mask_generation(
       lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_     )

   UpperCAmelCase__       :	List[Any]        =       defaultdict(lowerCamelCase_     )
   for output in model_outputs:
    for k, v in output.items():
     extra[k].append(lowerCamelCase_     )

   UpperCAmelCase__       :	Optional[Any]        =       {}
   if output_rle_mask:
    UpperCAmelCase__       :	Any        =       rle_mask

   if output_bboxes_mask:
    UpperCAmelCase__       :	Union[str, Any]        =       bounding_boxes

   return {"masks": output_masks, "scores": iou_scores, **optional, **extra}








"""simple docstring"""


class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self    )    ->      Union[str, Any]:
          A				  =		{}




    def UpperCamelCase__		( self    )    ->      None:
          print(self.vertex    )
          for i in self.vertex:
                print(lowerCamelCase_ ,""" -> """ ,""" -> """.join([str(lowerCamelCase_    ) for j in self.vertex[i]]    )    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # check if vertex is already present,
          if from_vertex in self.vertex:
                self.vertex[from_vertex].append(lowerCamelCase_    )
          else:
                # else make a new vertex
                A				  =		[to_vertex]




    def UpperCamelCase__		( self    )    ->      None:
          # visited array for storing already visited nodes
          A				  =		[False] * len(self.vertex    )

          # call the recursive helper function
          for i in range(len(self.vertex    )    ):
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # mark start vertex as visited
          A				  =		True

          print(lowerCamelCase_ ,end=""" """    )

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase													=Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("DFS:")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3




