



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

lowercase_						=       logging.get_logger(__name__)
@add_end_docstrings(a_)
class 			_snake_case     (   a_):




    def __init__(  self   :  Any,   **__lowercase   :  Dict  ):
       super().__init__(**lowercase_  )
       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(lowercase_  )




    def 							A__ (  self   :  Any,   **__lowercase   :  Any  ):
       lowercase__   					=			{}
       lowercase__   					=			{}
       lowercase__   					=			{}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowercase__   					=			kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
          lowercase__   					=			kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
          lowercase__   					=			kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
          lowercase__   					=			kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
          lowercase__   					=			kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowercase__   					=			kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
          lowercase__   					=			kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
          lowercase__   					=			kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
          lowercase__   					=			kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
          lowercase__   					=			kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
          lowercase__   					=			kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
          lowercase__   					=			kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs




    def __call__(  self   :  List[Any],   __lowercase   :  List[str],   *__lowercase   :  List[str],   __lowercase   :  List[Any]=None,   __lowercase   :  int=None,   **__lowercase   :  Union[str, Any]  ):
       return super().__call__(lowercase_,   *lowercase_,   num_workers=lowercase_,   batch_size=lowercase_,   **lowercase_  )




    def 							A__ (  self   :  Optional[int],   __lowercase   :  Union[str, Any],   __lowercase   :  Union[str, Any]=64,   __lowercase   :  int = 0,   __lowercase   :  float = 512 / 1500,   __lowercase   :  Optional[int] = 32,   __lowercase   :  Optional[int] = 1,   ):
       lowercase__   					=			load_image(lowercase_  )
       lowercase__   					=			self.image_processor.size['''longest_edge''']
       lowercase__   					=			self.image_processor.generate_crop_boxes(
           lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_  )
       lowercase__   					=			self.image_processor(images=lowercase_,   return_tensors="pt"  )

       with self.device_placement():
          if self.framework == "pt":
             lowercase__   					=			self.get_inference_context()
             with inference_context():
                lowercase__   					=			self._ensure_tensor_on_device(lowercase_,   device=self.device  )
                lowercase__   					=			self.model.get_image_embeddings(model_inputs.pop("pixel_values"  )  )
                lowercase__   					=			image_embeddings

       lowercase__   					=			grid_points.shape[1]
       lowercase__   					=			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,   lowercase_,   lowercase_  ):
          lowercase__   					=			grid_points[:, i : i + points_per_batch, :, :]
          lowercase__   					=			input_labels[:, i : i + points_per_batch]
          lowercase__   					=			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 							A__ (  self   :  List[Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Union[str, Any]=0.88,   __lowercase   :  Any=0.95,   __lowercase   :  Optional[Any]=0,   __lowercase   :  Optional[Any]=1,   ):
       lowercase__   					=			model_inputs.pop("input_boxes"  )
       lowercase__   					=			model_inputs.pop("is_last"  )
       lowercase__   					=			model_inputs.pop("original_sizes"  ).tolist()
       lowercase__   					=			model_inputs.pop("reshaped_input_sizes"  ).tolist()

       lowercase__   					=			self.model(**lowercase_  )

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




    def 							A__ (  self   :  Union[str, Any],   __lowercase   :  int,   __lowercase   :  List[Any]=False,   __lowercase   :  List[str]=False,   __lowercase   :  int=0.7,   ):
       lowercase__   					=			[]
       lowercase__   					=			[]
       lowercase__   					=			[]
       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"  )  )

       lowercase__   					=			torch.cat(lowercase_  )
       lowercase__   					=			torch.cat(lowercase_  )
       lowercase__   					=			self.image_processor.post_process_for_mask_generation(
           lowercase_,   lowercase_,   lowercase_,   lowercase_  )

       lowercase__   					=			defaultdict(lowercase_  )
       for output in model_outputs:
          for k, v in output.items():
             extra[k].append(lowercase_  )

       lowercase__   					=			{}
       if output_rle_mask:
          lowercase__   					=			rle_mask

       if output_bboxes_mask:
          lowercase__   					=			bounding_boxes

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









from graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




