



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

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE__   )
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		**lowerCamelCase__		):
        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       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        A			:	Tuple										=       {}
        A			:	Optional[int]										=       {}
        A			:	List[str]										=       {}
        # preprocess args
        if "points_per_batch" in kwargs:
            A			:	Optional[int]										=       kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
            A			:	int										=       kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
            A			:	str										=       kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
            A			:	List[Any]										=       kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
            A			:	List[str]										=       kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            A			:	Tuple										=       kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
            A			:	int										=       kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
            A			:	Any										=       kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
            A			:	Union[str, Any]										=       kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
            A			:	Optional[Any]										=       kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
            A			:	Any										=       kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
            A			:	Union[str, Any]										=       kwargs["""output_bboxes_mask"""]
        return preprocess_kwargs, forward_params, postprocess_kwargs






    def __call__(	self,		lowerCamelCase__,		*lowerCamelCase__,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
        return super().__call__(lowerCamelCase__,		*lowerCamelCase__,		num_workers=lowerCamelCase__,		batch_size=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=64,		lowerCamelCase__ = 0,		lowerCamelCase__ = 512 / 1500,		lowerCamelCase__ = 32,		lowerCamelCase__ = 1,		):
        A			:	str										=       load_image(lowerCamelCase__		)
        A			:	Any										=       self.image_processor.size["""longest_edge"""]
        A					,							A					,							A					,							A			:	str										=       self.image_processor.generate_crop_boxes(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A			:	str										=       self.image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		)

        with self.device_placement():
            if self.framework == "pt":
                A			:	str										=       self.get_inference_context()
                with inference_context():
                    A			:	Dict										=       self._ensure_tensor_on_device(lowerCamelCase__,		device=self.device		)
                    A			:	Dict										=       self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
                    A			:	Optional[int]										=       image_embeddings

        A			:	Dict										=       grid_points.shape[1]
        A			:	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__		):
            A			:	Optional[Any]										=       grid_points[:, i : i + points_per_batch, :, :]
            A			:	str										=       input_labels[:, i : i + points_per_batch]
            A			:	Optional[int]										=       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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=0.88,		lowerCamelCase__=0.95,		lowerCamelCase__=0,		lowerCamelCase__=1,		):
        A			:	Tuple										=       model_inputs.pop("""input_boxes"""		)
        A			:	List[Any]										=       model_inputs.pop("""is_last"""		)
        A			:	List[str]										=       model_inputs.pop("""original_sizes"""		).tolist()
        A			:	int										=       model_inputs.pop("""reshaped_input_sizes"""		).tolist()

        A			:	int										=       self.model(**lowerCamelCase__		)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        A			:	Union[str, Any]										=       model_outputs["""pred_masks"""]
        A			:	int										=       self.image_processor.post_process_masks(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		binarize=lowerCamelCase__		)
        A			:	List[Any]										=       model_outputs["""iou_scores"""]
        A					,							A					,							A			:	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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=0.7,		):
        A			:	Optional[Any]										=       []
        A			:	Tuple										=       []
        A			:	Union[str, Any]										=       []
        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"""		)		)

        A			:	Optional[Any]										=       torch.cat(lowerCamelCase__		)
        A			:	Optional[Any]										=       torch.cat(lowerCamelCase__		)
        A					,							A					,							A					,							A			:	int										=       self.image_processor.post_process_for_mask_generation(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)

        A			:	List[str]										=       defaultdict(lowerCamelCase__		)
        for output in model_outputs:
            for k, v in output.items():
                extra[k].append(lowerCamelCase__		)

        A			:	List[Any]										=       {}
        if output_rle_mask:
            A			:	List[str]										=       rle_mask

        if output_bboxes_mask:
            A			:	Tuple										=       bounding_boxes

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







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [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],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


