



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 				__A					( A_      ):





			def __init__(self							,    **__magic_name__				):
						super().__init__(**__magic_name__				)
						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(__magic_name__				)





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





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__=64							,    __magic_name__ = 0							,    __magic_name__ = 512 / 1500							,    __magic_name__ = 32							,    __magic_name__ = 1							,    ):
						lowerCamelCase__ :      str								=		load_image(__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		self.image_processor.size["""longest_edge"""]
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		self.image_processor.generate_crop_boxes(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      List[str]								=		self.image_processor(images=__magic_name__							,    return_tensors="""pt"""				)

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

						lowerCamelCase__ :      str								=		grid_points.shape[1]
						lowerCamelCase__ :      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							,    __magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Any								=		grid_points[:, i : i + points_per_batch, :, :]
									lowerCamelCase__ :      List[str]								=		input_labels[:, i : i + points_per_batch]
									lowerCamelCase__ :      Any								=		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        _snake_case    (self							,    __magic_name__							,    __magic_name__=0.88							,    __magic_name__=0.95							,    __magic_name__=0							,    __magic_name__=1							,    ):
						lowerCamelCase__ :      Tuple								=		model_inputs.pop("""input_boxes"""				)
						lowerCamelCase__ :      List[str]								=		model_inputs.pop("""is_last"""				)
						lowerCamelCase__ :      Union[str, Any]								=		model_inputs.pop("""original_sizes"""				).tolist()
						lowerCamelCase__ :      List[str]								=		model_inputs.pop("""reshaped_input_sizes"""				).tolist()

						lowerCamelCase__ :      Any								=		self.model(**__magic_name__				)

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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__=False							,    __magic_name__=False							,    __magic_name__=0.7							,    ):
						lowerCamelCase__ :      int								=		[]
						lowerCamelCase__ :      Optional[int]								=		[]
						lowerCamelCase__ :      List[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"""				)				)

						lowerCamelCase__ :      Union[str, Any]								=		torch.cat(__magic_name__				)
						lowerCamelCase__ :      Any								=		torch.cat(__magic_name__				)
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.image_processor.post_process_for_mask_generation(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      Optional[int]								=		defaultdict(__magic_name__				)
						for output in model_outputs:
									for k, v in output.items():
												extra[k].append(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		{}
						if output_rle_mask:
									lowerCamelCase__ :      Dict								=		rle_mask

						if output_bboxes_mask:
									lowerCamelCase__ :      str								=		bounding_boxes

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








import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 				__A					( unittest.TestCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		Vector([1, 2, 3]				)
						self.assertEqual(x.component(0				)							,    1				)
						self.assertEqual(x.component(2				)							,    3				)
						lowerCamelCase__ :      str								=		Vector()





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		Vector([0, 0, 0, 0, 0, 1]				)
						self.assertEqual(str(__magic_name__				)							,    """(0,0,0,0,0,1)"""				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		Vector([1, 2, 3, 4]				)
						self.assertEqual(len(__magic_name__				)							,    4				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		Vector([1, 2]				)
						lowerCamelCase__ :      str								=		Vector([1, 2, 3, 4, 5]				)
						lowerCamelCase__ :      int								=		Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]				)
						lowerCamelCase__ :      int								=		Vector([1, -1, 1, -1, 2, -3, 4, -5]				)
						self.assertAlmostEqual(x.euclidean_length()							,    2.2_36							,    3				)
						self.assertAlmostEqual(y.euclidean_length()							,    7.4_16							,    3				)
						self.assertEqual(z.euclidean_length()							,    0				)
						self.assertAlmostEqual(w.euclidean_length()							,    7.6_16							,    3				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		Vector([1, 2, 3]				)
						lowerCamelCase__ :      Union[str, Any]								=		Vector([1, 1, 1]				)
						self.assertEqual((x + y).component(0				)							,    2				)
						self.assertEqual((x + y).component(1				)							,    3				)
						self.assertEqual((x + y).component(2				)							,    4				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		Vector([1, 2, 3]				)
						lowerCamelCase__ :      Any								=		Vector([1, 1, 1]				)
						self.assertEqual((x - y).component(0				)							,    0				)
						self.assertEqual((x - y).component(1				)							,    1				)
						self.assertEqual((x - y).component(2				)							,    2				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		Vector([1, 2, 3]				)
						lowerCamelCase__ :      int								=		Vector([2, -1, 4]				)  # for test of dot product
						lowerCamelCase__ :      List[Any]								=		Vector([1, -2, -1]				)
						self.assertEqual(str(x * 3.0				)							,    """(3.0,6.0,9.0)"""				)
						self.assertEqual((a * b)							,    0				)





			def        _snake_case    (self				):
						self.assertEqual(str(zero_vector(10				)				).count("""0"""				)							,    10				)





			def        _snake_case    (self				):
						self.assertEqual(str(unit_basis_vector(3							,    1				)				)							,    """(0,1,0)"""				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		Vector([1, 2, 3]				)
						lowerCamelCase__ :      str								=		Vector([1, 0, 1]				)
						self.assertEqual(str(axpy(2							,    __magic_name__							,    __magic_name__				)				)							,    """(3,4,7)"""				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		Vector([1, 0, 0, 0, 0, 0]				)
						lowerCamelCase__ :      int								=		x.copy()
						self.assertEqual(str(__magic_name__				)							,    str(__magic_name__				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		Vector([1, 0, 0]				)
						x.change_component(0							,    0				)
						x.change_component(1							,    1				)
						self.assertEqual(str(__magic_name__				)							,    """(0,1,0)"""				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""							,    str(__magic_name__				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						lowerCamelCase__ :      int								=		[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
						for x in range(a.height()				):
									for y in range(a.width()				):
												self.assertEqual(minors[x][y]							,    a.minor(__magic_name__							,    __magic_name__				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						lowerCamelCase__ :      Optional[Any]								=		[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
						for x in range(a.height()				):
									for y in range(a.width()				):
												self.assertEqual(cofactors[x][y]							,    a.cofactor(__magic_name__							,    __magic_name__				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						self.assertEqual(-5							,    a.determinant()				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]							,    3							,    3				)
						lowerCamelCase__ :      Optional[int]								=		Vector([1, 2, 3]				)
						self.assertEqual("""(14,32,50)"""							,    str(a * x				)				)
						self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""							,    str(a * 2				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						a.change_component(0							,    2							,    5				)
						self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""							,    str(__magic_name__				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						self.assertEqual(7							,    a.component(2							,    1				)							,    0.01				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						lowerCamelCase__ :      int								=		Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]							,    3							,    3				)
						self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""							,    str(a + b				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,    3							,    3				)
						lowerCamelCase__ :      Optional[Any]								=		Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]							,    3							,    3				)
						self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""							,    str(a - b				)				)





			def        _snake_case    (self				):
						self.assertEqual(
						    """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""							,    str(square_zero_matrix(5				)				)							,    )


if __name__ == "__main__":
				unittest.main()



