




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

a													=			logging.get_logger(__name__)




@add_end_docstrings(__lowercase     )
class       _A				(		__lowercase     ):






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






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






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






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE = 0						,		_SCREAMING_SNAKE_CASE = 512 / 1500						,		_SCREAMING_SNAKE_CASE = 32						,		_SCREAMING_SNAKE_CASE = 1						,		):
        _UpperCAmelCase      =			load_image(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			self.image_processor.size["""longest_edge"""]
        _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.generate_crop_boxes(
            _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			self.image_processor(images=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)

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

        _UpperCAmelCase      =			grid_points.shape[1]
        _UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
              _UpperCAmelCase      =			grid_points[:, i : i + points_per_batch, :, :]
              _UpperCAmelCase      =			input_labels[:, i : i + points_per_batch]
              _UpperCAmelCase      =			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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0.88						,		_SCREAMING_SNAKE_CASE=0.95						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		):
        _UpperCAmelCase      =			model_inputs.pop("""input_boxes"""			)
        _UpperCAmelCase      =			model_inputs.pop("""is_last"""			)
        _UpperCAmelCase      =			model_inputs.pop("""original_sizes"""			).tolist()
        _UpperCAmelCase      =			model_inputs.pop("""reshaped_input_sizes"""			).tolist()

        _UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)

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






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=0.7						,		):
        _UpperCAmelCase      =			[]
        _UpperCAmelCase      =			[]
        _UpperCAmelCase      =			[]
        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      =			torch.cat(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			torch.cat(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.post_process_for_mask_generation(
            _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

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

        _UpperCAmelCase      =			{}
        if output_rle_mask:
              _UpperCAmelCase      =			rle_mask

        if output_bboxes_mask:
              _UpperCAmelCase      =			bounding_boxes

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





from __future__ import annotations

from scipy.special import comb  # type: ignore




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) - 1






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								_UpperCAmelCase      =			[]
								for i in range(len(self.list_of_points			)			):
														# basis function for each i
														output_values.append(
														    comb(self.degree						,		_SCREAMING_SNAKE_CASE			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
								# the basis must sum up to 1 for it to produce a valid Bezier curve.
								assert round(sum(_SCREAMING_SNAKE_CASE			)						,		5			) == 1
								return output_values






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."

								_UpperCAmelCase      =			self.basis_function(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			0.0
								_UpperCAmelCase      =			0.0
								for i in range(len(self.list_of_points			)			):
														# For all points, sum up the product of i-th basis function and i-th point.
														x += basis_function[i] * self.list_of_points[i][0]
														y += basis_function[i] * self.list_of_points[i][1]
								return (x, y)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = 0.01			):
								from matplotlib import pyplot as plt  # type: ignore

								_UpperCAmelCase      =			[]  # x coordinates of points to plot
								_UpperCAmelCase      =			[]  # y coordinates of points to plot

								_UpperCAmelCase      =			0.0
								while t <= 1:
														_UpperCAmelCase      =			self.bezier_curve_function(_SCREAMING_SNAKE_CASE			)
														to_plot_x.append(value[0]			)
														to_plot_y.append(value[1]			)
														t += step_size

								_UpperCAmelCase      =			[i[0] for i in self.list_of_points]
								_UpperCAmelCase      =			[i[1] for i in self.list_of_points]

								plt.plot(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""blue"""						,		label="""Curve of Degree """ + str(self.degree			)						,		)
								plt.scatter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""red"""						,		label="""Control Points"""			)
								plt.legend()
								plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3