


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(__SCREAMING_SNAKE_CASE			)
class 			A						(							__SCREAMING_SNAKE_CASE			):







    """simple docstring"""





    def __init__(				self			:	Any,**lowercase_			:	List[str]		)->   Union[str, Any]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Dict,**lowercase_			:	Optional[int]		)->   str:





        '''simple docstring'''







        A__   				=       {}
        A__   				=       {}
        A__   				=       {}
        # preprocess args
        if "points_per_batch" in kwargs:
            A__   				=       kwargs["points_per_batch"]
        if "points_per_crop" in kwargs:
            A__   				=       kwargs["points_per_crop"]
        if "crops_n_layers" in kwargs:
            A__   				=       kwargs["crops_n_layers"]
        if "crop_overlap_ratio" in kwargs:
            A__   				=       kwargs["crop_overlap_ratio"]
        if "crop_n_points_downscale_factor" in kwargs:
            A__   				=       kwargs["crop_n_points_downscale_factor"]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            A__   				=       kwargs["pred_iou_thresh"]
        if "stability_score_offset" in kwargs:
            A__   				=       kwargs["stability_score_offset"]
        if "mask_threshold" in kwargs:
            A__   				=       kwargs["mask_threshold"]
        if "stability_score_thresh" in kwargs:
            A__   				=       kwargs["stability_score_thresh"]
        if "crops_nms_thresh" in kwargs:
            A__   				=       kwargs["crops_nms_thresh"]
        if "output_rle_mask" in kwargs:
            A__   				=       kwargs["output_rle_mask"]
        if "output_bboxes_mask" in kwargs:
            A__   				=       kwargs["output_bboxes_mask"]
        return preprocess_kwargs, forward_params, postprocess_kwargs


    def __call__(				self			:	int,lowercase_			:	int,*lowercase_			:	Any,lowercase_			:	Dict=None,lowercase_			:	Any=None,**lowercase_			:	str		)->   Union[str, Any]:





        '''simple docstring'''







        return super().__call__(_SCREAMING_SNAKE_CASE,*_SCREAMING_SNAKE_CASE,num_workers=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE,**_SCREAMING_SNAKE_CASE		)


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[Any],lowercase_			:	Dict=6_4,lowercase_			:	int = 0,lowercase_			:	float = 5_1_2 / 1_5_0_0,lowercase_			:	Optional[int] = 3_2,lowercase_			:	Optional[int] = 1,)->   str:





        '''simple docstring'''







        A__   				=       load_image(_SCREAMING_SNAKE_CASE		)
        A__   				=       self.image_processor.size["longest_edge"]
        A__   				=       self.image_processor.generate_crop_boxes(
            _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE		)
        A__   				=       self.image_processor(images=_SCREAMING_SNAKE_CASE,return_tensors='pt'		)

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

        A__   				=       grid_points.shape[1]
        A__   				=       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		):
            A__   				=       grid_points[:, i : i + points_per_batch, :, :]
            A__   				=       input_labels[:, i : i + points_per_batch]
            A__   				=       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			:	Optional[Any],lowercase_			:	Tuple,lowercase_			:	Optional[int]=0.88,lowercase_			:	int=0.95,lowercase_			:	Tuple=0,lowercase_			:	List[str]=1,)->   List[str]:





        '''simple docstring'''







        A__   				=       model_inputs.pop('input_boxes'		)
        A__   				=       model_inputs.pop('is_last'		)
        A__   				=       model_inputs.pop('original_sizes'		).tolist()
        A__   				=       model_inputs.pop('reshaped_input_sizes'		).tolist()

        A__   				=       self.model(**_SCREAMING_SNAKE_CASE		)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        A__   				=       model_outputs["pred_masks"]
        A__   				=       self.image_processor.post_process_masks(
            _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,binarize=_SCREAMING_SNAKE_CASE		)
        A__   				=       model_outputs["iou_scores"]
        A__   				=       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 						snake_case__					(				self			:	Any,lowercase_			:	str,lowercase_			:	Optional[Any]=False,lowercase_			:	List[str]=False,lowercase_			:	Union[str, Any]=0.7,)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       []
        A__   				=       []
        A__   				=       []
        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__   				=       torch.cat(_SCREAMING_SNAKE_CASE		)
        A__   				=       torch.cat(_SCREAMING_SNAKE_CASE		)
        A__   				=       self.image_processor.post_process_for_mask_generation(
            _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE		)

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

        A__   				=       {}
        if output_rle_mask:
            A__   				=       rle_mask

        if output_bboxes_mask:
            A__   				=       bounding_boxes

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




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((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(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       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 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

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

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

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

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,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

