


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

__lowerCAmelCase			=							logging.get_logger(__name__)





@add_end_docstrings(__UpperCamelCase				)
class   __a      (   __UpperCamelCase				):
 def __init__(       self    ,			**lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       {}
        lowercase__:       List[str]        =       {}
        # preprocess args
        if "points_per_batch" in kwargs:
               lowercase__:       Tuple        =       kwargs['points_per_batch']
        if "points_per_crop" in kwargs:
               lowercase__:       Optional[Any]        =       kwargs['points_per_crop']
        if "crops_n_layers" in kwargs:
               lowercase__:       Dict        =       kwargs['crops_n_layers']
        if "crop_overlap_ratio" in kwargs:
               lowercase__:       str        =       kwargs['crop_overlap_ratio']
        if "crop_n_points_downscale_factor" in kwargs:
               lowercase__:       str        =       kwargs['crop_n_points_downscale_factor']
        # postprocess args
        if "pred_iou_thresh" in kwargs:
               lowercase__:       Dict        =       kwargs['pred_iou_thresh']
        if "stability_score_offset" in kwargs:
               lowercase__:       Optional[int]        =       kwargs['stability_score_offset']
        if "mask_threshold" in kwargs:
               lowercase__:       List[str]        =       kwargs['mask_threshold']
        if "stability_score_thresh" in kwargs:
               lowercase__:       Optional[int]        =       kwargs['stability_score_thresh']
        if "crops_nms_thresh" in kwargs:
               lowercase__:       Dict        =       kwargs['crops_nms_thresh']
        if "output_rle_mask" in kwargs:
               lowercase__:       int        =       kwargs['output_rle_mask']
        if "output_bboxes_mask" in kwargs:
               lowercase__:       Optional[Any]        =       kwargs['output_bboxes_mask']
        return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(       self    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return super().__call__(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			num_workers=lowerCAmelCase__    ,			batch_size=lowerCAmelCase__    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=64    ,			lowerCAmelCase__ = 0    ,			lowerCAmelCase__ = 512 / 1_500    ,			lowerCAmelCase__ = 32    ,			lowerCAmelCase__ = 1    ,			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       load_image(lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       self.image_processor.size['longest_edge']
        lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       int        =       self.image_processor.generate_crop_boxes(
            lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       self.image_processor(images=lowerCAmelCase__    ,			return_tensors='pt'			)

        with self.device_placement():
               if self.framework == "pt":
                      lowercase__:       Union[str, Any]        =       self.get_inference_context()
                      with inference_context():
                             lowercase__:       Tuple        =       self._ensure_tensor_on_device(lowerCAmelCase__    ,			device=self.device			)
                             lowercase__:       Any        =       self.model.get_image_embeddings(model_inputs.pop('pixel_values'			)			)
                             lowercase__:       Optional[Any]        =       image_embeddings

        lowercase__:       Union[str, Any]        =       grid_points.shape[1]
        lowercase__:       str        =       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__			):
               lowercase__:       Any        =       grid_points[:, i : i + points_per_batch, :, :]
               lowercase__:       List[str]        =       input_labels[:, i : i + points_per_batch]
               lowercase__:       str        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0.8_8    ,			lowerCAmelCase__=0.9_5    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[Any]        =       model_inputs.pop('input_boxes'			)
        lowercase__:       Union[str, Any]        =       model_inputs.pop('is_last'			)
        lowercase__:       Tuple        =       model_inputs.pop('original_sizes'			).tolist()
        lowercase__:       List[Any]        =       model_inputs.pop('reshaped_input_sizes'			).tolist()

        lowercase__:       Dict        =       self.model(**lowerCAmelCase__			)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        lowercase__:       str        =       model_outputs['pred_masks']
        lowercase__:       List[str]        =       self.image_processor.post_process_masks(
            lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			binarize=lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       model_outputs['iou_scores']
        lowercase__      ,						lowercase__      ,						lowercase__:       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0.7    ,			) ->    str:




        '''simple docstring'''
        lowercase__:       List[str]        =       []
        lowercase__:       Optional[int]        =       []
        lowercase__:       Optional[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'			)			)

        lowercase__:       int        =       torch.cat(lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       torch.cat(lowerCAmelCase__			)
        lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       str        =       self.image_processor.post_process_for_mask_generation(
            lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)

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

        lowercase__:       Union[str, Any]        =       {}
        if output_rle_mask:
               lowercase__:       Dict        =       rle_mask

        if output_bboxes_mask:
               lowercase__:       Dict        =       bounding_boxes

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






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


