






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(__A						)
class 							__a							(  __A						):





  '''simple docstring'''



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


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


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


  def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=64 ,    UpperCamelCase__ = 0 ,    UpperCamelCase__ = 512 / 1500 ,    UpperCamelCase__ = 32 ,    UpperCamelCase__ = 1 ,    ):
      SCREAMING_SNAKE_CASE_   :	Dict													=   load_image(UpperCamelCase__       )
      SCREAMING_SNAKE_CASE_   :	Any													=   self.image_processor.size['longest_edge']
      SCREAMING_SNAKE_CASE_   :	Any													=   self.image_processor.generate_crop_boxes(
          UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
      SCREAMING_SNAKE_CASE_   :	List[str]													=   self.image_processor(images=UpperCamelCase__ ,    return_tensors='pt'       )

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

      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   grid_points.shape[1]
      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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 ,    UpperCamelCase__ ,    UpperCamelCase__       ):
          SCREAMING_SNAKE_CASE_   :	Tuple													=   grid_points[:, i : i + points_per_batch, :, :]
          SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   input_labels[:, i : i + points_per_batch]
          SCREAMING_SNAKE_CASE_   :	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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=0.88 ,    UpperCamelCase__=0.95 ,    UpperCamelCase__=0 ,    UpperCamelCase__=1 ,    ):
      SCREAMING_SNAKE_CASE_   :	Any													=   model_inputs.pop('input_boxes'       )
      SCREAMING_SNAKE_CASE_   :	int													=   model_inputs.pop('is_last'       )
      SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model_inputs.pop('original_sizes'       ).tolist()
      SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model_inputs.pop('reshaped_input_sizes'       ).tolist()

      SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.model(**UpperCamelCase__       )

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






  def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=False ,    UpperCamelCase__=False ,    UpperCamelCase__=0.7 ,    ):
      SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   []
      SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []
      SCREAMING_SNAKE_CASE_   :	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'       )       )

      SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.cat(UpperCamelCase__       )
      SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.cat(UpperCamelCase__       )
      SCREAMING_SNAKE_CASE_   :	str													=   self.image_processor.post_process_for_mask_generation(
          UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )

      SCREAMING_SNAKE_CASE_   :	Any													=   defaultdict(UpperCamelCase__       )
      for output in model_outputs:
          for k, v in output.items():
              extra[k].append(UpperCamelCase__       )

      SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {}
      if output_rle_mask:
          SCREAMING_SNAKE_CASE_   :	Tuple													=   rle_mask

      if output_bboxes_mask:
          SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   bounding_boxes

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







from timeit import timeit



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   0
				while number:
								number &= number - 1
								result += 1
				return result



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
				while number:
								if number % 2 == 1:
												result += 1
								number >>= 1
				return result



def 					_lowerCamelCase(			):





				'''simple docstring'''







				def do_benchmark(lowerCAmelCase__				:		int      ) -> None:
								SCREAMING_SNAKE_CASE_   :	int													=   'import __main__ as z'
								print(F'''Benchmark when {number = }:'''      )
								print(F'''{get_set_bits_count_using_modulo_operator(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	int													=   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,    setup=lowerCAmelCase__      )
								print(F'''timeit() runs in {timing} seconds'''      )
								print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   timeit(
								    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,    setup=lowerCAmelCase__    ,    )
								print(F'''timeit() runs in {timing} seconds'''      )

				for number in (25, 37, 58, 0):
								do_benchmark(lowerCAmelCase__      )
								print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()