

"""simple docstring"""






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(__a     )
class        _lowercase					(     __a     ):







     def __init__(      self ,						**UpperCamelCase_       ):
      super().__init__(**a_       )
      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(a_       )







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







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=64 ,						UpperCamelCase_ = 0 ,						UpperCamelCase_ = 512 / 1500 ,						UpperCamelCase_ = 32 ,						UpperCamelCase_ = 1 ,						):
      __magic_name__     =      load_image(a_       )
      __magic_name__     =      self.image_processor.size["""longest_edge"""]
      __magic_name__     =      self.image_processor.generate_crop_boxes(
          a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_       )
      __magic_name__     =      self.image_processor(images=a_ ,						return_tensors='''pt'''       )

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

      __magic_name__     =      grid_points.shape[1]
      __magic_name__     =      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 ,						a_ ,						a_       ):
       __magic_name__     =      grid_points[:, i : i + points_per_batch, :, :]
       __magic_name__     =      input_labels[:, i : i + points_per_batch]
       __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0.8_8 ,						UpperCamelCase_=0.9_5 ,						UpperCamelCase_=0 ,						UpperCamelCase_=1 ,						):
      __magic_name__     =      model_inputs.pop('''input_boxes'''       )
      __magic_name__     =      model_inputs.pop('''is_last'''       )
      __magic_name__     =      model_inputs.pop('''original_sizes'''       ).tolist()
      __magic_name__     =      model_inputs.pop('''reshaped_input_sizes'''       ).tolist()

      __magic_name__     =      self.model(**a_       )

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=False ,						UpperCamelCase_=False ,						UpperCamelCase_=0.7 ,						):
      __magic_name__     =      []
      __magic_name__     =      []
      __magic_name__     =      []
      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'''       )       )

      __magic_name__     =      torch.cat(a_       )
      __magic_name__     =      torch.cat(a_       )
      __magic_name__     =      self.image_processor.post_process_for_mask_generation(
          a_ ,						a_ ,						a_ ,						a_       )

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

      __magic_name__     =      {}
      if output_rle_mask:
       __magic_name__     =      rle_mask

      if output_bboxes_mask:
       __magic_name__     =      bounding_boxes

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




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

