

'''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(_snake_case			)
class 					A__      (      _snake_case			):



    def __init__(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           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__       )						->      Optional[int]:





           '''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		,				UpperCamelCase__		,				*UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           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		,				)						->      List[Any]:





           '''simple docstring'''
           A_						=      load_image(UpperCamelCase__       )
           A_						=      self.image_processor.size["""longest_edge"""]
           A_					,			A_					,			A_					,			A_						=      self.image_processor.generate_crop_boxes(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )
           A_						=      self.image_processor(images=UpperCamelCase__		,				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(UpperCamelCase__		,				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		,				UpperCamelCase__		,				UpperCamelCase__       ):
                  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		,				UpperCamelCase__		,				UpperCamelCase__=0.88		,				UpperCamelCase__=0.95		,				UpperCamelCase__=0		,				UpperCamelCase__=1		,				)						->      Optional[Any]:





           '''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(**UpperCamelCase__       )

           # 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(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				binarize=UpperCamelCase__       )
           A_						=      model_outputs["""iou_scores"""]
           A_					,			A_					,			A_						=      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		,				)						->      str:





           '''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(UpperCamelCase__       )
           A_						=      torch.cat(UpperCamelCase__       )
           A_					,			A_					,			A_					,			A_						=      self.image_processor.post_process_for_mask_generation(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )

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

           A_						=      {}
           if output_rle_mask:
                  A_						=      rle_mask

           if output_bboxes_mask:
                  A_						=      bounding_boxes

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



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
