



"""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

UpperCAmelCase						:		Dict			= logging.get_logger(__name__)


@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Dict ,  **lowerCAmelCase_			:				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 					_UpperCAmelCase	(				self			:				str ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







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

    def __call__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str] ,  *lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Optional[int]=None ,  lowerCAmelCase_			:				List[str]=None ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=6_4 ,  lowerCAmelCase_			:				int = 0 ,  lowerCAmelCase_			:				float = 5_1_2 / 1_5_0_0 ,  lowerCAmelCase_			:				Optional[int] = 3_2 ,  lowerCAmelCase_			:				Optional[int] = 1 ,  ):







      """simple docstring"""







      lowercase_								= load_image(lowerCAmelCase_)
      lowercase_								= self.image_processor.size["""longest_edge"""]
      lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= self.image_processor.generate_crop_boxes(
          lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)
      lowercase_								= self.image_processor(images=lowerCAmelCase_ ,  return_tensors="""pt""")

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

      lowercase_								= grid_points.shape[1]
      lowercase_								= 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_								= grid_points[:, i : i + points_per_batch, :, :]
        lowercase_								= input_labels[:, i : i + points_per_batch]
        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Any=0.88 ,  lowerCAmelCase_			:				Tuple=0.95 ,  lowerCAmelCase_			:				str=0 ,  lowerCAmelCase_			:				Dict=1 ,  ):







      """simple docstring"""







      lowercase_								= model_inputs.pop("""input_boxes""")
      lowercase_								= model_inputs.pop("""is_last""")
      lowercase_								= model_inputs.pop("""original_sizes""").tolist()
      lowercase_								= model_inputs.pop("""reshaped_input_sizes""").tolist()

      lowercase_								= self.model(**lowerCAmelCase_)

      # post processing happens here in order to avoid CPU GPU copies of ALL the masks
      lowercase_								= model_outputs["""pred_masks"""]
      lowercase_								= self.image_processor.post_process_masks(
          lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  binarize=lowerCAmelCase_)
      lowercase_								= model_outputs["""iou_scores"""]
      lowercase_					,    lowercase_					,    lowercase_								= 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 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Any=False ,  lowerCAmelCase_			:				List[Any]=False ,  lowerCAmelCase_			:				Tuple=0.7 ,  ):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= []
      lowercase_								= []
      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_								= torch.cat(lowerCAmelCase_)
      lowercase_								= torch.cat(lowerCAmelCase_)
      lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= self.image_processor.post_process_for_mask_generation(
          lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)

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

      lowercase_								= {}
      if output_rle_mask:
        lowercase_								= rle_mask

      if output_bboxes_mask:
        lowercase_								= bounding_boxes

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





"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						list[list[int]]:







  '''simple docstring'''


  lowercase_								= []
  if len(__lowerCAmelCase							) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase							)							):
    lowercase_								= nums.pop(0							)
    lowercase_								= permute(__lowerCAmelCase							)
    for perm in permutations:
      perm.append(__lowerCAmelCase							)
    result.extend(__lowerCAmelCase							)
    nums.append(__lowerCAmelCase							)
  return result


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  def backtrack(__lowerCAmelCase							):
    if start == len(__lowerCAmelCase							) - 1:
      output.append(nums[:]							)
    else:
      for i in range(__lowerCAmelCase ,  len(__lowerCAmelCase							)							):
        lowercase_					,    lowercase_								= nums[i], nums[start]
        backtrack(start + 1							)
        lowercase_					,    lowercase_								= nums[i], nums[start]  # backtrack

  lowercase_								= []
  backtrack(0							)
  return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       UpperCAmelCase						:		Optional[Any]			= permutea([1, 2, 3])
       print(res)
       doctest.testmod()

