


'''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    :Union[str, Any]				       =     logging.get_logger(__name__)



@add_end_docstrings(_a )
class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				**snake_case  :	Optional[Any]    )		->							Optional[int]:
          super().__init__(**snake_case    )
          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(snake_case    )

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

     def __call__(       self  :	Dict							,				snake_case  :	str							,				*snake_case  :	Dict							,				snake_case  :	List[str]=None							,				snake_case  :	Dict=None							,				**snake_case  :	List[str]    )		->							Union[str, Any]:
          return super().__call__(snake_case							,				*snake_case							,				num_workers=snake_case							,				batch_size=snake_case							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	Any							,				snake_case  :	Dict=64							,				snake_case  :	int = 0							,				snake_case  :	float = 512 / 1500							,				snake_case  :	Optional[int] = 32							,				snake_case  :	Optional[int] = 1							,				)		->							Dict:
          __UpperCAmelCase			:				List[Any]													=    load_image(snake_case    )
          __UpperCAmelCase			:				Any													=    self.image_processor.size['''longest_edge''']
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Union[str, Any]													=    self.image_processor.generate_crop_boxes(
              snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    self.image_processor(images=snake_case							,				return_tensors='''pt'''    )

          with self.device_placement():
               if self.framework == "pt":
                    __UpperCAmelCase			:				List[Any]													=    self.get_inference_context()
                    with inference_context():
                         __UpperCAmelCase			:				List[Any]													=    self._ensure_tensor_on_device(snake_case							,				device=self.device    )
                         __UpperCAmelCase			:				str													=    self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''    )    )
                         __UpperCAmelCase			:				Dict													=    image_embeddings

          __UpperCAmelCase			:				Union[str, Any]													=    grid_points.shape[1]
          __UpperCAmelCase			:				int													=    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							,				snake_case							,				snake_case    ):
               __UpperCAmelCase			:				Dict													=    grid_points[:, i : i + points_per_batch, :, :]
               __UpperCAmelCase			:				str													=    input_labels[:, i : i + points_per_batch]
               __UpperCAmelCase			:				Union[str, Any]													=    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  :	List[Any]							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]=0.88							,				snake_case  :	Optional[Any]=0.95							,				snake_case  :	Any=0							,				snake_case  :	Optional[int]=1							,				)		->							Optional[Any]:
          __UpperCAmelCase			:				int													=    model_inputs.pop('''input_boxes'''    )
          __UpperCAmelCase			:				List[Any]													=    model_inputs.pop('''is_last'''    )
          __UpperCAmelCase			:				int													=    model_inputs.pop('''original_sizes'''    ).tolist()
          __UpperCAmelCase			:				List[Any]													=    model_inputs.pop('''reshaped_input_sizes'''    ).tolist()

          __UpperCAmelCase			:				List[str]													=    self.model(**snake_case    )

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






     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]=False							,				snake_case  :	List[str]=False							,				snake_case  :	Tuple=0.7							,				)		->							List[Any]:
          __UpperCAmelCase			:				Dict													=    []
          __UpperCAmelCase			:				int													=    []
          __UpperCAmelCase			:				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'''    )    )

          __UpperCAmelCase			:				Optional[Any]													=    torch.cat(snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.cat(snake_case    )
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[int]													=    self.image_processor.post_process_for_mask_generation(
              snake_case							,				snake_case							,				snake_case							,				snake_case    )

          __UpperCAmelCase			:				Union[str, Any]													=    defaultdict(snake_case    )
          for output in model_outputs:
               for k, v in output.items():
                    extra[k].append(snake_case    )

          __UpperCAmelCase			:				List[Any]													=    {}
          if output_rle_mask:
               __UpperCAmelCase			:				int													=    rle_mask

          if output_bboxes_mask:
               __UpperCAmelCase			:				Union[str, Any]													=    bounding_boxes

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



'''simple docstring'''



def 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()