




'''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_    	=				logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE )
class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							**A  ):







             '''simple docstring'''







             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        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						{}
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{}
             # preprocess args
             if "points_per_batch" in kwargs:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs['points_per_batch']
             if "points_per_crop" in kwargs:
                   SCREAMING_SNAKE_CASE   :      str    					=						kwargs['points_per_crop']
             if "crops_n_layers" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Any    					=						kwargs['crops_n_layers']
             if "crop_overlap_ratio" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						kwargs['crop_overlap_ratio']
             if "crop_n_points_downscale_factor" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						kwargs['crop_n_points_downscale_factor']
             # postprocess args
             if "pred_iou_thresh" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						kwargs['pred_iou_thresh']
             if "stability_score_offset" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						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[int]    					=						kwargs['stability_score_thresh']
             if "crops_nms_thresh" in kwargs:
                   SCREAMING_SNAKE_CASE   :      int    					=						kwargs['crops_nms_thresh']
             if "output_rle_mask" in kwargs:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						kwargs['output_rle_mask']
             if "output_bboxes_mask" in kwargs:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						kwargs['output_bboxes_mask']
             return preprocess_kwargs, forward_params, postprocess_kwargs







       def __call__(      self,							A,							*A,							A=None,							A=None,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A=64,							A = 0,							A = 512 / 1_500,							A = 32,							A = 1,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						load_image(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.image_processor.size['longest_edge']
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						self.image_processor.generate_crop_boxes(
                 A,							A,							A,							A,							A,							A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.image_processor(images=A,							return_tensors='pt'  )

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

             SCREAMING_SNAKE_CASE   :      List[str]    					=						grid_points.shape[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						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  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						grid_points[:, i : i + points_per_batch, :, :]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						input_labels[:, i : i + points_per_batch]
                   SCREAMING_SNAKE_CASE   :      str    					=						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,							A,							A=0.88,							A=0.95,							A=0,							A=1,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_inputs.pop('input_boxes'  )
             SCREAMING_SNAKE_CASE   :      Any    					=						model_inputs.pop('is_last'  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_inputs.pop('original_sizes'  ).tolist()
             SCREAMING_SNAKE_CASE   :      Dict    					=						model_inputs.pop('reshaped_input_sizes'  ).tolist()

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.model(**A  )

             # post processing happens here in order to avoid CPU GPU copies of ALL the masks
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model_outputs['pred_masks']
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.image_processor.post_process_masks(
                 A,							A,							A,							A,							binarize=A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model_outputs['iou_scores']
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						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        UpperCamelCase_							(      self,							A,							A=False,							A=False,							A=0.7,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[]
             SCREAMING_SNAKE_CASE   :      List[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   :      str    					=						torch.cat(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.cat(A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.image_processor.post_process_for_mask_generation(
                 A,							A,							A,							A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						defaultdict(A  )
             for output in model_outputs:
                   for k, v in output.items():
                         extra[k].append(A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{}
             if output_rle_mask:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						rle_mask

             if output_bboxes_mask:
                   SCREAMING_SNAKE_CASE   :      str    					=						bounding_boxes

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






from __future__ import annotations



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->list[str]:
   '''simple docstring'''






   if partitions <= 0:
      raise ValueError("partitions must be a positive number!"				)
   if partitions > number_of_bytes:
      raise ValueError("partitions can not > number_of_bytes!"				)
   snake_case_            =       number_of_bytes // partitions
   snake_case_            =       []
   for i in range(lowerCAmelCase_				):
      snake_case_            =       i * bytes_per_partition + 1
      snake_case_            =       (
          number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
      )
      allocation_list.append(F'''{start_bytes}-{end_bytes}'''				)
   return allocation_list


if __name__ == "__main__":
   import doctest

   doctest.testmod()




