

'''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					:			List[str]       =	logging.get_logger(__name__)







@add_end_docstrings(a   )
class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def __init__(						self   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      super().__init__(**__SCREAMING_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(__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			str   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {}
      __SCREAMING_SNAKE_CASE      = {}
      __SCREAMING_SNAKE_CASE      = {}
      # preprocess args
      if "points_per_batch" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""points_per_batch"""]
      if "points_per_crop" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""points_per_crop"""]
      if "crops_n_layers" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""crops_n_layers"""]
      if "crop_overlap_ratio" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""crop_overlap_ratio"""]
      if "crop_n_points_downscale_factor" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""crop_n_points_downscale_factor"""]
      # postprocess args
      if "pred_iou_thresh" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""pred_iou_thresh"""]
      if "stability_score_offset" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""stability_score_offset"""]
      if "mask_threshold" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""mask_threshold"""]
      if "stability_score_thresh" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""stability_score_thresh"""]
      if "crops_nms_thresh" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""crops_nms_thresh"""]
      if "output_rle_mask" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""output_rle_mask"""]
      if "output_bboxes_mask" in kwargs:
           __SCREAMING_SNAKE_CASE      = kwargs["""output_bboxes_mask"""]
      return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					List[str]:


      """simple docstring"""




      return super().__call__(__SCREAMING_SNAKE_CASE   ,				*__SCREAMING_SNAKE_CASE   ,				num_workers=__SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Tuple=64   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			float = 512 / 1_500   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = 32   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = 1   ,				)    ->					Tuple:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE      = grid_points.shape[1]
      __SCREAMING_SNAKE_CASE      = 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   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = grid_points[:, i : i + points_per_batch, :, :]
           __SCREAMING_SNAKE_CASE      = input_labels[:, i : i + points_per_batch]
           __SCREAMING_SNAKE_CASE      = 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   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.88   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.95   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				)    ->					Optional[int]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = self.model(**__SCREAMING_SNAKE_CASE     )

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


 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			str=0.7   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = []
      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      = torch.cat(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = torch.cat(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processor.post_process_for_mask_generation(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = {}
      if output_rle_mask:
           __SCREAMING_SNAKE_CASE      = rle_mask

      if output_bboxes_mask:
           __SCREAMING_SNAKE_CASE      = bounding_boxes

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









'''simple docstring'''



from math import isqrt







def   a__			(    a__		):




     """simple docstring"""


     return all(number % divisor != 0 for divisor in range(2 ,       isqrt(a__		) + 1		)		)







def   a__			(    a__ = 10**6		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 7
     while prime_candidate < max_prime:
          primes_count += is_prime(a__		)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
  print(f"""{solution() = }""")






