
import math
import unittest


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       assert isinstance(__lowerCamelCase				,					__lowerCamelCase    ) and (
           number >= 0
       ), "'number' must been an int and positive"

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5				,					int(math.sqrt(__lowerCamelCase    ) + 1    )				,					6    ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True






class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          self.assertTrue(is_prime(2 ) )
          self.assertTrue(is_prime(3 ) )
          self.assertTrue(is_prime(5 ) )
          self.assertTrue(is_prime(7 ) )
          self.assertTrue(is_prime(11 ) )
          self.assertTrue(is_prime(13 ) )
          self.assertTrue(is_prime(17 ) )
          self.assertTrue(is_prime(19 ) )
          self.assertTrue(is_prime(23 ) )
          self.assertTrue(is_prime(29 ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


          '''simple docstring'''


          with self.assertRaises(snake_case__ ):
                 is_prime(-19 )
          self.assertFalse(
              is_prime(0 )    ,						"Zero doesn't have any positive factors, primes must have exactly two."    ,						)
          self.assertFalse(
              is_prime(1 )    ,						"One only has 1 positive factor, primes must have exactly two."    ,						)
          self.assertFalse(is_prime(2 * 2 ) )
          self.assertFalse(is_prime(2 * 3 ) )
          self.assertFalse(is_prime(3 * 3 ) )
          self.assertFalse(is_prime(3 * 5 ) )
          self.assertFalse(is_prime(3 * 5 * 7 ) )


if __name__ == "__main__":
   unittest.main()










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

__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''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 		_lowerCamelCase			(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''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							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     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 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=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     ,		):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor.size['''longest_edge''']
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.generate_crop_boxes(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		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(__SCREAMING_SNAKE_CASE     ,		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     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __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 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=0.88     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.95     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		__SCREAMING_SNAKE_CASE							:     int=1     ,		):


           '''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(**__SCREAMING_SNAKE_CASE)

           # 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(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		binarize=__SCREAMING_SNAKE_CASE)
           __a						       =				model_outputs['''iou_scores''']
           __a       ,   __a       ,   __a						       =				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 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Dict=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     int=0.7     ,		):


           '''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(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.post_process_for_mask_generation(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

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

           __a						       =				{}
           if output_rle_mask:
               __a						       =				rle_mask

           if output_bboxes_mask:
               __a						       =				bounding_boxes

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





