


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:


  class     __UpperCAmelCase							:


       """simple docstring"""


       @staticmethod
       def 	snake_case_  (	*__A  ,       **__A    ):
          pass




def  a   (lowerCAmelCase__   ):
   __a					=  hashlib.mda(image.tobytes()   )
   return m.hexdigest()[:10]




def  a   (lowerCAmelCase__   ):
   __a					=  np.array(lowerCAmelCase__   )
   __a					=  npimg.shape
   return {"hash": hashimage(lowerCAmelCase__   ), "shape": shape}


@is_pipeline_test
@require_vision
@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
     _lowerCamelCase   =      dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  MaskGenerationPipeline(model=__A  ,       image_processor=__A    )
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        pass


     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF"""    )
     def 	snake_case_  (	self    ):
        pass


     @slow
     @require_torch
     def 	snake_case_  (	self    ):
        __a					=  pipeline("""mask-generation"""  ,       model="""facebook/sam-vit-huge"""    )

        __a					=  image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""  ,       points_per_batch=256    )

        # Shortening by hashing
        __a					=  []
        for i, o in enumerate(outputs["""masks"""]    ):
           new_outupt += [{"mask": mask_to_test_readable(__A    ), "scores": outputs["scores"][i]}]

        # fmt: off
        self.assertEqual(
            nested_simplify(__A  ,       decimals=4    )  ,       [
                {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
                {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
                {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
                {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
                {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
                {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
                {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
                {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
                {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
                {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
                {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
                {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
                {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
                {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
                {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
                {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
                {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
                {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
                {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
                {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
                {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
                {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
                {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
                {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
                {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
                {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
                {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
            ]  ,       )
        # fmt: on


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        __a					=  """facebook/sam-vit-huge"""
        __a					=  pipeline("""mask-generation"""  ,       model=__A    )

        __a					=  image_segmenter(
            """http://images.cocodataset.org/val2017/000000039769.jpg"""  ,       pred_iou_thresh=1  ,       points_per_batch=256    )

        # Shortening by hashing
        __a					=  []
        for i, o in enumerate(outputs["""masks"""]    ):
           new_outupt += [{"mask": mask_to_test_readable(__A    ), "scores": outputs["scores"][i]}]

        self.assertEqual(
            nested_simplify(__A  ,       decimals=4    )  ,       [
                {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
                {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
            ]  ,       )




"""simple docstring"""


from math import isqrt






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [True] * max_number
  for i in range(2 ,isqrt(max_number - 1		) + 1		):
    if is_prime[i]:
      for j in range(i**2 ,lowercase ,lowercase		):
        _UpperCAmelCase    			=   False

  return [i for i in range(2 ,lowercase		) if is_prime[i]]






def      __UpperCAmelCase       (      lowercase = 10**8		):







  """simple docstring"""
  _UpperCAmelCase    			=   calculate_prime_numbers(max_number // 2		)

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   len(lowercase		) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


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