

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 						__magic_name__			:

          '''simple docstring'''




          @staticmethod
          def  _UpperCAmelCase (				*lowercase_,    **lowercase_       )							->    List[str]:






            """simple docstring"""






            pass




def 			UpperCAmelCase__				(			_A      : Image							):





  '''simple docstring'''







  a__  =hashlib.mda(image.tobytes()							)
  return m.hexdigest()[:10]




def 			UpperCAmelCase__				(			_A      : Image							):





  '''simple docstring'''







  a__  =np.array(_A							)
  a__  =npimg.shape
  return {"hash": hashimage(_A							), "shape": shape}







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

   '''simple docstring'''




   lowerCamelCase__ :	Any    =   dict(
       (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
   lowerCamelCase__ :	Dict    =   dict(
       (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_       )							->    str:






     """simple docstring"""






     a__  =MaskGenerationPipeline(model=lowercase_,    image_processor=lowercase_       )
     return image_segmenter, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    List[str]:






     """simple docstring"""






     pass



   @require_tf
   @unittest.skip('''Image segmentation not implemented in TF'''       )
   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     pass



   @slow
   @require_torch
   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     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(lowercase_       ), "scores": outputs["scores"][i]}]

     # fmt: off
     self.assertEqual(
         nested_simplify(lowercase_,    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  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  ='''facebook/sam-vit-huge'''
     a__  =pipeline('''mask-generation''',    model=lowercase_       )

     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(lowercase_       ), "scores": outputs["scores"][i]}]

     self.assertEqual(
         nested_simplify(lowercase_,    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},
         ],    )



def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  return [
      a * b * (10_00 - a - b)
      for a in range(1	,      9_99							)
      for b in range(_A	,      9_99							)
      if (a * a + b * b == (10_00 - a - b) ** 2)
  ][0]


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