
"""simple docstring"""




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   _lowercase						:


         '''simple docstring'''





         @staticmethod
         def  lowerCAmelCase__       (      *__UpperCamelCase ,			**__UpperCamelCase  )->					str:
              pass



def 							a__ (				lowerCAmelCase	:       Image		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						hashlib.mda(image.tobytes()		)
     return m.hexdigest()[:10]



def 							a__ (				lowerCAmelCase	:       Image		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						np.array(lowerCAmelCase		)
     UpperCAmelCase__							: List[str] 							=						npimg.shape
     return {"hash": hashimage(lowerCAmelCase		), "shape": shape}







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


     '''simple docstring'''





     _A								=							dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
     _A								=							dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Union[str, Any] 							=						MaskGenerationPipeline(model=__UpperCamelCase ,			image_processor=__UpperCamelCase  )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					int:
          pass



     @require_tf
     @unittest.skip("Image segmentation not implemented in TF"  )
     def  lowerCAmelCase__       (      self  )->					List[str]:
          pass



     @slow
     @require_torch
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Union[str, Any] 							=						pipeline("mask-generation" ,			model="facebook/sam-vit-huge"  )

          UpperCAmelCase__							: Any 							=						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" ,			points_per_batch=2_56  )

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

          # fmt: off
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0444},
                  {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.021},
                  {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0167},
                  {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0132},
                  {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0053},
                  {"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.9967},
                  {"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.993},
                  {"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.9909},
                  {"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.9879},
                  {"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.9834},
                  {"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.9716},
                  {"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.9612},
                  {"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.9599},
                  {"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.9552},
                  {"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.9532},
                  {"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.9516},
                  {"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.9499},
                  {"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.9483},
                  {"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.9464},
                  {"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.943},
                  {"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.943},
                  {"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.9408},
                  {"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.9335},
                  {"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.9326},
                  {"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.9262},
                  {"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.8999},
                  {"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.8986},
                  {"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.8984},
                  {"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.8873},
                  {"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.8871}
              ] ,			)
          # fmt: on



     @require_torch
     @slow
     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: int 							=						"facebook/sam-vit-huge"
          UpperCAmelCase__							: Any 							=						pipeline("mask-generation" ,			model=__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						image_segmenter(
              "http://images.cocodataset.org/val2017/000000039769.jpg" ,			pred_iou_thresh=1 ,			points_per_batch=2_56  )

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

          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0444},
                  {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0210},
                  {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0167},
                  {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0132},
                  {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0053},
              ] ,			)





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(a - b		) for a, b in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)



def 							a__ (				lowerCAmelCase	:       list[float]		):






     '''simple docstring'''
     if point:
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               for item in point:
                    if not isinstance(lowerCAmelCase       ,    (int, float)		):
                         UpperCAmelCase__							: Tuple 							=						(
                             "Expected a list of numbers as input, found "
                             F"{type(lowerCAmelCase		).__name__}"
                         )
                         raise TypeError(lowerCAmelCase		)
          else:
               UpperCAmelCase__							: Dict 							=						F"Expected a list of numbers as input, found {type(lowerCAmelCase		).__name__}"
               raise TypeError(lowerCAmelCase		)
     else:
          raise ValueError("Missing an input"		)



def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(x - y		) for x, y in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



