





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    A__  :





          @staticmethod
          def 			__UpperCamelCase(					*A_			,				**A_  ):
           '''simple docstring'''




           pass



def 						A_							(	_lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Tuple			      =  hashlib.mda(image.tobytes()	)
 return m.hexdigest()[:10]



def 						A_							(	_lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   Dict			      =  np.array(_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  npimg.shape
 return {"hash": hashimage(_lowerCAmelCase	), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class    A__  (			unittest.TestCase     ):
      _UpperCAmelCase      :Any		       =  dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
      _UpperCAmelCase      :str		       =  dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  MaskGenerationPipeline(model=A_			,				image_processor=A_  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       pass





      @require_tf
      @unittest.skip("Image segmentation not implemented in TF"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @slow
      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  pipeline("mask-generation"			,				model="facebook/sam-vit-huge"  )

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

       # Shortening by hashing
       UpperCamelCase      :   str			      =  []
       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": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
               {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
               {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
               {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
               {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
               {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
               {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
               {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
               {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
               {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
               {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
               {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
               {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
               {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
               {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
               {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
               {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
               {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
               {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
               {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
               {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
               {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
               {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
               {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
           ]			,				)
       # fmt: on





      @require_torch
      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  "facebook/sam-vit-huge"
       UpperCamelCase      :   str			      =  pipeline("mask-generation"			,				model=A_  )

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

       # Shortening by hashing
       UpperCamelCase      :   Tuple			      =  []
       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": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
           ]			,				)







from typing import Any



def 						A_							(	_lowerCAmelCase	)		->					list[Any]:
 if not input_list:
  return []
 UpperCamelCase      :   List[str]			      =  [input_list.count(_lowerCAmelCase	) for value in input_list]
 UpperCamelCase      :   Dict			      =  max(_lowerCAmelCase	)  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase	) if value == y}	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
