





"""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      __SCREAMING_SNAKE_CASE    :







    '''simple docstring'''


    @staticmethod
    def    __SCREAMING_SNAKE_CASE				(		*__a   :       List[Any]					,   **__a   :       Optional[int]	)		->				Dict:
          pass




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	str						=       np.array(lowercase_			)
      _UpperCamelCase      :	Any						=       npimg.shape
      return {"hash": hashimage(lowercase_			), "shape": shape}



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







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int					,   __a   :       Optional[int]					,   __a   :       Dict	)		->				Union[str, Any]:
        _UpperCamelCase      :	Optional[Any]						=       MaskGenerationPipeline(model=__a					,   image_processor=__a	)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       Optional[int]					,   __a   :       Dict	)		->				Union[str, Any]:
        pass

  @require_tf
  @unittest.skip("Image segmentation not implemented in TF"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
        pass

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

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

        # Shortening by hashing
        _UpperCamelCase      :	Union[str, Any]						=       []
        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    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[Any]:
        _UpperCamelCase      :	str						=       "facebook/sam-vit-huge"
        _UpperCamelCase      :	str						=       pipeline("mask-generation"					,   model=__a	)

        _UpperCamelCase      :	List[Any]						=       image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg"					,   pred_iou_thresh=1					,   points_per_batch=256	)

        # Shortening by hashing
        _UpperCamelCase      :	Optional[Any]						=       []
        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},
            ]					,   )







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       0.0
        _UpperCamelCase      :	Union[str, Any]						=       0.0
        for i in range(len(__a	)	):
              da += math.pow((sample[i] - weights[0][i])					,   2	)
              da += math.pow((sample[i] - weights[1][i])					,   2	)
              return 0 if da > da else 1
        return 0


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

      # weight initialization ( n, C )
      _UpperCamelCase      :	List[str]						=       [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

      # results
      print(F'''Clusters that the test sample belongs to : {winner}'''			)
      print(F'''Weights that have been trained : {weights}'''			)


# running the main() function
if __name__ == "__main__":
  main()
