



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__    :


          @staticmethod
          def        a			(*a__	:				Tuple    ,      **a__	:				Any				):





             """simple docstring"""



             pass






def lowerCamelCase__   (							snake_case_     :  int	)				->    Union[str, Any]:
   __snake_case								=       hashlib.mda(image.tobytes()	)
   return m.hexdigest()[:10]






def lowerCamelCase__   (							snake_case_     :  List[Any]	)				->    Optional[Any]:
   __snake_case								=       np.array(A__	)
   __snake_case								=       npimg.shape
   return {"hash": hashimage(A__	), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):
      A_						:    Optional[Any]							=   dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])  )
      A_						:    List[Any]							=   dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])  )


      def        a			(self	:				int    ,      a__	:				Any    ,      a__	:				Optional[Any]    ,      a__	:				Optional[Any]				):





         """simple docstring"""



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


      def        a			(self	:				Tuple    ,      a__	:				List[Any]    ,      a__	:				List[str]				):





         """simple docstring"""



         pass


      @require_tf
      @unittest.skip('''Image segmentation not implemented in TF'''				)
      def        a			(self	:				str				):





         """simple docstring"""



         pass


      @slow
      @require_torch
      def        a			(self	:				List[Any]				):





         """simple docstring"""



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

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

         # Shortening by hashing
         __snake_case								=       []
         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''': 1.0_4_4_4},
                 {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
                 {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
                 {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
                 {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
                 {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
                 {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
                 {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
                 {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
                 {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
                 {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
                 {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
                 {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
                 {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
                 {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
                 {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
                 {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
                 {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
                 {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
                 {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
                 {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
                 {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
                 {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
                 {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
                 {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
                 {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
                 {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
                 {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
                 {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
                 {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
             ]    ,      )
         # fmt: on


      @require_torch
      @slow
      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       '''facebook/sam-vit-huge'''
         __snake_case								=       pipeline('''mask-generation'''    ,      model=lowercase__				)

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

         # Shortening by hashing
         __snake_case								=       []
         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''': 1.0_4_4_4},
                 {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
                 {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
                 {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
                 {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
             ]    ,      )








class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				str    ,      a__	:				list				):





         """simple docstring"""



         __snake_case								=       set_counts
         __snake_case								=       max(a__				)
         __snake_case								=       len(a__				)
         __snake_case								=       [1] * num_sets
         __snake_case								=       list(range(a__				)				)


      def        a			(self	:				str    ,      a__	:				int    ,      a__	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_parent(a__				)
         __snake_case								=       self.get_parent(a__				)

         if src_parent == dst_parent:
            return False

         if self.ranks[dst_parent] >= self.ranks[src_parent]:
            self.set_counts[dst_parent] += self.set_counts[src_parent]
            __snake_case								=       0
            __snake_case								=       dst_parent
            if self.ranks[dst_parent] == self.ranks[src_parent]:
               self.ranks[dst_parent] += 1
            __snake_case								=       self.set_counts[dst_parent]
         else:
            self.set_counts[src_parent] += self.set_counts[dst_parent]
            __snake_case								=       0
            __snake_case								=       src_parent
            __snake_case								=       self.set_counts[src_parent]

         __snake_case								=       max(self.max_set    ,      a__				)
         return True


      def        a			(self	:				Union[str, Any]    ,      a__	:				int				):





         """simple docstring"""



         if self.parents[disj_set] == disj_set:
            return disj_set
         __snake_case								=       self.get_parent(self.parents[disj_set]				)
         return self.parents[disj_set]

