





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       UpperCamelCase__  (    *__magic_name__,		**__magic_name__							) ->      Dict:






         """simple docstring"""
         pass




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Image      )      ->	str:
    UpperCamelCase__							:				int          =      hashlib.mda(image.tobytes()      )
    return m.hexdigest()[:10]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Image      )      ->	Dict:
    UpperCamelCase__							:				int          =      np.array(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      npimg.shape
    return {"hash": hashimage(__UpperCAmelCase      ), "shape": shape}






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

   '''simple docstring'''


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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      MaskGenerationPipeline(model=__magic_name__,		image_processor=__magic_name__							)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       pass




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






       """simple docstring"""
       pass




   @slow
   @require_torch
   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[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__							:				Optional[Any]          =      []
       for i, o in enumerate(outputs['''masks''']							):
           new_outupt += [{"mask": mask_to_test_readable(__magic_name__							), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(__magic_name__,		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							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      '''facebook/sam-vit-huge'''
       UpperCamelCase__							:				str          =      pipeline('''mask-generation''',		model=__magic_name__							)

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

       # Shortening by hashing
       UpperCamelCase__							:				List[Any]          =      []
       for i, o in enumerate(outputs['''masks''']							):
           new_outupt += [{"mask": mask_to_test_readable(__magic_name__							), "scores": outputs["scores"][i]}]

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








import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

