




"""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 				snake_case_			:







     """simple docstring"""




     @staticmethod
     def   UpperCAmelCase__			(		*lowerCamelCase_       ,					**lowerCamelCase_)				->   Optional[Any]:
         pass





def      __snake_case (				_lowercase    ):


    """simple docstring"""







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





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	np.array(_lowercase    )
    UpperCamelCase              =	npimg.shape
    return {"hash": hashimage(_lowercase    ), "shape": shape}


@is_pipeline_test
@require_vision
@require_torch
class 				snake_case_			(					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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Any:
       UpperCamelCase              =	MaskGenerationPipeline(model=lowerCamelCase_       ,					image_processor=lowerCamelCase_)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Union[str, Any]:
       pass







   @require_tf
   @unittest.skip('''Image segmentation not implemented in TF''')
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       pass







   @slow
   @require_torch
   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	pipeline('''mask-generation'''       ,					model='''facebook/sam-vit-huge''')

       UpperCamelCase              =	image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,					points_per_batch=2_5_6)

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

       # fmt: off
       self.assertEqual(
           nested_simplify(lowerCamelCase_       ,					decimals=4)       ,					[
               {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.021},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
               {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9967},
               {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.993},
               {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9909},
               {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9879},
               {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9834},
               {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9716},
               {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9612},
               {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9599},
               {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9552},
               {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9532},
               {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9516},
               {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9499},
               {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9483},
               {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9464},
               {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
               {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
               {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9408},
               {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9335},
               {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9326},
               {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9262},
               {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8999},
               {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8986},
               {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8984},
               {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8873},
               {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8871}
           ]       ,					)
       # fmt: on







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

       UpperCamelCase              =	image_segmenter(
           '''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,					pred_iou_thresh=1       ,					points_per_batch=2_5_6)

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

       self.assertEqual(
           nested_simplify(lowerCamelCase_       ,					decimals=4)       ,					[
               {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0210},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
           ]       ,					)





'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






     def __init__(					self       )				->     List[Any]:
          _snake_case				= None






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('''After swapping''')
   ll.print_list()


