





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 	UpperCamelCase__				:



           '''simple docstring'''

           @staticmethod
           def        SCREAMING_SNAKE_CASE__						(							*lowerCamelCase__		:					Tuple		,**lowerCamelCase__		:					List[str] )  ->	str:




                 '''simple docstring'''


                 pass



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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



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



     '''simple docstring'''

     __snake_case					:			Dict				=    dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
     __snake_case					:			Dict				=    dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           pass






     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           pass






     @slow
     @require_torch
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		pipeline("""mask-generation"""		,model="""facebook/sam-vit-huge""" )

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

           # Shortening by hashing
           SCREAMING_SNAKE_CASE	=		[]
           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""": (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        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""facebook/sam-vit-huge"""
           SCREAMING_SNAKE_CASE	=		pipeline("""mask-generation"""		,model=lowerCamelCase__ )

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

           # Shortening by hashing
           SCREAMING_SNAKE_CASE	=		[]
           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""": (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},
               ]		,)









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


