



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			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any]	)->  List[Any]:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()[:1_0]



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



@is_pipeline_test
@require_vision
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
     UpperCamelCase__		    =				dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					MaskGenerationPipeline(model=_a ,						image_processor=_a)
         return image_segmenter, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         pass




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




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					pipeline('''mask-generation''' ,						model='''facebook/sam-vit-huge''')

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

         # Shortening by hashing
         A__		 =					[]
         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''': (4_8_0, 6_4_0)}, '''scores''': 1.0_4_4_4},
                 {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_2_1},
                 {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_1_6_7},
                 {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_1_3_2},
                 {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_0_5_3},
                 {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_9_6_7},
                 {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_9_3},
                 {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_9_0_9},
                 {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_8_7_9},
                 {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_8_3_4},
                 {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_7_1_6},
                 {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_6_1_2},
                 {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_5_9_9},
                 {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_5_5_2},
                 {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_5_3_2},
                 {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_5_1_6},
                 {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_9_9},
                 {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_8_3},
                 {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_6_4},
                 {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_3},
                 {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_3},
                 {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_4_0_8},
                 {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_3_3_5},
                 {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_3_2_6},
                 {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9_2_6_2},
                 {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8_9_9_9},
                 {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8_9_8_6},
                 {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8_9_8_4},
                 {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8_8_7_3},
                 {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8_8_7_1}
             ] ,						)
         # fmt: on




     @require_torch
     @slow
     def 		snake_case_		(				self):
         A__		 =					"""facebook/sam-vit-huge"""
         A__		 =					pipeline('''mask-generation''' ,						model=_a)

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

         # Shortening by hashing
         A__		 =					[]
         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''': (4_8_0, 6_4_0)}, '''scores''': 1.0_4_4_4},
                 {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_2_1_0},
                 {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_1_6_7},
                 {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_1_3_2},
                 {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0_0_5_3},
             ] ,						)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	)->  float:

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

        return "".join(UpperCamelCase_	)

    # matching characters
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					len(UpperCamelCase_	)

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

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

    # common prefix up to 4 characters
    A__		 =					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"))
