



'''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 _UpperCamelCase		:





						'''simple docstring'''





						@staticmethod
						def 				__lowerCamelCase     (							*_lowerCAmelCase  :  List[Any]  ,       **_lowerCAmelCase  :  str):







													'''simple docstring'''

													pass



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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





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





				'''simple docstring'''





				lowerCAmelCase__					    =    dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
				lowerCAmelCase__					    =    dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Any):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Dict):







											'''simple docstring'''

											pass

				@require_tf
				@unittest.skip('Image segmentation not implemented in TF')
				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											pass

				@slow
				@require_torch
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

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

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

											# Shortening by hashing
											__lowercase       	=[]
											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 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

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

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

											# Shortening by hashing
											__lowercase       	=[]
											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'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('only integers accepted as input'					)
							else:
														__lowercase       	=str(abs(_lowerCAmelCase					)					)
														__lowercase       	=[list(_lowerCAmelCase					) for char in range(len(_lowerCAmelCase					)					)]
														for index in range(len(_lowerCAmelCase					)					):
																					num_transpositions[index].pop(_lowerCAmelCase					)
														return max(
														    int(''.join(list(_lowerCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
		__import__("""doctest""").testmod()



