



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        lowerCamelCase_   (  *snake_case	,       **snake_case						)			->							str:
														pass




def 	UpperCAmelCase     (		A	:						List[Any]       ):

		'''simple docstring'''


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




def 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


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






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







							_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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		MaskGenerationPipeline(model=__UpperCamelCase	,       image_processor=__UpperCamelCase						)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							Dict:
									pass






							@require_tf
							@unittest.skip('Image segmentation not implemented in TF'						)
							def        lowerCamelCase_   (  self						)			->							List[str]:
									pass






							@slow
							@require_torch
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		pipeline('mask-generation'	,       model='facebook/sam-vit-huge'						)

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

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

									# fmt: off
									self.assertEqual(
									    nested_simplify(__UpperCamelCase	,       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        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		'facebook/sam-vit-huge'
									_UpperCAmelCase							 =		pipeline('mask-generation'	,       model=__UpperCamelCase						)

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

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

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







"""simple docstring"""





import os




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		os.path.join(os.path.dirname(A       )     ,				'num.txt'       )
		with open(A       ) as file_hand:
				return str(sum(int(A       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
					print(solution())


