






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 							UpperCamelCase				(     *UpperCAmelCase_:  Optional[Any]				,     **UpperCAmelCase_:  int					):







											'''simple docstring'''




											pass

def 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  Dict:






						"""simple docstring"""





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




@is_pipeline_test
@require_vision
@require_torch
class 				__UpperCAmelCase		(unittest.TestCase   ):
			__snake_case    :				str									=							dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
			__snake_case    :				Any									=							dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )







			def 							UpperCamelCase				(     self:  Any				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									pass







			@require_tf
			@unittest.skip("""Image segmentation not implemented in TF"""					)
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									pass







			@slow
			@require_torch
			def 							UpperCamelCase				(     self:  int					):







									'''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(UpperCAmelCase_					), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(UpperCAmelCase_				,     decimals=4					)				,     [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.04_44},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_21},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.01_67},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.01_32},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.00_53},
									        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.99_67},
									        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_93},
									        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.99_09},
									        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.98_79},
									        {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.98_34},
									        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.97_16},
									        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.96_12},
									        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.95_99},
									        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.95_52},
									        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.95_32},
									        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.95_16},
									        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.94_99},
									        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.94_83},
									        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.94_64},
									        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_43},
									        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_43},
									        {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.94_08},
									        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.93_35},
									        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.93_26},
									        {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.92_62},
									        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.89_99},
									        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.89_86},
									        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.89_84},
									        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.88_73},
									        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.88_71}
									    ]				,     )
									# fmt: on







			@require_torch
			@slow
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




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

									_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(UpperCAmelCase_					), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(UpperCAmelCase_				,     decimals=4					)				,     [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.04_44},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.02_10},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.01_67},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.01_32},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.00_53},
									    ]				,     )









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

