





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 		__UpperCAmelCase       (    *lowerCamelCase_			:		Tuple ,				**lowerCamelCase_			:		int					):





												'''simple docstring'''







												pass

def       A__(		__lowerCAmelCase ):
				_snake_case :			int  						=   hashlib.mda(image.tobytes() )
				return m.hexdigest()[:10]

def       A__(		__lowerCAmelCase ):
				_snake_case :			Tuple  						=   np.array(__lowerCAmelCase )
				_snake_case :			str  						=   npimg.shape
				return {"hash": hashimage(__lowerCAmelCase ), "shape": shape}


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



						"""simple docstring"""

						_UpperCamelCase  :	Any								=	dict(
						    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
						_UpperCamelCase  :	Union[str, Any]								=	dict(
						    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   MaskGenerationPipeline(model=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)
										return image_segmenter, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										pass







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





										'''simple docstring'''







										pass







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





										'''simple docstring'''







										_snake_case :			int  						=   pipeline('mask-generation' ,				model='facebook/sam-vit-huge'					)

										_snake_case :			Optional[Any]  						=   image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,				points_per_batch=2_56					)

										# Shortening by hashing
										_snake_case :			int  						=   []
										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_80, 6_40)}, 'scores': 1.0444},
										        {'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.021},
										        {'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
										        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
										        {'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
										        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_80, 6_40)}, 'scores': 0.9967},
										        {'mask': {'hash': '453c7844bd', 'shape': (4_80, 6_40)}, 'scores': 0.993},
										        {'mask': {'hash': '3d44f2926d', 'shape': (4_80, 6_40)}, 'scores': 0.9909},
										        {'mask': {'hash': '64033ddc3f', 'shape': (4_80, 6_40)}, 'scores': 0.9879},
										        {'mask': {'hash': '801064ff79', 'shape': (4_80, 6_40)}, 'scores': 0.9834},
										        {'mask': {'hash': '6172f276ef', 'shape': (4_80, 6_40)}, 'scores': 0.9716},
										        {'mask': {'hash': 'b49e60e084', 'shape': (4_80, 6_40)}, 'scores': 0.9612},
										        {'mask': {'hash': 'a811e775fd', 'shape': (4_80, 6_40)}, 'scores': 0.9599},
										        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_80, 6_40)}, 'scores': 0.9552},
										        {'mask': {'hash': '9d8257e080', 'shape': (4_80, 6_40)}, 'scores': 0.9532},
										        {'mask': {'hash': '32de6454a8', 'shape': (4_80, 6_40)}, 'scores': 0.9516},
										        {'mask': {'hash': 'af3d4af2c8', 'shape': (4_80, 6_40)}, 'scores': 0.9499},
										        {'mask': {'hash': '3c6db475fb', 'shape': (4_80, 6_40)}, 'scores': 0.9483},
										        {'mask': {'hash': 'c290813fb9', 'shape': (4_80, 6_40)}, 'scores': 0.9464},
										        {'mask': {'hash': 'b6f0b8f606', 'shape': (4_80, 6_40)}, 'scores': 0.943},
										        {'mask': {'hash': '92ce16bfdf', 'shape': (4_80, 6_40)}, 'scores': 0.943},
										        {'mask': {'hash': 'c749b25868', 'shape': (4_80, 6_40)}, 'scores': 0.9408},
										        {'mask': {'hash': 'efb6cab859', 'shape': (4_80, 6_40)}, 'scores': 0.9335},
										        {'mask': {'hash': '1ff2eafb30', 'shape': (4_80, 6_40)}, 'scores': 0.9326},
										        {'mask': {'hash': '788b798e24', 'shape': (4_80, 6_40)}, 'scores': 0.9262},
										        {'mask': {'hash': 'abea804f0e', 'shape': (4_80, 6_40)}, 'scores': 0.8999},
										        {'mask': {'hash': '7b9e8ddb73', 'shape': (4_80, 6_40)}, 'scores': 0.8986},
										        {'mask': {'hash': 'cd24047c8a', 'shape': (4_80, 6_40)}, 'scores': 0.8984},
										        {'mask': {'hash': '6943e6bcbd', 'shape': (4_80, 6_40)}, 'scores': 0.8873},
										        {'mask': {'hash': 'b5f47c9191', 'shape': (4_80, 6_40)}, 'scores': 0.8871}
										    ] ,				)
										# fmt: on







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





										'''simple docstring'''







										_snake_case :			Tuple  						=   'facebook/sam-vit-huge'
										_snake_case :			int  						=   pipeline('mask-generation' ,				model=lowerCamelCase_					)

										_snake_case :			Tuple  						=   image_segmenter(
										    'http://images.cocodataset.org/val2017/000000039769.jpg' ,				pred_iou_thresh=1 ,				points_per_batch=2_56					)

										# Shortening by hashing
										_snake_case :			str  						=   []
										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_80, 6_40)}, 'scores': 1.0444},
										        {'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.0210},
										        {'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
										        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
										        {'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
										    ] ,				)













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






