



"""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     A__     :




		'''simple docstring'''





		@staticmethod
		def 							_SCREAMING_SNAKE_CASE     (  *_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    **_SCREAMING_SNAKE_CASE:	Optional[int])       -> List[Any]:

					"""simple docstring"""

					pass


def     _lowercase       (						__snake_case			)		->     str:
			__lowerCAmelCase   :						Union[str, Any]				= hashlib.mda(image.tobytes()			)
			return m.hexdigest()[:10]


def     _lowercase       (						__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Tuple				= np.array(__snake_case			)
			__lowerCAmelCase   :						Any				= npimg.shape
			return {"hash": hashimage(__snake_case			), "shape": shape}





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




	'''simple docstring'''





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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	int)       -> Optional[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> int:

				"""simple docstring"""

				pass

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

				"""simple docstring"""

				pass

	@slow
	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= pipeline("mask-generation" ,    model="facebook/sam-vit-huge")

				__lowerCAmelCase   :						int				= image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" ,    points_per_batch=256)

				# Shortening by hashing
				__lowerCAmelCase   :						Optional[Any]				= []
				for i, o in enumerate(outputs["masks"]):
							new_outupt += [{"mask": mask_to_test_readable(_SCREAMING_SNAKE_CASE), "scores": outputs["scores"][i]}]

				# fmt: off
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE ,    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 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= "facebook/sam-vit-huge"
				__lowerCAmelCase   :						str				= pipeline("mask-generation" ,    model=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						int				= image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg" ,    pred_iou_thresh=1 ,    points_per_batch=256)

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

				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE ,    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"""






def     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))