
"""simple docstring"""





UpperCamelCase_					:						int				= '''Alexander Joslin'''

import operator as op

from .stack import Stack
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

					A_				 =						Stack()
					A_				 =						Stack()

					for i in equation:
										if i.isdigit():
															# RULE 1
															operand_stack.push(int(__a				)				)
										elif i in operators:
															# RULE 2
															operator_stack.push(__a				)
										elif i == ")":
															# RULE 4
															A_				 =						operator_stack.peek()
															operator_stack.pop()
															A_				 =						operand_stack.peek()
															operand_stack.pop()
															A_				 =						operand_stack.peek()
															operand_stack.pop()

															A_				 =						operators[opr](__a				,   __a				)
															operand_stack.push(__a				)

    # RULE 5
					return operand_stack.peek()


if __name__ == "__main__":
		UpperCamelCase_					:						List[Any]				= '''(5 + ((4 * 2) * (2 + 3)))'''
		# answer = 45
		print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






"""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 __lowerCAmelCase				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					int       ,   **_snake_case						:					Optional[Any]	)      -> str:







										"""simple docstring"""
										pass
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						hashlib.mda(image.tobytes()				)
					return m.hexdigest()[:10]
def 							A_     (__a				):

					'''simple docstring'''





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




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




			"""simple docstring"""







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




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Dict       ,   _snake_case						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						MaskGenerationPipeline(model=_snake_case       ,   image_processor=_snake_case	)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						pipeline("mask-generation"       ,   model="facebook/sam-vit-huge"	)

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

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

								# fmt: off
								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
								    ]       ,   )
								# fmt: on






			@require_torch
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						"facebook/sam-vit-huge"
								A_				 =						pipeline("mask-generation"       ,   model=_snake_case	)

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

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

								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								    ]       ,   )




