





"""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 			__lowercase     :







							'''simple docstring'''




							@staticmethod
							def 	_lowerCamelCase						(  *_UpperCAmelCase		,     **_UpperCAmelCase       ):
											pass

def       __A (     a_  :Image)						->						str:
				__a	:  List[str]         =					hashlib.mda(image.tobytes())
				return m.hexdigest()[:10]

def       __A (     a_  :Image)						->						Dict:
				__a	:  Any         =					np.array(a_)
				__a	:  Tuple         =					npimg.shape
				return {"hash": hashimage(a_), "shape": shape}






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







					'''simple docstring'''




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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					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		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									pass







					@require_tf
					@unittest.skip('''Image segmentation not implemented in TF'''       )
					def 	_lowerCamelCase						(  self       ):
									pass







					@slow
					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					pipeline('''mask-generation'''		,     model='''facebook/sam-vit-huge'''       )

									__a	:  Optional[Any]         =					image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									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''': 1.0_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       ):
									__a	:  Dict         =					'''facebook/sam-vit-huge'''
									__a	:  Tuple         =					pipeline('''mask-generation'''		,     model=_UpperCAmelCase       )

									__a	:  List[Any]         =					image_segmenter(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     pred_iou_thresh=1		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									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''': 1.0_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},
									    ]		,     )






"""simple docstring"""






def       __A (     a_  :int)						->						Union[str, Any]:
				__a	:  int         =					[]
				__a	:  Dict         =					[]
				__a	:  str         =					{
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				__a	:  Tuple         =					len(a_) if (len(a_) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8)				,    '''Stack'''.center(a_)				,    '''Postfix'''.center(a_)				,    sep=''' | '''				,    )
				print('''-''' * (print_width * 3 + 7))

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(a_)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(a_)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(a_) == 0:
																stack.append(a_)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop())  # pop stack & add to Postfix
																stack.append(a_)  # push x to stack

								print(
								    x.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				while len(a_) > 0:  # while stack is not empty
								post_fix.append(stack.pop())  # pop stack & add to Postfix
								print(
								    ''' '''.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				return "".join(a_)  # return Postfix as str

def       __A (     a_  :int)						->						List[Any]:
				__a	:  Dict         =					list(infix[::-1])  # reverse the infix equation

				for i in range(len(a_)):
								if infix[i] == "(":
												__a	:  Union[str, Any]         =					''')'''  # change "(" to ")"
								elif infix[i] == ")":
												__a	:  List[str]         =					'''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(a_)))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		A	     =					input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
		A	     =					''''''.join(Infix.split())  # Remove spaces from the input
		print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')