


import os




def     __SCREAMING_SNAKE_CASE							(       )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	os.path.dirname(os.path.realpath(lowercase_      )      )
						__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(lowercase_      ,       '''triangle.txt'''      )

						with open(lowercase_      ) as f:
												__UpperCAmelCase			:							List[Any]	    =	f.readlines()

						__UpperCAmelCase			:							Optional[int]	    =	[]
						for line in triangle:
												__UpperCAmelCase			:							Optional[Any]	    =	[]
												for number in line.strip().split(''' '''      ):
																		numbers_from_line.append(int(lowercase_      )      )
												a.append(lowercase_      )

						for i in range(1      ,       len(lowercase_      )      ):
												for j in range(len(a[i]      )      ):
																		__UpperCAmelCase			:							Any	    =	a[i - 1][j] if j != len(a[i - 1]      ) else 0
																		__UpperCAmelCase			:							Dict	    =	a[i - 1][j - 1] if j > 0 else 0
																		a[i][j] += max(lowercase_      ,       lowercase_      )
						return max(a[-1]      )


if __name__ == "__main__":
				print(solution())





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

