



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

				with open(lowercase     ) as in_file:
								__lowerCamelCase		:   List[str]           =       [[int(lowercase     ) for i in line.split()] for line in in_file]

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


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





