


'''simple docstring'''




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowercase	:   Dict   		=					logging.get_logger(__name__)

__lowercase	:   Union[str, Any]   		=					{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}





class _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  '''gpt2'''
							__a				       =  ['''past_key_values''']
							__a				       =  {
							    '''hidden_size''': '''n_embd''',
							    '''max_position_embeddings''': '''n_positions''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}







							def __init__( self      ,					__A=50257      ,					__A=1024      ,					__A=768      ,					__A=12      ,					__A=12      ,					__A=None      ,					__A="gelu_new"      ,					__A=0.1      ,					__A=0.1      ,					__A=0.1      ,					__A=1E-5      ,					__A=0.02      ,					__A="cls_index"      ,					__A=True      ,					__A=None      ,					__A=True      ,					__A=0.1      ,					__A=True      ,					__A=True      ,					__A=50256      ,					__A=50256      ,					__A=False      ,					__A=False      ,					**__A      ,					)  ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       vocab_size
											__lowerCamelCase		:   Tuple           =       n_positions
											__lowerCamelCase		:   str           =       n_embd
											__lowerCamelCase		:   int           =       n_layer
											__lowerCamelCase		:   Union[str, Any]           =       n_head
											__lowerCamelCase		:   Tuple           =       n_inner
											__lowerCamelCase		:   List[str]           =       activation_function
											__lowerCamelCase		:   Dict           =       resid_pdrop
											__lowerCamelCase		:   str           =       embd_pdrop
											__lowerCamelCase		:   Optional[int]           =       attn_pdrop
											__lowerCamelCase		:   int           =       layer_norm_epsilon
											__lowerCamelCase		:   int           =       initializer_range
											__lowerCamelCase		:   List[Any]           =       summary_type
											__lowerCamelCase		:   List[str]           =       summary_use_proj
											__lowerCamelCase		:   Union[str, Any]           =       summary_activation
											__lowerCamelCase		:   str           =       summary_first_dropout
											__lowerCamelCase		:   Union[str, Any]           =       summary_proj_to_labels
											__lowerCamelCase		:   List[str]           =       scale_attn_weights
											__lowerCamelCase		:   Any           =       use_cache
											__lowerCamelCase		:   int           =       scale_attn_by_inverse_layer_idx
											__lowerCamelCase		:   Optional[int]           =       reorder_and_upcast_attn

											__lowerCamelCase		:   Union[str, Any]           =       bos_token_id
											__lowerCamelCase		:   int           =       eos_token_id

											super().__init__(bos_token_id=__A      ,					eos_token_id=__A      ,					**__A						)





class _snake_case (			a__ ):
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A = "default"      ,					__A = None      ,					__A = False      ,					)  ->Any:



											"""simple docstring"""







											super().__init__(__A      ,					task=__A      ,					patching_specs=__A      ,					use_past=__A						)
											if not getattr(self._config      ,					"pad_token_id"      ,					__A						):
															# TODO: how to do that better?
															__lowerCamelCase		:   str           =       0







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Mapping[str, Mapping[int, str]]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
											if self.use_past:
															self.fill_with_past_key_values_(__A      ,					direction="inputs"						)
															__lowerCamelCase		:   Optional[Any]           =       {0: "batch", 1: "past_sequence + sequence"}
											else:
															__lowerCamelCase		:   int           =       {0: "batch", 1: "sequence"}

											return common_inputs







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self._config.n_layer







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self._config.n_head







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A = -1      ,					__A = -1      ,					__A = False      ,					__A = None      ,					)  ->Mapping[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       super(__A      ,					self						).generate_dummy_inputs(
											    __A      ,					batch_size=__A      ,					seq_length=__A      ,					is_pair=__A      ,					framework=__A						)

											# We need to order the input in the way they appears in the forward()
											__lowerCamelCase		:   List[Any]           =       OrderedDict({"input_ids": common_inputs["input_ids"]}						)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
															else:
																			import torch

																			__lowerCamelCase    ,       __lowerCamelCase		:   str           =       common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			__lowerCamelCase		:   Dict           =       seqlen + 2
																			__lowerCamelCase		:   Optional[int]           =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			__lowerCamelCase		:   Union[str, Any]           =       [
																			    (torch.zeros(__A						), torch.zeros(__A						)) for _ in range(self.num_layers						)
																			]

											__lowerCamelCase		:   List[str]           =       common_inputs["attention_mask"]
											if self.use_past:
															__lowerCamelCase		:   Union[str, Any]           =       ordered_inputs["attention_mask"].dtype
															__lowerCamelCase		:   List[str]           =       torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__A      ,					__A      ,					dtype=__A						)]      ,					dim=1						)

											return ordered_inputs







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return 13









'''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())





