






from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
					from .data.data_collator import DataCollator
					from .modeling_utils import PreTrainedModel
					from .tokenization_utils_base import PreTrainedTokenizerBase
					from .trainer_callback import TrainerCallback
					from .trainer_utils import EvalPrediction, PredictionOutput
					from .training_args import TrainingArguments


snake_case_   :	Optional[int]							      =logging.get_logger(__name__)





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = (None, None)							, lowercase__ = None							, )				->				List[str]:
				super().__init__(
				    model=lowercase__							, args=lowercase__							, data_collator=lowercase__							, train_dataset=lowercase__							, eval_dataset=lowercase__							, tokenizer=lowercase__							, model_init=lowercase__							, compute_metrics=lowercase__							, callbacks=lowercase__							, optimizers=lowercase__							, preprocess_logits_for_metrics=lowercase__							, )

				# Override self.model.generation_config if a GenerationConfig is specified in args.
				# Priority: args.generation_config > model.generation_config > default GenerationConfig.
				if self.args.generation_config is not None:
						__A    =							self.load_generation_config(self.args.generation_config						)
						__A    =							gen_config






		@staticmethod
		def 					_lowerCamelCase					(      lowercase__						)				->				GenerationConfig:

				# GenerationConfig provided, nothing to do
				if isinstance(lowercase__							, lowercase__						):
						return deepcopy(lowercase__						)

				# str or Path
				__A    =							Path(lowercase__						) if isinstance(lowercase__							, lowercase__						) else gen_config_arg
				__A    =							None

				# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
				# This step is required in order to determine config_file_name
				if pretrained_model_name.is_file():
						__A    =							pretrained_model_name.name
						__A    =							pretrained_model_name.parent
				# dir path
				elif pretrained_model_name.is_dir():
						pass
				# model id or URL
				else:
						__A    =							gen_config_arg

				__A    =							GenerationConfig.from_pretrained(lowercase__							, lowercase__						)
				return gen_config






		def 					_lowerCamelCase					(      self							, lowercase__ = None							, lowercase__ = None							, lowercase__ = "eval"							, **lowercase__							, )				->				Dict[str, float]:
				__A    =							gen_kwargs.copy()
				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.args.generation_max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.args.generation_num_beams
				)
				__A    =							gen_kwargs

				return super().evaluate(lowercase__							, ignore_keys=lowercase__							, metric_key_prefix=lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None							, lowercase__ = "test"							, **lowercase__							, )				->				"PredictionOutput":
				__A    =							gen_kwargs.copy()
				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.args.generation_max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.args.generation_num_beams
				)
				__A    =							gen_kwargs

				return super().predict(lowercase__							, ignore_keys=lowercase__							, metric_key_prefix=lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__ = None							, **lowercase__							, )				->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

				if not self.args.predict_with_generate or prediction_loss_only:
						return super().prediction_step(
						    lowercase__							, lowercase__							, prediction_loss_only=lowercase__							, ignore_keys=lowercase__						)

				__A    =							"labels" in inputs
				__A    =							self._prepare_inputs(lowercase__						)

				# XXX: adapt synced_gpus for fairscale as well
				# Priority (handled in generate):
				# gen_kwargs > model.generation_config > default GenerationConfig()

				if len(lowercase__						) == 0 and hasattr(self							, "_gen_kwargs"						):
						__A    =							self._gen_kwargs.copy()

				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.model.config.max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.model.config.num_beams
				)
				__A    =							True if is_deepspeed_zeroa_enabled() else False
				__A    =							(
				    gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus"						) is not None else default_synced_gpus
				)

				# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
				# (otherwise, it would continue generating from the padded `decoder_input_ids`)
				if (
				    "labels" in inputs
				    and "decoder_input_ids" in inputs
				    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
				):
						__A    =							{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
				__A    =							self.model.generate(**lowercase__							, **lowercase__						)

				# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
				# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
				# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
				if self.model.generation_config._from_model_config:
						__A    =							False

				# Retrieves GenerationConfig from model.generation_config
				__A    =							self.model.generation_config
				# in case the batch is shorter than max length, the output should be padded
				if generated_tokens.shape[-1] < gen_config.max_length:
						__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_length						)
				elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
						__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_new_tokens + 1						)

				with torch.no_grad():
						if has_labels:
								with self.compute_loss_context_manager():
										__A    =							model(**lowercase__						)
								if self.label_smoother is not None:
										__A    =							self.label_smoother(lowercase__							, inputs["labels"]						).mean().detach()
								else:
										__A    =							(outputs["loss"] if isinstance(lowercase__							, lowercase__						) else outputs[0]).mean().detach()
						else:
								__A    =							None

				if self.args.prediction_loss_only:
						return loss, None, None

				if has_labels:
						__A    =							inputs["labels"]
						if labels.shape[-1] < gen_config.max_length:
								__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_length						)
						elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
								__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_new_tokens + 1						)
				else:
						__A    =							None

				return loss, generated_tokens, labels






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				Optional[int]:
				if self.tokenizer is not None and hasattr(self.tokenizer							, "pad_token_id"						):
						# If PAD token is not defined at least EOS token has to be defined
						__A    =							(
						    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
						)
				else:
						if self.model.config.pad_token_id is not None:
								__A    =							self.model.config.pad_token_id
						else:
								raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors"						)

				__A    =							pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)							, dtype=tensor.dtype							, device=tensor.device						)
				__A    =							tensor
				return padded_tensor








import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


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





