
"""simple docstring"""







import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'sequence-classification'





					def __init__(				self   :							List[Any]							,						snake_case_   :							str    ):
												if type(snake_case_    ) == dict:
																			__snake_case     			=       Namespace(**snake_case_    )
												__snake_case     			=       glue_output_modes[hparams.task]
												__snake_case     			=       glue_tasks_num_labels[hparams.task]

												super().__init__(snake_case_							,						snake_case_							,						self.mode    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						**snake_case_   :							Optional[Any]    ):
												return self.model(**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																			__snake_case     			=       batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

												__snake_case     			=       self(**snake_case_    )
												__snake_case     			=       outputs[0]

												__snake_case     			=       self.trainer.lr_schedulers[0]["scheduler"]
												__snake_case     			=       {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss, "log": tensorboard_logs}





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.hparams
												__snake_case     			=       processors[args.task]()
												__snake_case     			=       processor.get_labels()

												for mode in ["train", "dev"]:
																			__snake_case     			=       self._feature_file(snake_case_    )
																			if os.path.exists(snake_case_    ) and not args.overwrite_cache:
																										logger.info("Loading features from cached file %s"							,						snake_case_    )
																			else:
																										logger.info("Creating features from dataset file at %s"							,						args.data_dir    )
																										__snake_case     			=       (
																										    processor.get_dev_examples(args.data_dir    )
																										    if mode == "dev"
																										    else processor.get_train_examples(args.data_dir    )
																										)
																										__snake_case     			=       convert_examples_to_features(
																										    snake_case_							,						self.tokenizer							,						max_length=args.max_seq_length							,						label_list=self.labels							,						output_mode=args.glue_output_mode							,						)
																										logger.info("Saving features into cached file %s"							,						snake_case_    )
																										torch.save(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							str							,						snake_case_   :							int							,						snake_case_   :							bool = False    ):
												__snake_case     			=       "dev" if mode == "test" else mode

												__snake_case     			=       self._feature_file(snake_case_    )
												logger.info("Loading features from cached file %s"							,						snake_case_    )
												__snake_case     			=       torch.load(snake_case_    )
												__snake_case     			=       torch.tensor([f.input_ids for f in features]							,						dtype=torch.long    )
												__snake_case     			=       torch.tensor([f.attention_mask for f in features]							,						dtype=torch.long    )
												__snake_case     			=       torch.tensor([f.token_type_ids for f in features]							,						dtype=torch.long    )
												if self.hparams.glue_output_mode == "classification":
																			__snake_case     			=       torch.tensor([f.label for f in features]							,						dtype=torch.long    )
												elif self.hparams.glue_output_mode == "regression":
																			__snake_case     			=       torch.tensor([f.label for f in features]							,						dtype=torch.float    )

												return DataLoader(
												    TensorDataset(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )							,						batch_size=snake_case_							,						shuffle=snake_case_							,						)





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							str    ):
												__snake_case     			=       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																			__snake_case     			=       batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

												__snake_case     			=       self(**snake_case_    )
												__snake_case			,   __snake_case     			=       outputs[:2]
												__snake_case     			=       logits.detach().cpu().numpy()
												__snake_case     			=       inputs["labels"].detach().cpu().numpy()

												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							int    ):
												__snake_case     			=       torch.stack([x["val_loss"] for x in outputs]    ).mean().detach().cpu().item()
												__snake_case     			=       np.concatenate([x["pred"] for x in outputs]							,						axis=0    )

												if self.hparams.glue_output_mode == "classification":
																			__snake_case     			=       np.argmax(snake_case_							,						axis=1    )
												elif self.hparams.glue_output_mode == "regression":
																			__snake_case     			=       np.squeeze(snake_case_    )

												__snake_case     			=       np.concatenate([x["target"] for x in outputs]							,						axis=0    )
												__snake_case     			=       [[] for _ in range(out_label_ids.shape[0]    )]
												__snake_case     			=       [[] for _ in range(out_label_ids.shape[0]    )]

												__snake_case     			=       {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task							,						snake_case_							,						snake_case_    )}

												__snake_case     			=       dict(results.items()    )
												__snake_case     			=       results
												return ret, preds_list, out_label_list





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							list    ):
												__snake_case			,   __snake_case			,   __snake_case     			=       self._eval_end(snake_case_    )
												__snake_case     			=       ret["log"]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict    ):
												__snake_case			,   __snake_case			,   __snake_case     			=       self._eval_end(snake_case_    )
												__snake_case     			=       ret["log"]
												# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
												return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					@staticmethod
					def 							lowerCAmelCase      (				snake_case_   :							List[Any]							,						snake_case_   :							List[str]    ):
												BaseTransformer.add_model_specific_args(snake_case_							,						snake_case_    )
												parser.add_argument(
												    "--max_seq_length"							,						default=128							,						type=snake_case_							,						help=(
												        "The maximum total input sequence length after tokenization. Sequences longer "
												        "than this will be truncated, sequences shorter will be padded."
												    )							,						)

												parser.add_argument(
												    "--task"							,						default=""							,						type=snake_case_							,						required=snake_case_							,						help="The GLUE task to run"							,						)
												parser.add_argument(
												    "--gpus"							,						default=0							,						type=snake_case_							,						help="The number of GPUs allocated for this, it is by default 0 meaning none"							,						)

												parser.add_argument(
												    "--overwrite_cache"							,						action="store_true"							,						help="Overwrite the cached training and evaluation sets"    )

												return parser




def   __UpperCamelCase   (					)  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       argparse.ArgumentParser()
							add_generic_args(SCREAMING_SNAKE_CASE							,    os.getcwd() )
							__snake_case     			=       GLUETransformer.add_model_specific_args(SCREAMING_SNAKE_CASE							,    os.getcwd() )
							__snake_case     			=       parser.parse_args()

							# If output_dir not provided, a folder will be generated in pwd
							if args.output_dir is None:
														__snake_case     			=       os.path.join(
														    "./results"							,    F'''{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}'''							,    )
														os.makedirs(args.output_dir )

							__snake_case     			=       GLUETransformer(SCREAMING_SNAKE_CASE )
							__snake_case     			=       generic_train(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Optionally, predict on dev set and write to output_dir
							if args.do_predict:
														__snake_case     			=       sorted(glob.glob(os.path.join(args.output_dir							,    "checkpoint-epoch=*.ckpt" )							,    recursive=SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       model.load_from_checkpoint(checkpoints[-1] )
														return trainer.test(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
