
"""simple docstring"""





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


UpperCamelCase_       :    int						      =			logging.getLogger(__name__)



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"token-classification"




			def __init__(					self    :       Dict							, _snake_case    :       List[str]						)   ->			Tuple:







								"""simple docstring"""
								if type(_snake_case						) == dict:
													A_								=    Namespace(**_snake_case						)
								A_								=    import_module("tasks"						)
								try:
													A_								=    getattr(_snake_case							, hparams.task_type						)
													A_								=    token_classification_task_clazz()
								except AttributeError:
													raise ValueError(
													    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
													    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'						)
								A_								=    self.token_classification_task.get_labels(hparams.labels						)
								A_								=    CrossEntropyLoss().ignore_index
								super().__init__(_snake_case							, len(self.labels						)							, self.mode						)




			def   lowerCamelCase__  (					self    :       str							, **_snake_case    :       List[Any]						)   ->			Dict:







								"""simple docstring"""
								return self.model(**_snake_case						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Tuple						)   ->			Dict:







								"""simple docstring"""
								A_								=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
								if self.config.model_type != "distilbert":
													A_								=    (
													    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
													)  # XLM and RoBERTa don"t use token_type_ids

								A_								=    self(**_snake_case						)
								A_								=    outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    self.hparams
								for mode in ["train", "dev", "test"]:
													A_								=    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						)
																		A_								=    torch.load(_snake_case						)
													else:
																		logger.info("Creating features from dataset file at %s"							, args.data_dir						)
																		A_								=    self.token_classification_task.read_examples_from_file(args.data_dir							, _snake_case						)
																		A_								=    self.token_classification_task.convert_examples_to_features(
																		    _snake_case							, self.labels							, args.max_seq_length							, self.tokenizer							, cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)							, cls_token=self.tokenizer.cls_token							, cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0							, sep_token=self.tokenizer.sep_token							, sep_token_extra=_snake_case							, pad_on_left=bool(self.config.model_type in ["xlnet"]						)							, pad_token=self.tokenizer.pad_token_id							, pad_token_segment_id=self.tokenizer.pad_token_type_id							, pad_token_label_id=self.pad_token_label_id							, )
																		logger.info("Saving features into cached file %s"							, _snake_case						)
																		torch.save(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       int							, _snake_case    :       int							, _snake_case    :       bool = False						)   ->			DataLoader:







								"""simple docstring"""
								A_								=    self._feature_file(_snake_case						)
								logger.info("Loading features from cached file %s"							, _snake_case						)
								A_								=    torch.load(_snake_case						)
								A_								=    torch.tensor([f.input_ids for f in features]							, dtype=torch.long						)
								A_								=    torch.tensor([f.attention_mask for f in features]							, dtype=torch.long						)
								if features[0].token_type_ids is not None:
													A_								=    torch.tensor([f.token_type_ids for f in features]							, dtype=torch.long						)
								else:
													A_								=    torch.tensor([0 for f in features]							, dtype=torch.long						)
													# HACK(we will not use this anymore soon)
								A_								=    torch.tensor([f.label_ids for f in features]							, dtype=torch.long						)
								return DataLoader(
								    TensorDataset(_snake_case							, _snake_case							, _snake_case							, _snake_case						)							, batch_size=_snake_case						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Union[str, Any]							, _snake_case    :       int						)   ->			List[Any]:







								"""simple docstring"""
								"""Compute validation""" ""
								A_								=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
								if self.config.model_type != "distilbert":
													A_								=    (
													    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
													)  # XLM and RoBERTa don"t use token_type_ids
								A_								=    self(**_snake_case						)
								A_			,     A_								=    outputs[:2]
								A_								=    logits.detach().cpu().numpy()
								A_								=    inputs["labels"].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Dict						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    torch.stack([x["val_loss"] for x in outputs]						).mean()
								A_								=    np.concatenate([x["pred"] for x in outputs]							, axis=0						)
								A_								=    np.argmax(_snake_case							, axis=2						)
								A_								=    np.concatenate([x["target"] for x in outputs]							, axis=0						)

								A_								=    dict(enumerate(self.labels						)						)
								A_								=    [[] for _ in range(out_label_ids.shape[0]						)]
								A_								=    [[] for _ in range(out_label_ids.shape[0]						)]

								for i in range(out_label_ids.shape[0]						):
													for j in range(out_label_ids.shape[1]						):
																		if out_label_ids[i, j] != self.pad_token_label_id:
																							out_label_list[i].append(label_map[out_label_ids[i][j]]						)
																							preds_list[i].append(label_map[preds[i][j]]						)

								A_								=    {
								    "val_loss": val_loss_mean,
								    "accuracy_score": accuracy_score(_snake_case							, _snake_case						),
								    "precision": precision_score(_snake_case							, _snake_case						),
								    "recall": recall_score(_snake_case							, _snake_case						),
								    "f1": fa_score(_snake_case							, _snake_case						),
								}

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




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       List[Any]						)   ->			Tuple:







								"""simple docstring"""
								# when stable
								A_			,     A_			,     A_								=    self._eval_end(_snake_case						)
								A_								=    ret["log"]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       Optional[int]						)   ->			List[Any]:







								"""simple docstring"""
								# updating to test_epoch_end instead of deprecated test_end
								A_			,     A_			,     A_								=    self._eval_end(_snake_case						)

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								A_								=    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[str]							, _snake_case    :       Optional[Any]						)   ->			Any:







								"""simple docstring"""
								# Add NER specific options
								BaseTransformer.add_model_specific_args(_snake_case							, _snake_case						)
								parser.add_argument(
								    "--task_type"							, default="NER"							, type=_snake_case							, help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
								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(
								    "--labels"							, default=""							, type=_snake_case							, help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."							, )
								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


if __name__ == "__main__":
		UpperCamelCase_       :    Optional[Any]						      =			argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		UpperCamelCase_       :    List[Any]						      =			NERTransformer.add_model_specific_args(parser, os.getcwd())
		UpperCamelCase_       :    List[str]						      =			parser.parse_args()
		UpperCamelCase_       :    Union[str, Any]						      =			NERTransformer(args)
		UpperCamelCase_       :    Union[str, Any]						      =			generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				UpperCamelCase_       :    List[str]						      =			sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
				UpperCamelCase_       :    Tuple						      =			model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)






"""simple docstring"""





import operator
def 				A_  (__a					, __a = False					, __a = None					):

					'''simple docstring'''





					A_								=    operator.lt if reverse else operator.gt
					A_								=    solution or []

					if not arr:
										return solution

					A_								=    [arr.pop(0					)]
					for i, item in enumerate(__a					):
										if _operator(__a					, sublist[-1]					):
															sublist.append(__a					)
															arr.pop(__a					)

    #  merging sublist into solution list
					if not solution:
										solution.extend(__a					)
					else:
										while sublist:
															A_								=    sublist.pop(0					)
															for i, xx in enumerate(__a					):
																				if not _operator(__a					, __a					):
																									solution.insert(__a					, __a					)
																									break
															else:
																				solution.append(__a					)

					strand_sort(__a					, __a					, __a					)
					return solution


if __name__ == "__main__":
		assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
		assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]




