






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


a_							=    logging.getLogger(__name__)


class      lowercase__     (			_UpperCAmelCase	):
	a_         ="""token-classification"""

	def __init__(							self		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





							if type(__UpperCAmelCase       ) == dict:
													lowerCAmelCase__       =						Namespace(**__UpperCAmelCase       )
							lowerCAmelCase__       =						import_module("tasks"       )
							try:
													lowerCAmelCase__       =						getattr(__UpperCAmelCase		,    hparams.task_type       )
													lowerCAmelCase__       =						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__()}"       )
							lowerCAmelCase__       =						self.token_classification_task.get_labels(hparams.labels       )
							lowerCAmelCase__       =						CrossEntropyLoss().ignore_index
							super().__init__(__UpperCAmelCase		,    len(self.labels       )		,    self.mode       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





							return self.model(**__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





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

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

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.hparams
							for mode in ["train", "dev", "test"]:
													lowerCAmelCase__       =						self._feature_file(__UpperCAmelCase       )
													if os.path.exists(__UpperCAmelCase       ) and not args.overwrite_cache:
																			logger.info("Loading features from cached file %s"		,    __UpperCAmelCase       )
																			lowerCAmelCase__       =						torch.load(__UpperCAmelCase       )
													else:
																			logger.info("Creating features from dataset file at %s"		,    args.data_dir       )
																			lowerCAmelCase__       =						self.token_classification_task.read_examples_from_file(args.data_dir		,    __UpperCAmelCase       )
																			lowerCAmelCase__       =						self.token_classification_task.convert_examples_to_features(
																			    __UpperCAmelCase		,    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=__UpperCAmelCase		,    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"		,    __UpperCAmelCase       )
																			torch.save(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = False       )->					DataLoader:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





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

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

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

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						self._eval_end(__UpperCAmelCase       )
							lowerCAmelCase__       =						ret["log"]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						self._eval_end(__UpperCAmelCase       )

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							__UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							BaseTransformer.add_model_specific_args(__UpperCAmelCase		,    __UpperCAmelCase       )
							parser.add_argument(
							    "--task_type"		,    default="NER"		,    type=__UpperCAmelCase		,    help="Task type to fine tune in training (e.g. NER, POS, etc)"       )
							parser.add_argument(
							    "--max_seq_length"		,    default=128		,    type=__UpperCAmelCase		,    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=__UpperCAmelCase		,    help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,    )
							parser.add_argument(
							    "--gpus"		,    default=0		,    type=__UpperCAmelCase		,    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__":
	a_							=    argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	a_							=    NERTransformer.add_model_specific_args(parser, os.getcwd())
	a_							=    parser.parse_args()
	a_							=    NERTransformer(args)
	a_							=    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
		a_							=    sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
		a_							=    model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)














from collections import defaultdict
from math import ceil, sqrt


def        _a  (     UpperCamelCase_					:				int = 1_000_000    ,   UpperCamelCase_					:				int = 10   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						defaultdict(UpperCamelCase_   )

						for outer_width in range(3    ,   (t_limit // 4) + 2   ):
												if outer_width * outer_width > t_limit:
																		lowerCAmelCase__       =						max(
																		    ceil(sqrt(outer_width * outer_width - t_limit   )   )    ,   1   )
												else:
																		lowerCAmelCase__       =						1

												hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

												for hole_width in range(UpperCamelCase_    ,   outer_width - 1    ,   2   ):
																		count[outer_width * outer_width - hole_width * hole_width] += 1

						return sum(1 for n in count.values() if 1 <= n <= 10   )


if __name__ == "__main__":
	print(F"{solution() = }")






