





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


lowercase				:	str      						=  logging.getLogger(__name__)





class 			A__   (							__UpperCAmelCase     ):




			"""simple docstring"""



			__A       :    Tuple	  =  '''token-classification'''

			def __init__( self ,  lowercase)      ->						List[Any]:







										'''simple docstring'''

										if type(lowercase) == dict:
																	a__						:     List[Any]			=    Namespace(**lowercase)
										a__						:     str			=    import_module('tasks')
										try:
																	a__						:     int			=    getattr(lowercase ,  hparams.task_type)
																	a__						:     TokenClassificationTask			=    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__						:     List[str]			=    self.token_classification_task.get_labels(hparams.labels)
										a__						:     Union[str, Any]			=    CrossEntropyLoss().ignore_index
										super().__init__(lowercase ,  len(self.labels) ,  self.mode)

			def 							__lowercase		( self ,  **lowercase)      ->						Optional[Any]:







										'''simple docstring'''

										return self.model(**lowercase)

			def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Optional[int]:







										'''simple docstring'''

										a__						:     List[Any]			=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
										if self.config.model_type != "distilbert":
																	a__						:     List[Any]			=    (
																	    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
																	)  # XLM and RoBERTa don"t use token_type_ids

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

			def 							__lowercase		( self)      ->						int:







										'''simple docstring'''

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

			def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = False)      ->						DataLoader:







										'''simple docstring'''

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

			def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						List[str]:







										'''simple docstring'''

										"""Compute validation""" ""
										a__						:     int			=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
										if self.config.model_type != "distilbert":
																	a__						:     List[str]			=    (
																	    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
																	)  # XLM and RoBERTa don"t use token_type_ids
										a__						:     List[str]			=    self(**lowercase)
										a__						:     Tuple			=    outputs[:2]
										a__						:     Dict			=    logits.detach().cpu().numpy()
										a__						:     Dict			=    inputs['labels'].detach().cpu().numpy()
										return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

			def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







										'''simple docstring'''

										a__						:     Dict			=    torch.stack([x['val_loss'] for x in outputs]).mean()
										a__						:     List[Any]			=    np.concatenate([x['pred'] for x in outputs] ,  axis=0)
										a__						:     str			=    np.argmax(lowercase ,  axis=2)
										a__						:     Optional[Any]			=    np.concatenate([x['target'] for x in outputs] ,  axis=0)

										a__						:     List[str]			=    dict(enumerate(self.labels))
										a__						:     Tuple			=    [[] for _ in range(out_label_ids.shape[0])]
										a__						:     Dict			=    [[] 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__						:     List[str]			=    {
										    'val_loss': val_loss_mean,
										    'accuracy_score': accuracy_score(lowercase ,  lowercase),
										    'precision': precision_score(lowercase ,  lowercase),
										    'recall': recall_score(lowercase ,  lowercase),
										    'f1': fa_score(lowercase ,  lowercase),
										}

										a__						:     Optional[Any]			=    dict(results.items())
										a__						:     Optional[Any]			=    results
										return ret, preds_list, out_label_list

			def 							__lowercase		( self ,  lowercase)      ->						Tuple:







										'''simple docstring'''

										a__						:     Any			=    self._eval_end(lowercase)
										a__						:     List[str]			=    ret['log']
										return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







										'''simple docstring'''

										a__						:     Optional[Any]			=    self._eval_end(lowercase)

										# Converting to the dict required by pl
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
										# pytorch_lightning/trainer/logging.py#L139
										a__						:     Any			=    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 							__lowercase		( lowercase ,  lowercase)      ->						Tuple:







										'''simple docstring'''

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









def A_     (		A__		,				A__		,				A__    )  ->		float:
       if principal <= 0:
              raise Exception('Principal borrowed must be > 0'    )
       if rate_per_annum < 0:
              raise Exception('Rate of interest must be >= 0'    )
       if years_to_repay <= 0 or not isinstance(A__		,				A__    ):
              raise Exception('Years to repay must be an integer > 0'    )

       # Yearly rate is divided by 12 to get monthly rate
       a__						:     str			=    rate_per_annum / 12

       # Years to repay is multiplied by 12 to get number of payments as payment is monthly
       a__						:     List[Any]			=    years_to_repay * 12

       return (
           principal
           * rate_per_month
           * (1 + rate_per_month) ** number_of_payments
           / ((1 + rate_per_month) ** number_of_payments - 1)
       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


