
"""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


SCREAMING_SNAKE_CASE_            =    logging.getLogger(__name__)
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Dict				   =						"token-classification"




      def __init__(				self ,							lowercase_				)    ->       List[Any]:
          if type(lowercase_				) == dict:
              UpperCAmelCase          =       Namespace(**lowercase_				)
          UpperCAmelCase          =       import_module('tasks'				)
          try:
              UpperCAmelCase          =       getattr(lowercase_ ,							hparams.task_type				)
              UpperCAmelCase          =       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__()}"				)
          UpperCAmelCase          =       self.token_classification_task.get_labels(hparams.labels				)
          UpperCAmelCase          =       CrossEntropyLoss().ignore_index
          super().__init__(lowercase_ ,							len(self.labels				) ,							self.mode				)




      def      a_   (				self ,							**lowercase_				)    ->       Union[str, Any]:
          return self.model(**lowercase_				)




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

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




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.hparams
          for mode in ["train", "dev", "test"]:
              UpperCAmelCase          =       self._feature_file(lowercase_				)
              if os.path.exists(lowercase_				) and not args.overwrite_cache:
                  logger.info('Loading features from cached file %s' ,							lowercase_				)
                  UpperCAmelCase          =       torch.load(lowercase_				)
              else:
                  logger.info('Creating features from dataset file at %s' ,							args.data_dir				)
                  UpperCAmelCase          =       self.token_classification_task.read_examples_from_file(args.data_dir ,							lowercase_				)
                  UpperCAmelCase          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ = False				)    ->       DataLoader:
          UpperCAmelCase          =       self._feature_file(lowercase_				)
          logger.info('Loading features from cached file %s' ,							lowercase_				)
          UpperCAmelCase          =       torch.load(lowercase_				)
          UpperCAmelCase          =       torch.tensor([f.input_ids for f in features] ,							dtype=torch.long				)
          UpperCAmelCase          =       torch.tensor([f.attention_mask for f in features] ,							dtype=torch.long				)
          if features[0].token_type_ids is not None:
              UpperCAmelCase          =       torch.tensor([f.token_type_ids for f in features] ,							dtype=torch.long				)
          else:
              UpperCAmelCase          =       torch.tensor([0 for f in features] ,							dtype=torch.long				)
              # HACK(we will not use this anymore soon)
          UpperCAmelCase          =       torch.tensor([f.label_ids for f in features] ,							dtype=torch.long				)
          return DataLoader(
              TensorDataset(lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				) ,							batch_size=lowercase_				)




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




      def      a_   (				self ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       torch.stack([x['val_loss'] for x in outputs]				).mean()
          UpperCAmelCase          =       np.concatenate([x['pred'] for x in outputs] ,							axis=0				)
          UpperCAmelCase          =       np.argmax(lowercase_ ,							axis=2				)
          UpperCAmelCase          =       np.concatenate([x['target'] for x in outputs] ,							axis=0				)

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

          UpperCAmelCase          =       {
              '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_				),
          }

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




      def      a_   (				self ,							lowercase_				)    ->       int:
          # when stable
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       self._eval_end(lowercase_				)
          UpperCAmelCase          =       ret['log']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




      def      a_   (				self ,							lowercase_				)    ->       int:
          # updating to test_epoch_end instead of deprecated test_end
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       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
          UpperCAmelCase          =       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      a_   (				lowercase_ ,							lowercase_				)    ->       Optional[int]:
          # Add NER specific options
          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=1_2_8 ,							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__":
   SCREAMING_SNAKE_CASE_            =    argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   SCREAMING_SNAKE_CASE_            =    NERTransformer.add_model_specific_args(parser, os.getcwd())
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   SCREAMING_SNAKE_CASE_            =    NERTransformer(args)
   SCREAMING_SNAKE_CASE_            =    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
      SCREAMING_SNAKE_CASE_            =    sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
      SCREAMING_SNAKE_CASE_            =    model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)





"""simple docstring"""

from math import asin, atan, cos, radians, sin, sqrt, tan

SCREAMING_SNAKE_CASE_            =    6_3_7_8_1_3_7.0
SCREAMING_SNAKE_CASE_            =    6_3_5_6_7_5_2.3_1_4_2_4_5
SCREAMING_SNAKE_CASE_            =    637_8137




def      lowercase__    (       lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			)			->   float:






    """simple docstring"""




    UpperCAmelCase          =       (AXIS_A - AXIS_B) / AXIS_A
    UpperCAmelCase          =       atan((1 - flattening) * tan(radians(lowerCAmelCase			)			)			)
    UpperCAmelCase          =       atan((1 - flattening) * tan(radians(lowerCAmelCase			)			)			)
    UpperCAmelCase          =       radians(lowerCAmelCase			)
    UpperCAmelCase          =       radians(lowerCAmelCase			)
    # Equation
    UpperCAmelCase          =       sin((phi_a - phi_a) / 2			)
    UpperCAmelCase          =       sin((lambda_a - lambda_a) / 2			)
    # Square both values
    sin_sq_phi *= sin_sq_phi
    sin_sq_lambda *= sin_sq_lambda
    UpperCAmelCase          =       sqrt(sin_sq_phi + (cos(lowerCAmelCase			) * cos(lowerCAmelCase			) * sin_sq_lambda)			)
    return 2 * RADIUS * asin(lowerCAmelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



