


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


lowerCamelCase		   =			logging.getLogger(__name__)




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Any ='token-classification'






      def __init__(   self		:	Tuple     ,  lowercase_		:	Union[str, Any]					)   ->		Dict:
             """simple docstring"""




             if type(lowercase_					) == dict:
                    _lowerCamelCase			:						Union[str, Any]            =Namespace(**lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =import_module('tasks'					)
             try:
                    _lowerCamelCase			:						Dict            =getattr(lowercase_     ,  hparams.task_type					)
                    _lowerCamelCase			:						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__()}'''					)
             _lowerCamelCase			:						Dict            =self.token_classification_task.get_labels(hparams.labels					)
             _lowerCamelCase			:						Union[str, Any]            =CrossEntropyLoss().ignore_index
             super().__init__(lowercase_     ,  len(self.labels					)     ,  self.mode					)






      def 			lowerCamelCase			(   self		:	List[Any]     ,  **lowercase_		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             return self.model(**lowercase_					)






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




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

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






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.hparams
             for mode in ["train", "dev", "test"]:
                    _lowerCamelCase			:						str            =self._feature_file(lowercase_					)
                    if os.path.exists(lowercase_					) and not args.overwrite_cache:
                           logger.info('Loading features from cached file %s'     ,  lowercase_					)
                           _lowerCamelCase			:						Optional[int]            =torch.load(lowercase_					)
                    else:
                           logger.info('Creating features from dataset file at %s'     ,  args.data_dir					)
                           _lowerCamelCase			:						Optional[Any]            =self.token_classification_task.read_examples_from_file(args.data_dir     ,  lowercase_					)
                           _lowerCamelCase			:						int            =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 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	int     ,  lowercase_		:	int     ,  lowercase_		:	bool = False					)   ->		DataLoader:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	Tuple					)   ->		Tuple:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	List[Any]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =torch.stack([x['val_loss'] for x in outputs]					).mean()
             _lowerCamelCase			:						List[str]            =np.concatenate([x['pred'] for x in outputs]     ,  axis=0					)
             _lowerCamelCase			:						int            =np.argmax(lowercase_     ,  axis=2					)
             _lowerCamelCase			:						List[str]            =np.concatenate([x['target'] for x in outputs]     ,  axis=0					)

             _lowerCamelCase			:						Optional[int]            =dict(enumerate(self.labels					)					)
             _lowerCamelCase			:						Any            =[[] for _ in range(out_label_ids.shape[0]					)]
             _lowerCamelCase			:						Optional[int]            =[[] 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			:						Optional[int]            ={
                 '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_					),
             }

             _lowerCamelCase			:						List[str]            =dict(results.items()					)
             _lowerCamelCase			:						Optional[int]            =results
             return ret, preds_list, out_label_list






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	Optional[Any]					)   ->		Optional[int]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	int					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						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
             _lowerCamelCase			:						List[str]            =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			(   lowercase_		:	Optional[Any]     ,  lowercase_		:	Dict					)   ->		List[str]:
             """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__":
 lowerCamelCase		   =			argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 lowerCamelCase		   =			NERTransformer.add_model_specific_args(parser, os.getcwd())
 lowerCamelCase		   =			parser.parse_args()
 lowerCamelCase		   =			NERTransformer(args)
 lowerCamelCase		   =			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
  lowerCamelCase		   =			sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
  lowerCamelCase		   =			model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


