


"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						int:


    """simple docstring"""





    while a != 0:
        snake_case					,snake_case    :      Union[str, Any]      					=    b % a, a
    return b







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						int:


    """simple docstring"""





    if gcd(lowercase						,							lowercase ) != 1:
        snake_case    :      Union[str, Any]      					=    F'mod inverse of {a!r} and {m!r} does not exist'
        raise ValueError(lowercase )
    snake_case					,snake_case					,snake_case    :      int      					=    1, 0, a
    snake_case					,snake_case					,snake_case    :      List[Any]      					=    0, 1, m
    while va != 0:
        snake_case    :      Union[str, Any]      					=    ua // va
        snake_case					,snake_case					,snake_case					,snake_case					,snake_case					,snake_case    :      int      					=    (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
    return ua % m









"""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


__snake_case   			=    logging.getLogger(__name__)







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[int]							  =  '''token-classification'''







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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       return self.model(**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




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

       snake_case    :      List[Any]      					=    self(**UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    outputs[0]
       # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss}







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Optional[Any]:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




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

       snake_case    :      Tuple      					=    dict(enumerate(self.labels						)						)
       snake_case    :      str      					=    [[] for _ in range(out_label_ids.shape[0]						)]
       snake_case    :      List[Any]      					=    [[] 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]]						)

       snake_case    :      Union[str, Any]      					=    {
           "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__						),
       }

       snake_case    :      int      					=    dict(results.items()						)
       snake_case    :      Union[str, Any]      					=    results
       return ret, preds_list, out_label_list







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    self._eval_end(UpperCamelCase__						)
       snake_case    :      Tuple      					=    ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      List[Any]      					=    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
       snake_case    :      Optional[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  lowerCamelCase		(	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''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__":
  __snake_case   			=    argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __snake_case   			=    NERTransformer.add_model_specific_args(parser, os.getcwd())
  __snake_case   			=    parser.parse_args()
  __snake_case   			=    NERTransformer(args)
  __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
    __snake_case   			=    sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __snake_case   			=    model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)





