





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       :     Dict   =     logging.getLogger(__name__)



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   'token-classification'





      def __init__( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




       return self.model(**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = False   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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

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

       UpperCamelCase	:					Dict       	=   {
           "val_loss": val_loss_mean,
           "accuracy_score": accuracy_score(A_       ,							A_   ),
           "precision": precision_score(A_       ,							A_   ),
           "recall": recall_score(A_       ,							A_   ),
           "f1": fa_score(A_       ,							A_   ),
       }

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					int       	=   self._eval_end(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					str       	=   self._eval_end(A_   )

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       UpperCamelCase	:					Optional[int]       	=   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( A_       ,							A_   ):
       '''simple docstring'''




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








from __future__ import annotations





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):  # noqa: E741
							while r - l > 1:
														snake_case_				=							(l + r) // 2
														if v[m] >= key:
																					snake_case_				=							m
														else:
																					snake_case_				=							m  # noqa: E741
							return r





def 						__UpperCAmelCase			(     a_):
							if len(a_) == 0:
														return 0

							snake_case_				=							[0] * len(a_)
							snake_case_				=							1

							snake_case_				=							v[0]

							for i in range(1							,     len(a_)):
														if v[i] < tail[0]:
																					snake_case_				=							v[i]
														elif v[i] > tail[length - 1]:
																					snake_case_				=							v[i]
																					length += 1
														else:
																					snake_case_				=							v[i]

							return length


if __name__ == "__main__":
	import doctest

	doctest.testmod()





