



"""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


lowerCamelCase  			=		logging.getLogger(__name__)
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	'''token-classification'''



       def __init__(				self     :    int						,							_UpperCAmelCase     :    Dict       )					->		List[str]:


             '''simple docstring'''




             if type(_UpperCAmelCase       ) == dict:
                   UpperCAmelCase_     					=				Namespace(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				import_module("tasks"       )
             try:
                   UpperCAmelCase_     					=				getattr(_UpperCAmelCase						,							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__(_UpperCAmelCase						,							len(self.labels       )						,							self.mode       )



       def 	lowercase__							(				self     :    Optional[int]						,							**_UpperCAmelCase     :    str       )					->		Any:


             '''simple docstring'''




             return self.model(**_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]       )					->		List[Any]:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Optional[int]       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.hparams
             for mode in ["train", "dev", "test"]:
                   UpperCAmelCase_     					=				self._feature_file(_UpperCAmelCase       )
                   if os.path.exists(_UpperCAmelCase       ) and not args.overwrite_cache:
                         logger.info("Loading features from cached file %s"						,							_UpperCAmelCase       )
                         UpperCAmelCase_     					=				torch.load(_UpperCAmelCase       )
                   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						,							_UpperCAmelCase       )
                         UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    bool = False       )					->		DataLoader:


             '''simple docstring'''




             UpperCAmelCase_     					=				self._feature_file(_UpperCAmelCase       )
             logger.info("Loading features from cached file %s"						,							_UpperCAmelCase       )
             UpperCAmelCase_     					=				torch.load(_UpperCAmelCase       )
             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(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )						,							batch_size=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Any       )					->		Dict:


             '''simple docstring'''




             """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(**_UpperCAmelCase       )
             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 	lowercase__							(				self     :    int						,							_UpperCAmelCase     :    Tuple       )					->		Any:


             '''simple docstring'''




             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(_UpperCAmelCase						,							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(_UpperCAmelCase						,							_UpperCAmelCase       ),
                 "precision": precision_score(_UpperCAmelCase						,							_UpperCAmelCase       ),
                 "recall": recall_score(_UpperCAmelCase						,							_UpperCAmelCase       ),
                 "f1": fa_score(_UpperCAmelCase						,							_UpperCAmelCase       ),
             }

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



       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    Dict       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				self._eval_end(_UpperCAmelCase       )
             UpperCAmelCase_     					=				ret["log"]
             return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    str       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				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
             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 	lowercase__							(				_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    int       )					->		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__":
  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)






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()