





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


_a     :    List[Any]         =						logging.getLogger(__name__)
class   a_ (			__a      ):
			A__      :			Optional[int]       	=  '''token-classification'''

			def __init__(   self   :  List[str]     , UpperCAmelCase__   :  Optional[int]						):






							"""simple docstring"""
							if type(__UpperCamelCase						) == dict:
											snake_case   :					Optional[Any]    						=				Namespace(**__UpperCamelCase						)
							snake_case   :					Tuple    						=				import_module('''tasks'''						)
							try:
											snake_case   :					str    						=				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   :					List[Any]    						=				self.token_classification_task.get_labels(hparams.labels						)
							snake_case   :					Optional[int]    						=				CrossEntropyLoss().ignore_index
							super().__init__(__UpperCamelCase     , len(self.labels						)     , self.mode						)

			def      lowerCAmelCase(   self   :  Optional[int]     , **UpperCAmelCase__   :  Any						):






							"""simple docstring"""
							return self.model(**__UpperCamelCase						)

			def      lowerCAmelCase(   self   :  str     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  Any						):






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

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

			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							snake_case   :					str    						=				self.hparams
							for mode in ["train", "dev", "test"]:
											snake_case   :					int    						=				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   :					Union[str, Any]    						=				torch.load(__UpperCamelCase						)
											else:
															logger.info('''Creating features from dataset file at %s'''     , args.data_dir						)
															snake_case   :					int    						=				self.token_classification_task.read_examples_from_file(args.data_dir     , __UpperCamelCase						)
															snake_case   :					str    						=				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   :  Optional[int]     , UpperCAmelCase__   :  Tuple     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  List[Any] = False						):






							"""simple docstring"""
							snake_case   :					List[Any]    						=				self._feature_file(__UpperCamelCase						)
							logger.info('''Loading features from cached file %s'''     , __UpperCamelCase						)
							snake_case   :					Optional[Any]    						=				torch.load(__UpperCamelCase						)
							snake_case   :					Any    						=				torch.tensor([f.input_ids for f in features]     , dtype=torch.long						)
							snake_case   :					int    						=				torch.tensor([f.attention_mask for f in features]     , dtype=torch.long						)
							if features[0].token_type_ids is not None:
											snake_case   :					Optional[Any]    						=				torch.tensor([f.token_type_ids for f in features]     , dtype=torch.long						)
							else:
											snake_case   :					List[Any]    						=				torch.tensor([0 for f in features]     , dtype=torch.long						)
											# HACK(we will not use this anymore soon)
							snake_case   :					List[Any]    						=				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   :  List[str]     , UpperCAmelCase__   :  str     , UpperCAmelCase__   :  int						):






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

			def      lowerCAmelCase(   self   :  Optional[Any]     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							snake_case   :					Tuple    						=				torch.stack([x['''val_loss'''] for x in outputs]						).mean()
							snake_case   :					Tuple    						=				np.concatenate([x['''pred'''] for x in outputs]     , axis=0						)
							snake_case   :					Optional[int]    						=				np.argmax(__UpperCamelCase     , axis=2						)
							snake_case   :					int    						=				np.concatenate([x['''target'''] for x in outputs]     , axis=0						)

							snake_case   :					Dict    						=				dict(enumerate(self.labels						)						)
							snake_case   :					Optional[Any]    						=				[[] for _ in range(out_label_ids.shape[0]						)]
							snake_case   :					Union[str, 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   :					int    						=				{
							    """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   :					str    						=				dict(results.items()						)
							snake_case   :					Any    						=				results
							return ret, preds_list, out_label_list

			def      lowerCAmelCase(   self   :  Tuple     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							# when stable
							snake_case   :					Optional[int]    						=				self._eval_end(__UpperCamelCase						)
							snake_case   :					Dict    						=				ret["""log"""]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def      lowerCAmelCase(   self   :  str     , UpperCAmelCase__   :  Tuple						):






							"""simple docstring"""
							# updating to test_epoch_end instead of deprecated test_end
							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[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      lowerCAmelCase(   UpperCAmelCase__   :  Optional[Any]     , UpperCAmelCase__   :  Dict						):






							"""simple docstring"""
							# Add NER specific options
							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__":
			_a     :    str         =						argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			_a     :    Union[str, Any]         =						NERTransformer.add_model_specific_args(parser, os.getcwd())
			_a     :    Any         =						parser.parse_args()
			_a     :    Optional[Any]         =						NERTransformer(args)
			_a     :    Dict         =						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
						_a     :    Any         =						sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
						_a     :    str         =						model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	"▁"

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





						def __getstate__(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.__dict__.copy()
												UpperCamelCase_		:							List[str]  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

												# for backward compatibility
												if not hasattr(self			,     """sp_model_kwargs"""						):
																		UpperCamelCase_		:							Any  =		{}

												UpperCamelCase_		:							Dict  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, Any]  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__UpperCamelCase						) + token
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		os.path.join(
												    __UpperCamelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file			,     __UpperCamelCase						)
												elif not os.path.isfile(self.vocab_file						):
																		with open(__UpperCamelCase			,     """wb"""						) as fi:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






