'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  '''▁'''

__lowercase          =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowercase          =  {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowercase          =  {'''vinai/bartpho-syllable''': 1_0_2_4}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       VOCAB_FILES_NAMES
			UpperCAmelCase_			:     Optional[Any]      =       PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_			:     List[str]      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_			:     Tuple      =       ['''input_ids''', '''attention_mask''']



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase="<s>"       ,    __lowerCAmelCase="</s>"       ,    __lowerCAmelCase="</s>"       ,    __lowerCAmelCase="<s>"       ,    __lowerCAmelCase="<unk>"       ,    __lowerCAmelCase="<pad>"       ,    __lowerCAmelCase="<mask>"       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  AddedToken(__lowerCAmelCase       ,    lstrip=__lowerCAmelCase       ,    rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase       ,    __lowerCAmelCase) else mask_token

					lowerCAmelCase   					=  {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=__lowerCAmelCase       ,    eos_token=__lowerCAmelCase       ,    unk_token=__lowerCAmelCase       ,    sep_token=__lowerCAmelCase       ,    cls_token=__lowerCAmelCase       ,    pad_token=__lowerCAmelCase       ,    mask_token=__lowerCAmelCase       ,    sp_model_kwargs=self.sp_model_kwargs       ,    **__lowerCAmelCase       ,    )

					lowerCAmelCase   					=  vocab_file
					lowerCAmelCase   					=  monolingual_vocab_file
					lowerCAmelCase   					=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(__lowerCAmelCase))

					# Load the reduced vocab

					# Keep order of special tokens for backward compatibility
					lowerCAmelCase   					=  {}
					lowerCAmelCase   					=  0
					for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
							if str(__lowerCAmelCase) not in self.fairseq_tokens_to_ids:
									lowerCAmelCase   					=  cnt
									cnt += 1
					with open(__lowerCAmelCase       ,    """r"""       ,    encoding="""utf-8""") as f:
							for line in f.readlines():
									lowerCAmelCase   					=  line.strip().split()[0]
									lowerCAmelCase   					=  len(self.fairseq_tokens_to_ids)
					if str(__lowerCAmelCase) not in self.fairseq_tokens_to_ids:
							lowerCAmelCase   					=  len(self.fairseq_tokens_to_ids)

					lowerCAmelCase   					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}



			def __getstate__(   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.__dict__.copy()
					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.sp_model.serialized_model_proto()
					return state



			def __setstate__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  d

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

					lowerCAmelCase   					=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowerCAmelCase   					=  [self.cls_token_id]
					lowerCAmelCase   					=  [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = False):




					"""simple docstring"""


					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowerCAmelCase       ,    token_ids_a=__lowerCAmelCase       ,    already_has_special_tokens=__lowerCAmelCase)

					if token_ids_a is None:
							return [1] + ([0] * len(__lowerCAmelCase)) + [1]
					return [1] + ([0] * len(__lowerCAmelCase)) + [1, 1] + ([0] * len(__lowerCAmelCase)) + [1]



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  [self.sep_token_id]
					lowerCAmelCase   					=  [self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return len(self.fairseq_ids_to_tokens)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  {self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return self.sp_model.encode(__lowerCAmelCase       ,    out_type=__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					else:
							return self.unk_token_id



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return self.fairseq_ids_to_tokens[index]



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  """""".join(__lowerCAmelCase).replace(__lowerCAmelCase       ,    """ """).strip()
					return out_string







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase   					=  os.path.join(
					    __lowerCAmelCase       ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  os.path.join(
					    __lowerCAmelCase       ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]       ,    )

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

					if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
					    __lowerCAmelCase) and os.path.isfile(self.monolingual_vocab_file):
							copyfile(self.monolingual_vocab_file       ,    __lowerCAmelCase)
					elif not os.path.isfile(self.monolingual_vocab_file):
							with open(__lowerCAmelCase       ,    """w"""       ,    encoding="""utf-8""") as fp:
									for token in self.fairseq_tokens_to_ids:
											if token not in self.all_special_tokens:
													fp.write(f"{str(__lowerCAmelCase)} \n")

					return out_vocab_file, out_monolingual_vocab_file





'''simple docstring'''




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__lowercase          =  logging.getLogger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       '''sequence-classification'''



			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					if type(__lowerCAmelCase) == dict:
							lowerCAmelCase   					=  Namespace(**__lowerCAmelCase)
					lowerCAmelCase   					=  glue_output_modes[hparams.task]
					lowerCAmelCase   					=  glue_tasks_num_labels[hparams.task]

					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase       ,    self.mode)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.model(**__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

					if self.config.model_type not in ["distilbert", "bart"]:
							lowerCAmelCase   					=  batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase   					=  outputs[0]

					lowerCAmelCase   					=  self.trainer.lr_schedulers[0]["""scheduler"""]
					lowerCAmelCase   					=  {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
					return {"loss": loss, "log": tensorboard_logs}



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.hparams
					lowerCAmelCase   					=  processors[args.task]()
					lowerCAmelCase   					=  processor.get_labels()

					for mode in ["train", "dev"]:
							lowerCAmelCase   					=  self._feature_file(__lowerCAmelCase)
							if os.path.exists(__lowerCAmelCase) and not args.overwrite_cache:
									logger.info("""Loading features from cached file %s"""       ,    __lowerCAmelCase)
							else:
									logger.info("""Creating features from dataset file at %s"""       ,    args.data_dir)
									lowerCAmelCase   					=  (
									    processor.get_dev_examples(args.data_dir)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir)
									)
									lowerCAmelCase   					=  convert_examples_to_features(
									    __lowerCAmelCase       ,    self.tokenizer       ,    max_length=args.max_seq_length       ,    label_list=self.labels       ,    output_mode=args.glue_output_mode       ,    )
									logger.info("""Saving features into cached file %s"""       ,    __lowerCAmelCase)
									torch.save(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = False):




					"""simple docstring"""


					lowerCAmelCase   					=  """dev""" if mode == """test""" else mode

					lowerCAmelCase   					=  self._feature_file(__lowerCAmelCase)
					logger.info("""Loading features from cached file %s"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  torch.load(__lowerCAmelCase)
					lowerCAmelCase   					=  torch.tensor([f.input_ids for f in features]       ,    dtype=torch.long)
					lowerCAmelCase   					=  torch.tensor([f.attention_mask for f in features]       ,    dtype=torch.long)
					lowerCAmelCase   					=  torch.tensor([f.token_type_ids for f in features]       ,    dtype=torch.long)
					if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase   					=  torch.tensor([f.label for f in features]       ,    dtype=torch.long)
					elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase   					=  torch.tensor([f.label for f in features]       ,    dtype=torch.float)

					return DataLoader(
					    TensorDataset(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)       ,    batch_size=__lowerCAmelCase       ,    shuffle=__lowerCAmelCase       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

					if self.config.model_type not in ["distilbert", "bart"]:
							lowerCAmelCase   					=  batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase   					=  outputs[:2]
					lowerCAmelCase   					=  logits.detach().cpu().numpy()
					lowerCAmelCase   					=  inputs["""labels"""].detach().cpu().numpy()

					return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.stack([x["""val_loss"""] for x in outputs]).mean().detach().cpu().item()
					lowerCAmelCase   					=  np.concatenate([x["""pred"""] for x in outputs]       ,    axis=0)

					if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase   					=  np.argmax(__lowerCAmelCase       ,    axis=1)
					elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase   					=  np.squeeze(__lowerCAmelCase)

					lowerCAmelCase   					=  np.concatenate([x["""target"""] for x in outputs]       ,    axis=0)
					lowerCAmelCase   					=  [[] for _ in range(out_label_ids.shape[0])]
					lowerCAmelCase   					=  [[] for _ in range(out_label_ids.shape[0])]

					lowerCAmelCase   					=  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task       ,    __lowerCAmelCase       ,    __lowerCAmelCase)}

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					BaseTransformer.add_model_specific_args(__lowerCAmelCase       ,    __lowerCAmelCase)
					parser.add_argument(
					    """--max_seq_length"""       ,    default=128       ,    type=__lowerCAmelCase       ,    help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )       ,    )

					parser.add_argument(
					    """--task"""       ,    default=""""""       ,    type=__lowerCAmelCase       ,    required=__lowerCAmelCase       ,    help="""The GLUE task to run"""       ,    )
					parser.add_argument(
					    """--gpus"""       ,    default=0       ,    type=__lowerCAmelCase       ,    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







def 				snake_case__							(			)    ->	Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  argparse.ArgumentParser()
		add_generic_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  GLUETransformer.add_model_specific_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  parser.parse_args()

		# If output_dir not provided, a folder will be generated in pwd
		if args.output_dir is None:
				lowerCAmelCase   					=  os.path.join(
				    """./results"""      ,   f"{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}"      ,   )
				os.makedirs(args.output_dir )

		lowerCAmelCase   					=  GLUETransformer(_A )
		lowerCAmelCase   					=  generic_train(_A      ,   _A )

		# Optionally, predict on dev set and write to output_dir
		if args.do_predict:
				lowerCAmelCase   					=  sorted(glob.glob(os.path.join(args.output_dir      ,   """checkpoint-epoch=*.ckpt""" )      ,   recursive=_A ) )
				lowerCAmelCase   					=  model.load_from_checkpoint(checkpoints[-1] )
				return trainer.test(_A )


if __name__ == "__main__":
						main()




