
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


lowercase  :					List[str]						    =							logging.getLogger(__name__)

class __snake_case		(      __lowercase     ):
    _a		:						Optional[Any]=				"token-classification"
    def __init__(  self		,snake_case      ):



           '''simple docstring'''







           if type(snake_case      ) == dict:
                  lowercase							:     List[str]       	=      Namespace(**snake_case      )
           lowercase							:     str       	=      import_module("""tasks"""      )
           try:
                  lowercase							:     List[str]       	=      getattr(snake_case		,hparams.task_type      )
                  lowercase							:     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__()}"      )
           lowercase							:     str       	=      self.token_classification_task.get_labels(hparams.labels      )
           lowercase							:     Tuple       	=      CrossEntropyLoss().ignore_index
           super().__init__(snake_case		,len(self.labels      )		,self.mode      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return self.model(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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

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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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

           lowercase							:     Dict       	=      dict(enumerate(self.labels      )      )
           lowercase							:     int       	=      [[] for _ in range(out_label_ids.shape[0]      )]
           lowercase							:     Dict       	=      [[] 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]]      )

           lowercase							:     Optional[Any]       	=      {
               "val_loss": val_loss_mean,
               "accuracy_score": accuracy_score(snake_case		,snake_case      ),
               "precision": precision_score(snake_case		,snake_case      ),
               "recall": recall_score(snake_case		,snake_case      ),
               "f1": fa_score(snake_case		,snake_case      ),
           }

           lowercase							:     int       	=      dict(results.items()      )
           lowercase							:     List[str]       	=      results
           return ret, preds_list, out_label_list
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     str       	=      self._eval_end(snake_case      )

           # Converting to the dict required by pl
           # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
           # pytorch_lightning/trainer/logging.py#L139
           lowercase							:     str       	=      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       _SCREAMING_SNAKE_CASE       (  snake_case		,snake_case      ):



           '''simple docstring'''







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


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:int     ,   a		:List[Any]     ,   a		:Tuple="</s>"     ,   a		:str="<unk>"     ,   a		:Dict=[]     ,   a		:Optional[Dict[str, Any]] = None     ,   **a		:Union[str, Any]     ,   )		-> None:
								__UpperCamelCase	:   str			   =					{} if sp_model_kwargs is None else sp_model_kwargs

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

								__UpperCamelCase	:   Optional[Any]			   =					vocab_file
								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								return self.sp_model.get_piece_size()







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







	def __getstate__(					self		:str						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Optional[Any]			   =					None
								return state







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

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

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







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







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								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(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







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

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

								return (out_vocab_file,)