




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    lowerCamelCase    (		_lowerCamelCase						):






   '''simple docstring'''





   UpperCamelCase__								='''token-classification'''

   def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   List[Any]  )						->   List[str]:
        if type(lowerCamelCase_  ) == dict:
             __magic_name__							:	List[str]    							=							Namespace(**lowerCamelCase_  )
        __magic_name__							:	int    							=							import_module('''tasks'''  )
        try:
             __magic_name__							:	Union[str, Any]    							=							getattr(lowerCamelCase_       ,						hparams.task_type  )
             __magic_name__							:	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__()}'''  )
        __magic_name__							:	Union[str, Any]    							=							self.token_classification_task.get_labels(hparams.labels  )
        __magic_name__							:	Optional[int]    							=							CrossEntropyLoss().ignore_index
        super().__init__(lowerCamelCase_       ,						len(self.labels  )       ,						self.mode  )

   def 							UpperCAmelCase__       ( self			:   Dict       ,						**lowerCamelCase_			:   Optional[int]  )						->   Union[str, Any]:
        return self.model(**lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Union[str, Any]  )						->   List[str]:
        __magic_name__							:	List[str]    							=							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
             __magic_name__							:	List[str]    							=							(
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids

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

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

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

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict  )						->   int:
        """Compute validation""" ""
        __magic_name__							:	Tuple    							=							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
             __magic_name__							:	int    							=							(
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids
        __magic_name__							:	str    							=							self(**lowerCamelCase_  )
        __magic_name__							:	List[Any]    							=							outputs[:2]
        __magic_name__							:	Dict    							=							logits.detach().cpu().numpy()
        __magic_name__							:	Union[str, Any]    							=							inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   str  )						->   Any:
        __magic_name__							:	Optional[int]    							=							torch.stack([x['''val_loss'''] for x in outputs]  ).mean()
        __magic_name__							:	str    							=							np.concatenate([x['''pred'''] for x in outputs]       ,						axis=0  )
        __magic_name__							:	Any    							=							np.argmax(lowerCamelCase_       ,						axis=2  )
        __magic_name__							:	str    							=							np.concatenate([x['''target'''] for x in outputs]       ,						axis=0  )

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

        __magic_name__							:	List[str]    							=							{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(lowerCamelCase_       ,						lowerCamelCase_  ),
            '''precision''': precision_score(lowerCamelCase_       ,						lowerCamelCase_  ),
            '''recall''': recall_score(lowerCamelCase_       ,						lowerCamelCase_  ),
            '''f1''': fa_score(lowerCamelCase_       ,						lowerCamelCase_  ),
        }

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

   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]  )						->   Dict:
        # when stable
        __magic_name__							:	Optional[Any]    							=							self._eval_end(lowerCamelCase_  )
        __magic_name__							:	int    							=							ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Optional[int]  )						->   Union[str, Any]:
        # updating to test_epoch_end instead of deprecated test_end
        __magic_name__							:	str    							=							self._eval_end(lowerCamelCase_  )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        __magic_name__							:	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__       ( lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Tuple  )						->   Tuple:
        # Add NER specific options
        BaseTransformer.add_model_specific_args(lowerCamelCase_       ,						lowerCamelCase_  )
        parser.add_argument(
            '''--task_type'''       ,						default='''NER'''       ,						type=lowerCamelCase_       ,						help='''Task type to fine tune in training (e.g. NER, POS, etc)'''  )
        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(
            '''--labels'''       ,						default=''''''       ,						type=lowerCamelCase_       ,						help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''       ,						)
        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


if __name__ == "__main__":
 __lowerCamelCase   :						Dict				=			argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 __lowerCamelCase   :						List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
 __lowerCamelCase   :						Any				=			parser.parse_args()
 __lowerCamelCase   :						List[Any]				=			NERTransformer(args)
 __lowerCamelCase   :						Optional[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
  __lowerCamelCase   :						Union[str, Any]				=			sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
  __lowerCamelCase   :						Optional[Any]				=			model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)









import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



