


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	:		Tuple    =       logging.getLogger(__name__)



class 							A   (	UpperCAmelCase__							):
     '''simple docstring'''






     A__      =			'''token-classification'''

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




       """simple docstring"""


       if type(_UpperCAmelCase		) == dict:
         lowercase__					      =		Namespace(**_UpperCAmelCase		)
       lowercase__					      =		import_module("""tasks"""		)
       try:
         lowercase__					      =		getattr(_UpperCAmelCase				, hparams.task_type		)
         lowercase__					      =		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__					      =		self.token_classification_task.get_labels(hparams.labels		)
       lowercase__					      =		CrossEntropyLoss().ignore_index
       super().__init__(_UpperCAmelCase				, len(self.labels		)				, self.mode		)

     def   lowerCamelCase__					(self   :						Dict				, **_UpperCAmelCase   :						int		)						->       Optional[Any]:




       """simple docstring"""


       return self.model(**_UpperCAmelCase		)

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




       """simple docstring"""


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

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

     def   lowerCamelCase__					(self   :						Tuple		)						->       Dict:




       """simple docstring"""


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




       """simple docstring"""


       lowercase__					      =		self._feature_file(_UpperCAmelCase		)
       logger.info("""Loading features from cached file %s"""				, _UpperCAmelCase		)
       lowercase__					      =		torch.load(_UpperCAmelCase		)
       lowercase__					      =		torch.tensor([f.input_ids for f in features]				, dtype=torch.long		)
       lowercase__					      =		torch.tensor([f.attention_mask for f in features]				, dtype=torch.long		)
       if features[0].token_type_ids is not None:
         lowercase__					      =		torch.tensor([f.token_type_ids for f in features]				, dtype=torch.long		)
       else:
         lowercase__					      =		torch.tensor([0 for f in features]				, dtype=torch.long		)
         # HACK(we will not use this anymore soon)
       lowercase__					      =		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   :						Tuple				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Any		)						->       Any:




       """simple docstring"""


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

     def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						Any		)						->       int:




       """simple docstring"""


       lowercase__					      =		torch.stack([x["""val_loss"""] for x in outputs]		).mean()
       lowercase__					      =		np.concatenate([x["""pred"""] for x in outputs]				, axis=0		)
       lowercase__					      =		np.argmax(_UpperCAmelCase				, axis=2		)
       lowercase__					      =		np.concatenate([x["""target"""] for x in outputs]				, axis=0		)

       lowercase__					      =		dict(enumerate(self.labels		)		)
       lowercase__					      =		[[] for _ in range(out_label_ids.shape[0]		)]
       lowercase__					      =		[[] 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__					      =		{
           """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		),
       }

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

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




       """simple docstring"""


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

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




       """simple docstring"""


       lowercase__      ,     lowercase__      ,     lowercase__					      =		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
       lowercase__					      =		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   :						Any				, _UpperCAmelCase   :						Tuple		)						->       Tuple:




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





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 							A   (	UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			OpenAIGPTTokenizer
					A__      =			OpenAIGPTTokenizerFast
					A__      =			True
					A__      =			False

					def   lowerCamelCase__					(self   :						List[str]		)						->       int:




							"""simple docstring"""


							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowercase__					      =		[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """w</w>""",
							    """r</w>""",
							    """t</w>""",
							    """lo""",
							    """low""",
							    """er</w>""",
							    """low</w>""",
							    """lowest</w>""",
							    """newer</w>""",
							    """wider</w>""",
							    """<unk>""",
							]
							lowercase__					      =		dict(zip(_UpperCAmelCase				, range(len(_UpperCAmelCase		)		)		)		)
							lowercase__					      =		["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

							lowercase__					      =		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["""vocab_file"""]		)
							lowercase__					      =		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["""merges_file"""]		)
							with open(self.vocab_file				, """w"""		) as fp:
									fp.write(json.dumps(_UpperCAmelCase		)		)
							with open(self.merges_file				, """w"""		) as fp:
									fp.write("""\n""".join(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						List[Any]		)						->       Any:




							"""simple docstring"""


							return "lower newer", "lower newer"

					def   lowerCamelCase__					(self   :						Dict		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		OpenAIGPTTokenizer(self.vocab_file				, self.merges_file		)

							lowercase__					      =		"""lower"""
							lowercase__					      =		["""low""", """er</w>"""]
							lowercase__					      =		tokenizer.tokenize(_UpperCAmelCase		)
							self.assertListEqual(_UpperCAmelCase				, _UpperCAmelCase		)

							lowercase__					      =		tokens + ["""<unk>"""]
							lowercase__					      =		[14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						Union[str, Any]=15		)						->       Optional[int]:




							"""simple docstring"""


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
											lowercase__					      =		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase				, **_UpperCAmelCase		)

											# Simple input
											lowercase__					      =		"""This is a simple input"""
											lowercase__					      =		["""This is a simple input 1""", """This is a simple input 2"""]
											lowercase__					      =		("""This is a simple input""", """This is a pair""")
											lowercase__					      =		[
											    ("""This is a simple input 1""", """This is a simple input 2"""),
											    ("""This is a simple pair 1""", """This is a simple pair 2"""),
											]

											# Simple input tests
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Simple input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Simple input
											self.assertRaises(
											    _UpperCAmelCase				, tokenizer_r.batch_encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""				, )

											# Pair input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Pair input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Pair input
											self.assertRaises(
											    _UpperCAmelCase				, tokenizer_r.batch_encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""				, )


					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							pass






@require_ftfy
@require_spacy
@require_tokenizers
class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					pass

