




"""simple docstring"""


import json
import os
from typing import Optional, Tuple

import regex as re

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


_A													=      logging.get_logger(__name__)

_A													=      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_A													=      {
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

_A													=      {
    """ctrl""": 2_5_6,
}

_A													=      {
    """Pregnancy""": 1_6_8_6_2_9,
    """Christianity""": 7_6_7_5,
    """Explain""": 1_0_6_4_2_3,
    """Fitness""": 6_3_4_4_0,
    """Saving""": 6_3_1_6_3,
    """Ask""": 2_7_1_7_1,
    """Ass""": 9_5_9_8_5,
    """Joke""": 1_6_3_5_0_9,
    """Questions""": 4_5_6_2_2,
    """Thoughts""": 4_9_6_0_5,
    """Retail""": 5_2_3_4_2,
    """Feminism""": 1_6_4_3_3_8,
    """Writing""": 1_1_9_9_2,
    """Atheism""": 1_9_2_2_6_3,
    """Netflix""": 4_8_6_1_6,
    """Computing""": 3_9_6_3_9,
    """Opinion""": 4_3_2_1_3,
    """Alone""": 4_4_9_6_7,
    """Funny""": 5_8_9_1_7,
    """Gaming""": 4_0_3_5_8,
    """Human""": 4_0_8_8,
    """India""": 1_3_3_1,
    """Joker""": 7_7_1_3_8,
    """Diet""": 3_6_2_0_6,
    """Legal""": 1_1_8_5_9,
    """Norman""": 4_9_3_9,
    """Tip""": 7_2_6_8_9,
    """Weight""": 5_2_3_4_3,
    """Movies""": 4_6_2_7_3,
    """Running""": 2_3_4_2_5,
    """Science""": 2_0_9_0,
    """Horror""": 3_7_7_9_3,
    """Confession""": 6_0_5_7_2,
    """Finance""": 1_2_2_5_0,
    """Politics""": 1_6_3_6_0,
    """Scary""": 1_9_1_9_8_5,
    """Support""": 1_2_6_5_4,
    """Technologies""": 3_2_5_1_6,
    """Teenage""": 6_6_1_6_0,
    """Event""": 3_2_7_6_9,
    """Learned""": 6_7_4_6_0,
    """Notion""": 1_8_2_7_7_0,
    """Wikipedia""": 3_7_5_8_3,
    """Books""": 6_6_6_5,
    """Extract""": 7_6_0_5_0,
    """Confessions""": 1_0_2_7_0_1,
    """Conspiracy""": 7_5_9_3_2,
    """Links""": 6_3_6_7_4,
    """Narcissus""": 1_5_0_4_2_5,
    """Relationship""": 5_4_7_6_6,
    """Relationships""": 1_3_4_7_9_6,
    """Reviews""": 4_1_6_7_1,
    """News""": 4_2_5_6,
    """Translation""": 2_6_8_2_0,
    """multilingual""": 1_2_8_4_0_6,
}



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Optional[int]   =	set()
      lowerCAmelCase__      :			List[Any]   =	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            lowerCAmelCase__      :			Dict   =	char

      lowerCAmelCase__      :			str   =	set(__UpperCAmelCase				)
      return pairs


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :List[Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Tuple     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    :List[Any]     				=     CONTROL_CODES






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Tuple="<unk>"   ,     **UpperCamelCase       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         super().__init__(unk_token=UpperCamelCase   ,     **UpperCamelCase   )

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			List[Any]   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	{v: k for k, v in self.encoder.items()}
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as merges_handle:
               lowerCAmelCase__      :			Dict   =	merges_handle.read().split("""\n"""   )[1:-1]
         lowerCAmelCase__      :			str   =	[tuple(merge.split()   ) for merge in merges]
         lowerCAmelCase__      :			Optional[int]   =	dict(zip(UpperCamelCase   ,     range(len(UpperCamelCase   )   )   )   )
         lowerCAmelCase__      :			Union[str, Any]   =	{}






   @property
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Any:

         """simple docstring"""
         return len(self.encoder   )






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

         """simple docstring"""
         return dict(self.encoder   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Tuple   )			->						Dict:

         """simple docstring"""
         if token in self.cache:
               return self.cache[token]
         lowerCAmelCase__      :			Tuple   =	tuple(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	tuple(list(word[:-1]   ) + [word[-1] + """</w>"""]   )
         lowerCAmelCase__      :			Union[str, Any]   =	get_pairs(UpperCamelCase   )

         if not pairs:
               return token

         while True:
               lowerCAmelCase__      :			Tuple   =	min(UpperCamelCase   ,     key=lambda UpperCamelCase   : self.bpe_ranks.get(UpperCamelCase   ,     float("""inf"""   )   )   )
               if bigram not in self.bpe_ranks:
                     break
               lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	bigram
               lowerCAmelCase__      :			Optional[int]   =	[]
               lowerCAmelCase__      :			Dict   =	0
               while i < len(UpperCamelCase   ):
                     try:
                           lowerCAmelCase__      :			List[str]   =	word.index(UpperCamelCase   ,     UpperCamelCase   )
                     except ValueError:
                           new_word.extend(word[i:]   )
                           break
                     else:
                           new_word.extend(word[i:j]   )
                           lowerCAmelCase__      :			List[str]   =	j

                     if word[i] == first and i < len(UpperCamelCase   ) - 1 and word[i + 1] == second:
                           new_word.append(first + second   )
                           i += 2
                     else:
                           new_word.append(word[i]   )
                           i += 1
               lowerCAmelCase__      :			Dict   =	tuple(UpperCamelCase   )
               lowerCAmelCase__      :			int   =	new_word
               if len(UpperCamelCase   ) == 1:
                     break
               else:
                     lowerCAmelCase__      :			List[str]   =	get_pairs(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	"""@@ """.join(UpperCamelCase   )
         lowerCAmelCase__      :			int   =	word[:-4]
         lowerCAmelCase__      :			int   =	word
         return word






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	[]

         lowerCAmelCase__      :			str   =	re.findall(r"""\S+\n?"""   ,     UpperCamelCase   )

         for token in words:
               split_tokens.extend(list(self.bpe(UpperCamelCase   ).split(""" """   )   )   )
         return split_tokens






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   )			->						List[Any]:

         """simple docstring"""
         return self.encoder.get(UpperCamelCase   ,     self.encoder.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		List[str]   )			->						str:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   ,     self.unk_token   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	""" """.join(UpperCamelCase   ).replace("""@@ """   ,     """"""   ).strip()
         return out_string






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
         lowerCAmelCase__      :			Tuple   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
         lowerCAmelCase__      :			Dict   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.encoder   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         lowerCAmelCase__      :			List[str]   =	0
         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as writer:
               writer.write("""#version: 0.2\n"""   )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,     key=lambda UpperCamelCase   : kv[1]   ):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                               """ Please check that the tokenizer is not corrupted!"""   )
                           lowerCAmelCase__      :			Any   =	token_index
                     writer.write(""" """.join(UpperCamelCase   ) + """\n"""   )
                     index += 1

         return vocab_file, merge_file

   # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
   #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
   #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
   #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
   #     return ''.join(tokens_generated_so_far)











"""simple docstring"""


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													=      logging.getLogger(__name__)


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Union[str, Any]     				=     "token-classification"






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Any   )			->						Optional[int]:

         """simple docstring"""
         if type(UpperCamelCase   ) == dict:
               lowerCAmelCase__      :			Optional[int]   =	Namespace(**UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	import_module("""tasks"""   )
         try:
               lowerCAmelCase__      :			Union[str, Any]   =	getattr(UpperCamelCase   ,     hparams.task_type   )
               lowerCAmelCase__      :			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__()}"""   )
         lowerCAmelCase__      :			Optional[Any]   =	self.token_classification_task.get_labels(hparams.labels   )
         lowerCAmelCase__      :			Dict   =	CrossEntropyLoss().ignore_index
         super().__init__(UpperCamelCase   ,     len(self.labels   )   ,     self.mode   )






   def 	_lowerCAmelCase   (		self       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						str:

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






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Any   )			->						Dict:

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

         lowerCAmelCase__      :			Tuple   =	self(**UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}






   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

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

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

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






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	torch.stack([x["""val_loss"""] for x in outputs]   ).mean()
         lowerCAmelCase__      :			Any   =	np.concatenate([x["""pred"""] for x in outputs]   ,     axis=0   )
         lowerCAmelCase__      :			List[str]   =	np.argmax(UpperCamelCase   ,     axis=2   )
         lowerCAmelCase__      :			str   =	np.concatenate([x["""target"""] for x in outputs]   ,     axis=0   )

         lowerCAmelCase__      :			Any   =	dict(enumerate(self.labels   )   )
         lowerCAmelCase__      :			str   =	[[] for _ in range(out_label_ids.shape[0]   )]
         lowerCAmelCase__      :			Optional[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]]   )

         lowerCAmelCase__      :			Optional[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   ),
         }

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






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		List[Any]   )			->						Any:

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






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		int   )			->						Optional[Any]:

         """simple docstring"""
         # updating to test_epoch_end instead of deprecated test_end
         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	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
         lowerCAmelCase__      :			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       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   )			->						List[str]:

         """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=1_28   ,     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													=      argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A													=      NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A													=      parser.parse_args()
  _A													=      NERTransformer(args)
  _A													=      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													=      sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A													=      model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)





