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			: Tuple							   =		logging.getLogger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"token-classification"




   def __init__(    self			:				Optional[int]       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return self.model(**_lowercase      )




   def 	__a						(    self			:				Any       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






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

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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.hparams
     for mode in ["train", "dev", "test"]:
       SCREAMING_SNAKE_CASE__										=      self._feature_file(_lowercase      )
       if os.path.exists(_lowercase      ) and not args.overwrite_cache:
         logger.info("""Loading features from cached file %s"""       ,	_lowercase      )
         SCREAMING_SNAKE_CASE__										=      torch.load(_lowercase      )
       else:
         logger.info("""Creating features from dataset file at %s"""       ,	args.data_dir      )
         SCREAMING_SNAKE_CASE__										=      self.token_classification_task.read_examples_from_file(args.data_dir       ,	_lowercase      )
         SCREAMING_SNAKE_CASE__										=      self.token_classification_task.convert_examples_to_features(
             _lowercase       ,	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=_lowercase       ,	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"""       ,	_lowercase      )
         torch.save(_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				str       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._feature_file(_lowercase      )
     logger.info("""Loading features from cached file %s"""       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.load(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([f.input_ids for f in features]       ,	dtype=torch.long      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([f.attention_mask for f in features]       ,	dtype=torch.long      )
     if features[0].token_type_ids is not None:
       SCREAMING_SNAKE_CASE__										=      torch.tensor([f.token_type_ids for f in features]       ,	dtype=torch.long      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.tensor([0 for f in features]       ,	dtype=torch.long      )
       # HACK(we will not use this anymore soon)
     SCREAMING_SNAKE_CASE__										=      torch.tensor([f.label_ids for f in features]       ,	dtype=torch.long      )
     return DataLoader(
         TensorDataset(_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase      )       ,	batch_size=_lowercase      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				int       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Dict       ,	_lowercase			:				str      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      dict(enumerate(self.labels      )      )
     SCREAMING_SNAKE_CASE__										=      [[] for _ in range(out_label_ids.shape[0]      )]
     SCREAMING_SNAKE_CASE__										=      [[] 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]]      )

     SCREAMING_SNAKE_CASE__										=      {
         """val_loss""": val_loss_mean,
         """accuracy_score""": accuracy_score(_lowercase       ,	_lowercase      ),
         """precision""": precision_score(_lowercase       ,	_lowercase      ),
         """recall""": recall_score(_lowercase       ,	_lowercase      ),
         """f1""": fa_score(_lowercase       ,	_lowercase      ),
     }

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




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self._eval_end(_lowercase      )

     # Converting to the dict required by pl
     # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
     # pytorch_lightning/trainer/logging.py#L139
     SCREAMING_SNAKE_CASE__										=      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						(    _lowercase			:				Optional[Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     BaseTransformer.add_model_specific_args(_lowercase       ,	_lowercase      )
     parser.add_argument(
         """--task_type"""       ,	default="""NER"""       ,	type=_lowercase       ,	help="""Task type to fine tune in training (e.g. NER, POS, etc)"""      )
     parser.add_argument(
         """--max_seq_length"""       ,	default=1_28       ,	type=_lowercase       ,	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=_lowercase       ,	help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""       ,	)
     parser.add_argument(
         """--gpus"""       ,	default=0       ,	type=_lowercase       ,	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			: str							   =		argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   __lowerCamelCase			: List[str]							   =		NERTransformer.add_model_specific_args(parser, os.getcwd())
   __lowerCamelCase			: Any							   =		parser.parse_args()
   __lowerCamelCase			: Dict							   =		NERTransformer(args)
   __lowerCamelCase			: 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
      __lowerCamelCase			: Tuple							   =		sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
      __lowerCamelCase			: Union[str, Any]							   =		model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)

import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  sock.connect((host, port)	)
  sock.send(B"""Hello server!"""	)

  with open("""Received_file""" ,     """wb"""	) as out_file:
    print("""File opened"""	)
    print("""Receiving data..."""	)
    while True:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

  print("""Successfully received the file"""	)
  sock.close()
  print("""Connection closed"""	)


if __name__ == "__main__":
   main()
