





'''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_		:  Optional[int]		 =						logging.getLogger(__name__)
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''token-classification'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE       ):
    if type(__SCREAMING_SNAKE_CASE       ) == dict:
     snake_case__     :					Optional[Any]    =  Namespace(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  import_module("""tasks"""       )
    try:
     snake_case__     :					Optional[int]    =  getattr(__SCREAMING_SNAKE_CASE	,		hparams.task_type       )
     snake_case__     :					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__()}"       )
    snake_case__     :					Optional[int]    =  self.token_classification_task.get_labels(hparams.labels       )
    snake_case__     :					Optional[int]    =  CrossEntropyLoss().ignore_index
    super().__init__(__SCREAMING_SNAKE_CASE	,		len(self.labels       )	,		self.mode       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return self.model(**__SCREAMING_SNAKE_CASE       )

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

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

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

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

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

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Any    =  torch.stack([x["""val_loss"""] for x in outputs]       ).mean()
    snake_case__     :					Tuple    =  np.concatenate([x["""pred"""] for x in outputs]	,		axis=0       )
    snake_case__     :					List[Any]    =  np.argmax(__SCREAMING_SNAKE_CASE	,		axis=2       )
    snake_case__     :					Optional[int]    =  np.concatenate([x["""target"""] for x in outputs]	,		axis=0       )

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

    snake_case__     :					int    =  {
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """precision""": precision_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """recall""": recall_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """f1""": fa_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
    }

    snake_case__     :					Any    =  dict(results.items()       )
    snake_case__     :					Dict    =  results
    return ret, preds_list, out_label_list

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # when stable
    snake_case__  ,							snake_case__  ,							snake_case__     :					Any    =  self._eval_end(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # updating to test_epoch_end instead of deprecated test_end
    snake_case__  ,							snake_case__  ,							snake_case__     :					Union[str, Any]    =  self._eval_end(__SCREAMING_SNAKE_CASE       )

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    snake_case__     :					Dict    =  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						(							__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    # Add NER specific options
    BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
    parser.add_argument(
        """--task_type"""	,		default="""NER"""	,		type=__SCREAMING_SNAKE_CASE	,		help="""Task type to fine tune in training (e.g. NER, POS, etc)"""       )
    parser.add_argument(
        """--max_seq_length"""	,		default=1_2_8	,		type=__SCREAMING_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=__SCREAMING_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=__SCREAMING_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__":
    A_		:  Dict		 =						argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    A_		:  int		 =						NERTransformer.add_model_specific_args(parser, os.getcwd())
    A_		:  List[Any]		 =						parser.parse_args()
    A_		:  Union[str, Any]		 =						NERTransformer(args)
    A_		:  str		 =						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_		:  Union[str, Any]		 =						sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
        A_		:  List[str]		 =						model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)






import string

import numpy
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int:
       return b if a == 0 else greatest_common_divisor(b % a			,							UpperCAmelCase_				)


class 			lowercase_		:
       __lowerCamelCase				       =						string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       __lowerCamelCase				       =						numpy.vectorize(lambda A       : x % 3_6       )

       __lowerCamelCase				       =						numpy.vectorize(A       )


       def __init__(    self						,       __A				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.modulus(__A				)  # mod36 calc's on the encrypt key
              self.check_determinant()  # validate the determinant of the encryption key
              SCREAMING_SNAKE_CASE_							:				Dict		=encrypt_key.shape[0]


       def 	_snake_case				(    self						,       __A				)						->    int:
              return self.key_string.index(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              return self.key_string[round(__A				)]


       def 	_snake_case				(    self				)						->    None:
              SCREAMING_SNAKE_CASE_							:				List[str]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=det % len(self.key_string				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=len(self.key_string				)
              if greatest_common_divisor(__A						,       len(self.key_string				)				) != 1:
                     SCREAMING_SNAKE_CASE_							:				Any		=(
                         F'determinant modular {req_l} of encryption key({det}) '
                         F'is not co prime w.r.t {req_l}.\nTry another key.'
                     )
                     raise ValueError(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[char for char in text.upper() if char in self.key_string]

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=chars[-1]
              while len(__A				) % self.break_key != 0:
                     chars.append(__A				)

              return "".join(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				Any		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self.modulus(self.encrypt_key.dot(__A				)				).T.tolist()[
                         0
                     ]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_encrypted				)
                     encrypted += encrypted_batch

              return encrypted


       def 	_snake_case				(    self				)						->    numpy.ndarray:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=det % len(self.key_string				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=None
              for i in range(len(self.key_string				)				):
                     if (det * i) % len(self.key_string				) == 1:
                            SCREAMING_SNAKE_CASE_							:				List[str]		=i
                            break

              SCREAMING_SNAKE_CASE_							:				List[str]		=(
                  det_inv
                  * numpy.linalg.det(self.encrypt_key				)
                  * numpy.linalg.inv(self.encrypt_key				)
              )

              return self.to_int(self.modulus(__A				)				)





       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				int		=self.make_decrypt_key()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Any		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				str		=self.modulus(decrypt_key.dot(__A				)				).T.tolist()[0]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_decrypted				)
                     decrypted += decrypted_batch

              return decrypted






def 							SCREAMING_SNAKE_CASE_	(					)			->						None:
       SCREAMING_SNAKE_CASE_							:				List[str]		=int(input('''Enter the order of the encryption key: '''				)				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[]

       print('''Enter each row of the encryption key with space separated integers'''				)
       for _ in range(UpperCAmelCase_				):
              SCREAMING_SNAKE_CASE_							:				Tuple		=[int(UpperCAmelCase_				) for x in input().split()]
              hill_matrix.append(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=HillCipher(numpy.array(UpperCAmelCase_				)				)

       print('''Would you like to encrypt or decrypt some text? (1 or 2)'''				)
       SCREAMING_SNAKE_CASE_							:				Dict		=input('''\n1. Encrypt\n2. Decrypt\n'''				)
       if option == "1":
              SCREAMING_SNAKE_CASE_							:				List[Any]		=input('''What text would you like to encrypt?: '''				)
              print('''Your encrypted text is:'''				)
              print(hc.encrypt(UpperCAmelCase_				)				)
       elif option == "2":
              SCREAMING_SNAKE_CASE_							:				Any		=input('''What text would you like to decrypt?: '''				)
              print('''Your decrypted text is:'''				)
              print(hc.decrypt(UpperCAmelCase_				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
