


"""simple docstring"""







import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__    :							str            =					logging.getLogger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Optional[int]    =      "sequence-classification"




	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    List[Any]       )	->     int:
						if type(UpperCAmelCase__       ) == dict:
											__SCREAMING_SNAKE_CASE						       =    Namespace(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    glue_output_modes[hparams.task]
						__SCREAMING_SNAKE_CASE						       =    glue_tasks_num_labels[hparams.task]

						super().__init__(UpperCAmelCase__      ,      UpperCAmelCase__      ,      self.mode       )




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




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

						if self.config.model_type not in ["distilbert", "bart"]:
											__SCREAMING_SNAKE_CASE						       =    batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

						__SCREAMING_SNAKE_CASE						       =    self(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    outputs[0]

						__SCREAMING_SNAKE_CASE						       =    self.trainer.lr_schedulers[0]["scheduler"]
						__SCREAMING_SNAKE_CASE						       =    {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss, "log": tensorboard_logs}




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    self.hparams
						__SCREAMING_SNAKE_CASE						       =    processors[args.task]()
						__SCREAMING_SNAKE_CASE						       =    processor.get_labels()

						for mode in ["train", "dev"]:
											__SCREAMING_SNAKE_CASE						       =    self._feature_file(UpperCAmelCase__       )
											if os.path.exists(UpperCAmelCase__       ) and not args.overwrite_cache:
																logger.info("Loading features from cached file %s"      ,      UpperCAmelCase__       )
											else:
																logger.info("Creating features from dataset file at %s"      ,      args.data_dir       )
																__SCREAMING_SNAKE_CASE						       =    (
																    processor.get_dev_examples(args.data_dir       )
																    if mode == "dev"
																    else processor.get_train_examples(args.data_dir       )
																)
																__SCREAMING_SNAKE_CASE						       =    convert_examples_to_features(
																    UpperCAmelCase__      ,      self.tokenizer      ,      max_length=args.max_seq_length      ,      label_list=self.labels      ,      output_mode=args.glue_output_mode      ,      )
																logger.info("Saving features into cached file %s"      ,      UpperCAmelCase__       )
																torch.save(UpperCAmelCase__      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    bool = False       )	->     DataLoader:
						__SCREAMING_SNAKE_CASE						       =    "dev" if mode == "test" else mode

						__SCREAMING_SNAKE_CASE						       =    self._feature_file(UpperCAmelCase__       )
						logger.info("Loading features from cached file %s"      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    torch.load(UpperCAmelCase__       )
						__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       )
						__SCREAMING_SNAKE_CASE						       =    torch.tensor([f.token_type_ids for f in features]      ,      dtype=torch.long       )
						if self.hparams.glue_output_mode == "classification":
											__SCREAMING_SNAKE_CASE						       =    torch.tensor([f.label for f in features]      ,      dtype=torch.long       )
						elif self.hparams.glue_output_mode == "regression":
											__SCREAMING_SNAKE_CASE						       =    torch.tensor([f.label for f in features]      ,      dtype=torch.float       )

						return DataLoader(
						    TensorDataset(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )      ,      batch_size=UpperCAmelCase__      ,      shuffle=UpperCAmelCase__      ,      )




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Any       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

						if self.config.model_type not in ["distilbert", "bart"]:
											__SCREAMING_SNAKE_CASE						       =    batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

						__SCREAMING_SNAKE_CASE						       =    self(**UpperCAmelCase__       )
						__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   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    Dict       )	->     tuple:
						__SCREAMING_SNAKE_CASE						       =    torch.stack([x["val_loss"] for x in outputs]       ).mean().detach().cpu().item()
						__SCREAMING_SNAKE_CASE						       =    np.concatenate([x["pred"] for x in outputs]      ,      axis=0       )

						if self.hparams.glue_output_mode == "classification":
											__SCREAMING_SNAKE_CASE						       =    np.argmax(UpperCAmelCase__      ,      axis=1       )
						elif self.hparams.glue_output_mode == "regression":
											__SCREAMING_SNAKE_CASE						       =    np.squeeze(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    np.concatenate([x["target"] for x in outputs]      ,      axis=0       )
						__SCREAMING_SNAKE_CASE						       =    [[] for _ in range(out_label_ids.shape[0]       )]
						__SCREAMING_SNAKE_CASE						       =    [[] for _ in range(out_label_ids.shape[0]       )]

						__SCREAMING_SNAKE_CASE						       =    {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )}

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




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    list       )	->     dict:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self._eval_end(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    ret["log"]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    List[str]       )	->     dict:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self._eval_end(UpperCAmelCase__       )
						__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   UpperCAmelCase_				(			UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Optional[int]       )	->     Tuple:
						BaseTransformer.add_model_specific_args(UpperCAmelCase__      ,      UpperCAmelCase__       )
						parser.add_argument(
						    "--max_seq_length"      ,      default=1_2_8      ,      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(
						    "--task"      ,      default=""      ,      type=UpperCAmelCase__      ,      required=UpperCAmelCase__      ,      help="The GLUE task to run"      ,      )
						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




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    argparse.ArgumentParser()
					add_generic_args(lowerCAmelCase_  ,						os.getcwd()       )
					__SCREAMING_SNAKE_CASE						       =    GLUETransformer.add_model_specific_args(lowerCAmelCase_  ,						os.getcwd()       )
					__SCREAMING_SNAKE_CASE						       =    parser.parse_args()

					# If output_dir not provided, a folder will be generated in pwd
					if args.output_dir is None:
										__SCREAMING_SNAKE_CASE						       =    os.path.join(
										    "./results"  ,						f"""{args.task}_{time.strftime('%Y%m%d_%H%M%S'       )}"""  ,						)
										os.makedirs(args.output_dir       )

					__SCREAMING_SNAKE_CASE						       =    GLUETransformer(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    generic_train(lowerCAmelCase_  ,						lowerCAmelCase_       )

					# Optionally, predict on dev set and write to output_dir
					if args.do_predict:
										__SCREAMING_SNAKE_CASE						       =    sorted(glob.glob(os.path.join(args.output_dir  ,						"checkpoint-epoch=*.ckpt"       )  ,						recursive=lowerCAmelCase_       )       )
										__SCREAMING_SNAKE_CASE						       =    model.load_from_checkpoint(checkpoints[-1]       )
										return trainer.test(lowerCAmelCase_       )


if __name__ == "__main__":
		main()




"""simple docstring"""







a__    :							Optional[Any]            =					'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    input("Enter message: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Enter key [alphanumeric]: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Encrypt/Decrypt [e/d]: "       )

					if mode.lower().startswith("e"       ):
										__SCREAMING_SNAKE_CASE						       =    "encrypt"
										__SCREAMING_SNAKE_CASE						       =    encrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )
					elif mode.lower().startswith("d"       ):
										__SCREAMING_SNAKE_CASE						       =    "decrypt"
										__SCREAMING_SNAKE_CASE						       =    decrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )

					print(f"""\n{mode.title()}ed message:"""       )
					print(lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"encrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"decrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    []
					__SCREAMING_SNAKE_CASE						       =    0
					__SCREAMING_SNAKE_CASE						       =    key.upper()

					for symbol in message:
										__SCREAMING_SNAKE_CASE						       =    LETTERS.find(symbol.upper()       )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]       )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]       )

															num %= len(lowerCAmelCase_       )

															if symbol.isupper():
																				translated.append(LETTERS[num]       )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()       )

															key_index += 1
															if key_index == len(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    0
										else:
															translated.append(lowerCAmelCase_       )
					return "".join(lowerCAmelCase_       )


if __name__ == "__main__":
		main()
