
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


lowercase  :					List[str]													=       logging.getLogger(__name__)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'token-classification'







	def __init__(					self		:Any     ,   a		:str						)		-> int:
								if type(a						) == dict:
															__UpperCamelCase	:   List[str]			   =					Namespace(**a						)
								__UpperCamelCase	:   str			   =					import_module("tasks"						)
								try:
															__UpperCamelCase	:   List[str]			   =					getattr(a     ,   hparams.task_type						)
															__UpperCamelCase	:   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__()}'						)
								__UpperCamelCase	:   str			   =					self.token_classification_task.get_labels(hparams.labels						)
								__UpperCamelCase	:   Tuple			   =					CrossEntropyLoss().ignore_index
								super().__init__(a     ,   len(self.labels						)     ,   self.mode						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   **a		:int						)		-> Optional[Any]:
								return self.model(**a						)







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

								__UpperCamelCase	:   List[str]			   =					self(**a						)
								__UpperCamelCase	:   int			   =					outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}







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







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







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







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Dict						)		-> int:
								__UpperCamelCase	:   Optional[int]			   =					torch.stack([x["val_loss"] for x in outputs]						).mean()
								__UpperCamelCase	:   Tuple			   =					np.concatenate([x["pred"] for x in outputs]     ,   axis=0						)
								__UpperCamelCase	:   Tuple			   =					np.argmax(a     ,   axis=2						)
								__UpperCamelCase	:   Dict			   =					np.concatenate([x["target"] for x in outputs]     ,   axis=0						)

								__UpperCamelCase	:   Dict			   =					dict(enumerate(self.labels						)						)
								__UpperCamelCase	:   int			   =					[[] for _ in range(out_label_ids.shape[0]						)]
								__UpperCamelCase	:   Dict			   =					[[] 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]]						)

								__UpperCamelCase	:   Optional[Any]			   =					{
								    "val_loss": val_loss_mean,
								    "accuracy_score": accuracy_score(a     ,   a						),
								    "precision": precision_score(a     ,   a						),
								    "recall": recall_score(a     ,   a						),
								    "f1": fa_score(a     ,   a						),
								}

								__UpperCamelCase	:   int			   =					dict(results.items()						)
								__UpperCamelCase	:   List[str]			   =					results
								return ret, preds_list, out_label_list







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[int]						)		-> Any:
								# when stable
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Any			   =					self._eval_end(a						)
								__UpperCamelCase	:   Optional[Any]			   =					ret["log"]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Tuple						)		-> Union[str, Any]:
								# updating to test_epoch_end instead of deprecated test_end
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self._eval_end(a						)

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								__UpperCamelCase	:   str			   =					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				(					a		:int     ,   a		:Union[str, Any]						)		-> Optional[int]:
								# Add NER specific options
								BaseTransformer.add_model_specific_args(a     ,   a						)
								parser.add_argument(
								    "--task_type"     ,   default="NER"     ,   type=a     ,   help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
								parser.add_argument(
								    "--max_seq_length"     ,   default=1_2_8     ,   type=a     ,   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=a     ,   help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."     ,   )
								parser.add_argument(
								    "--gpus"     ,   default=0     ,   type=a     ,   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__":
		lowercase  :					int													=       argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		lowercase  :					List[str]													=       NERTransformer.add_model_specific_args(parser, os.getcwd())
		lowercase  :					List[str]													=       parser.parse_args()
		lowercase  :					Union[str, Any]													=       NERTransformer(args)
		lowercase  :					int													=       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
				lowercase  :					Dict													=       sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
				lowercase  :					Any													=       model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)

import math




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					input("Enter message: ")
							__UpperCamelCase	:   Optional[int]			   =					int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
							__UpperCamelCase	:   str			   =					input("Encryption/Decryption [e/d]: ")

							if mode.lower().startswith("e"):
														__UpperCamelCase	:   List[str]			   =					encrypt_message(_lowerCamelCase						,  _lowerCamelCase)
							elif mode.lower().startswith("d"):
														__UpperCamelCase	:   Dict			   =					decrypt_message(_lowerCamelCase						,  _lowerCamelCase)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}')




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[""] * key
							for col in range(_lowerCamelCase):
														__UpperCamelCase	:   Any			   =					col
														while pointer < len(_lowerCamelCase):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(_lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					math.ceil(len(_lowerCamelCase) / key)
							__UpperCamelCase	:   Any			   =					key
							__UpperCamelCase	:   str			   =					(num_cols * num_rows) - len(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					[""] * num_cols
							__UpperCamelCase	:   Dict			   =					0
							__UpperCamelCase	:   int			   =					0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__UpperCamelCase	:   List[Any]			   =					0
																					row += 1

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()