

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__		      =       logging.getLogger(__name__)

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'token-classification'


			def __init__(			self					,					lowercase				)     ->  List[str]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  Any:
							'''simple docstring'''






							return self.model(**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  int:
							'''simple docstring'''






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

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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






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






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






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

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

							A__						=	{
							    "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				),
							}

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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__			, A__			, A__						=	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
							A__						=	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						(			lowercase					,					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=128					,					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__		      =       argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	lowerCAmelCase__		      =       NERTransformer.add_model_specific_args(parser, os.getcwd())
	lowerCAmelCase__		      =       parser.parse_args()
	lowerCAmelCase__		      =       NERTransformer(args)
	lowerCAmelCase__		      =       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__		      =       sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
		lowerCAmelCase__		      =       model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)








def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return True
  a__	:					Union[str, Any]										=series[1] - series[0]
  for index in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  a__	:					Any										=0
  for val in series:
    answer += val
  return answer / len(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






