






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_						:  Any									=			logging.getLogger(__name__)

class 						_snake_case     (     lowercase_    ):
			_lowercase						:			List[str]	   =							'''token-classification'''






			def __init__(				self						,    a)					->				Any:
						if type(SCREAMING_SNAKE_CASE_) == dict:
									SCREAMING_SNAKE_CASE						 =       Namespace(**SCREAMING_SNAKE_CASE_)
						SCREAMING_SNAKE_CASE						 =       import_module('tasks')
						try:
									SCREAMING_SNAKE_CASE						 =       getattr(SCREAMING_SNAKE_CASE_						,    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__(SCREAMING_SNAKE_CASE_						,    len(self.labels)						,    self.mode)






			def       SCREAMING_SNAKE_CASE__			(				self						,    **a)					->				Optional[int]:
						return self.model(**SCREAMING_SNAKE_CASE_)






			def       SCREAMING_SNAKE_CASE__			(				self						,    a						,    a)					->				List[str]:
						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(**SCREAMING_SNAKE_CASE_)
						SCREAMING_SNAKE_CASE						 =       outputs[0]
						# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss}






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Union[str, Any]:
						SCREAMING_SNAKE_CASE						 =       self.hparams
						for mode in ["train", "dev", "test"]:
									SCREAMING_SNAKE_CASE						 =       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_)
												SCREAMING_SNAKE_CASE						 =       torch.load(SCREAMING_SNAKE_CASE_)
									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						,    SCREAMING_SNAKE_CASE_)
												SCREAMING_SNAKE_CASE						 =       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       SCREAMING_SNAKE_CASE__			(				self						,    a						,    a						,    a = False)					->				Optional[Any]:
						SCREAMING_SNAKE_CASE						 =       self._feature_file(SCREAMING_SNAKE_CASE_)
						logger.info('Loading features from cached file %s'						,    SCREAMING_SNAKE_CASE_)
						SCREAMING_SNAKE_CASE						 =       torch.load(SCREAMING_SNAKE_CASE_)
						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(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_)						,    batch_size=SCREAMING_SNAKE_CASE_)






			def       SCREAMING_SNAKE_CASE__			(				self						,    a						,    a)					->				List[Any]:
						"""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(**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       SCREAMING_SNAKE_CASE__			(				self						,    a)					->				Union[str, Any]:
						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(SCREAMING_SNAKE_CASE_						,    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(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_),
						}

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






			def       SCREAMING_SNAKE_CASE__			(				self						,    a)					->				Optional[int]:
						# when stable
						SCREAMING_SNAKE_CASE						 =       self._eval_end(SCREAMING_SNAKE_CASE_)
						SCREAMING_SNAKE_CASE						 =       ret['log']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






			def       SCREAMING_SNAKE_CASE__			(				self						,    a)					->				str:
						# updating to test_epoch_end instead of deprecated test_end
						SCREAMING_SNAKE_CASE						 =       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
						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       SCREAMING_SNAKE_CASE__			(				a						,    a)					->				Optional[Any]:
						# 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=128						,    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_						:  int									=			argparse.ArgumentParser()
				add_generic_args(parser, os.getcwd())
				a_						:  Tuple									=			NERTransformer.add_model_specific_args(parser, os.getcwd())
				a_						:  Tuple									=			parser.parse_args()
				a_						:  int									=			NERTransformer(args)
				a_						:  List[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
								a_						:  Tuple									=			sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
								a_						:  Optional[Any]									=			model.load_from_checkpoint(checkpoints[-1])
								trainer.test(model)









"""simple docstring"""







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)





