


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__			:      Optional[Any]								=logging.getLogger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""token-classification"""






							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.model(**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
										if self.config.model_type != "distilbert":
													SCREAMING_SNAKE_CASE_		:     int					=					(
													    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
													)  # XLM and RoBERTa don"t use token_type_ids

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = False       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										"""Compute validation""" ""
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
										if self.config.model_type != "distilbert":
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
													    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
													)  # XLM and RoBERTa don"t use token_type_ids
										SCREAMING_SNAKE_CASE_		:     str					=					self(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					outputs[:2]
										SCREAMING_SNAKE_CASE_		:     Any					=					logits.detach().cpu().numpy()
										SCREAMING_SNAKE_CASE_		:     Dict					=					inputs['labels'].detach().cpu().numpy()
										return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.stack([x['val_loss'] for x in outputs]       ).mean()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.concatenate([x['pred'] for x in outputs]			,						axis=0       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					np.argmax(lowerCAmelCase__			,						axis=2       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					np.concatenate([x['target'] for x in outputs]			,						axis=0       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					dict(enumerate(self.labels       )       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[[] for _ in range(out_label_ids.shape[0]       )]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[[] 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_		:     Any					=					{
										    'val_loss': val_loss_mean,
										    'accuracy_score': accuracy_score(lowerCAmelCase__			,						lowerCAmelCase__       ),
										    'precision': precision_score(lowerCAmelCase__			,						lowerCAmelCase__       ),
										    'recall': recall_score(lowerCAmelCase__			,						lowerCAmelCase__       ),
										    'f1': fa_score(lowerCAmelCase__			,						lowerCAmelCase__       ),
										}

										SCREAMING_SNAKE_CASE_		:     str					=					dict(results.items()       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					results
										return ret, preds_list, out_label_list






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self._eval_end(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					ret['log']
										return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self._eval_end(lowerCAmelCase__       )

										# Converting to the dict required by pl
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
										# pytorch_lightning/trainer/logging.py#L139
										SCREAMING_SNAKE_CASE_		:     Any					=					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__     (      lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										BaseTransformer.add_model_specific_args(lowerCAmelCase__			,						lowerCAmelCase__       )
										parser.add_argument(
										    '--task_type'			,						default='NER'			,						type=lowerCAmelCase__			,						help='Task type to fine tune in training (e.g. NER, POS, etc)'       )
										parser.add_argument(
										    '--max_seq_length'			,						default=1_2_8			,						type=lowerCAmelCase__			,						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=lowerCAmelCase__			,						help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'			,						)
										parser.add_argument(
										    '--gpus'			,						default=0			,						type=lowerCAmelCase__			,						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__			:      Optional[Any]								=argparse.ArgumentParser()
				add_generic_args(parser, os.getcwd())
				lowerCAmelCase__			:      Tuple								=NERTransformer.add_model_specific_args(parser, os.getcwd())
				lowerCAmelCase__			:      Dict								=parser.parse_args()
				lowerCAmelCase__			:      Dict								=NERTransformer(args)
				lowerCAmelCase__			:      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
								lowerCAmelCase__			:      List[Any]								=sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
								lowerCAmelCase__			:      List[str]								=model.load_from_checkpoint(checkpoints[-1])
								trainer.test(model)













'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




