


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)









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You have to specify either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





