
"""simple docstring"""





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


UpperCamelCase_					:						List[Any]				= logging.getLogger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"token-classification"




			def __init__(				self						:					List[Any]       ,   _snake_case						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								if type(_snake_case	) == dict:
													A_				 =						Namespace(**_snake_case	)
								A_				 =						import_module("tasks"	)
								try:
													A_				 =						getattr(_snake_case       ,   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__(_snake_case       ,   len(self.labels	)       ,   self.mode	)




			def      lowerCamelCase__     (				self						:					str       ,   **_snake_case						:					Optional[Any]	)      -> List[str]:







								"""simple docstring"""
								return self.model(**_snake_case	)




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Optional[int]	)      -> Tuple:







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




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						self.hparams
								for mode in ["train", "dev", "test"]:
													A_				 =						self._feature_file(_snake_case	)
													if os.path.exists(_snake_case	) and not args.overwrite_cache:
																		logger.info("Loading features from cached file %s"       ,   _snake_case	)
																		A_				 =						torch.load(_snake_case	)
													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       ,   _snake_case	)
																		A_				 =						self.token_classification_task.convert_examples_to_features(
																		    _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=_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"       ,   _snake_case	)
																		torch.save(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					int       ,   _snake_case						:					int       ,   _snake_case						:					bool = False	)      -> DataLoader:







								"""simple docstring"""
								A_				 =						self._feature_file(_snake_case	)
								logger.info("Loading features from cached file %s"       ,   _snake_case	)
								A_				 =						torch.load(_snake_case	)
								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(_snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)       ,   batch_size=_snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					int       ,   _snake_case						:					str	)      -> int:







								"""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(**_snake_case	)
								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      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					Tuple	)      -> Optional[int]:







								"""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(_snake_case       ,   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(_snake_case       ,   _snake_case	),
								    "precision": precision_score(_snake_case       ,   _snake_case	),
								    "recall": recall_score(_snake_case       ,   _snake_case	),
								    "f1": fa_score(_snake_case       ,   _snake_case	),
								}

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




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								A_ ,    A_ ,    A_				 =						self._eval_end(_snake_case	)
								A_				 =						ret["log"]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int	)      -> Any:







								"""simple docstring"""
								A_ ,    A_ ,    A_				 =						self._eval_end(_snake_case	)

								# 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      lowerCamelCase__     (				_snake_case						:					int       ,   _snake_case						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								BaseTransformer.add_model_specific_args(_snake_case       ,   _snake_case	)
								parser.add_argument(
								    "--task_type"       ,   default="NER"       ,   type=_snake_case       ,   help="Task type to fine tune in training (e.g. NER, POS, etc)"	)
								parser.add_argument(
								    "--max_seq_length"       ,   default=128       ,   type=_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=_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=_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__":
		UpperCamelCase_					:						List[Any]				= argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		UpperCamelCase_					:						Tuple				= NERTransformer.add_model_specific_args(parser, os.getcwd())
		UpperCamelCase_					:						int				= parser.parse_args()
		UpperCamelCase_					:						str				= NERTransformer(args)
		UpperCamelCase_					:						Tuple				= 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
				UpperCamelCase_					:						Dict				= sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
				UpperCamelCase_					:						Union[str, Any]				= model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_					:						Tuple				= logging.get_logger(__name__)

UpperCamelCase_					:						Optional[int]				= {
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"markuplm"




			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					Dict=30_522       ,   _snake_case						:					str=768       ,   _snake_case						:					Tuple=12       ,   _snake_case						:					Union[str, Any]=12       ,   _snake_case						:					Union[str, Any]=3_072       ,   _snake_case						:					List[str]="gelu"       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					Dict=512       ,   _snake_case						:					Union[str, Any]=2       ,   _snake_case						:					Tuple=0.0_2       ,   _snake_case						:					Tuple=1e-12       ,   _snake_case						:					Tuple=0       ,   _snake_case						:					Tuple=0       ,   _snake_case						:					Optional[Any]=2       ,   _snake_case						:					int=256       ,   _snake_case						:					Dict=1_024       ,   _snake_case						:					List[str]=216       ,   _snake_case						:					Optional[int]=1_001       ,   _snake_case						:					Union[str, Any]=32       ,   _snake_case						:					List[str]=50       ,   _snake_case						:					str="absolute"       ,   _snake_case						:					Optional[Any]=True       ,   _snake_case						:					str=None       ,   **_snake_case						:					Optional[Any]       ,   )      -> Optional[Any]:







								"""simple docstring"""
								super().__init__(
								    pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case       ,   **_snake_case       ,   )
								A_				 =						vocab_size
								A_				 =						hidden_size
								A_				 =						num_hidden_layers
								A_				 =						num_attention_heads
								A_				 =						hidden_act
								A_				 =						intermediate_size
								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						max_position_embeddings
								A_				 =						type_vocab_size
								A_				 =						initializer_range
								A_				 =						layer_norm_eps
								A_				 =						position_embedding_type
								A_				 =						use_cache
								A_				 =						classifier_dropout
								# additional properties
								A_				 =						max_depth
								A_				 =						max_xpath_tag_unit_embeddings
								A_				 =						max_xpath_subs_unit_embeddings
								A_				 =						tag_pad_id
								A_				 =						subs_pad_id
								A_				 =						xpath_unit_hidden_size




