
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__lowerCamelCase								=					logging.getLogger(__name__)
@dataclass
class    _UpperCamelCase:
						__A:						str				   =      field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
						__A:						Optional[str]				   =      field(
						    default=lowerCAmelCase__		,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
						__A:						Optional[str]				   =      field(
						    default="""NER"""		,						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}				)
						__A:						Optional[str]				   =      field(
						    default=lowerCAmelCase__		,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
						__A:						bool				   =      field(default=lowerCAmelCase__		,						metadata={"""help""": """Set this flag to use fast tokenization."""}				)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						__A:						Optional[str]				   =      field(
						    default=lowerCAmelCase__		,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,						)







@dataclass
class    _UpperCamelCase:
						__A:						str				   =      field(
						    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
						__A:						Optional[str]				   =      field(
						    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				)
						__A:						int				   =      field(
						    default=1_28		,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }		,						)
						__A:						bool				   =      field(
						    default=lowerCAmelCase__		,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)







def 		lowerCAmelCase_     (     )   ->      int:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
							_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			str	       =		parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir  )
							    and os.listdir(training_args.output_dir  )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    " --overwrite_output_dir to overcome."  )

							_UpperCAmelCase  :			int	       =		import_module("tasks"  )

							try:
														_UpperCAmelCase  :			Dict	       =		getattr(_SCREAMING_SNAKE_CASE      ,	model_args.task_type  )
														_UpperCAmelCase  :			Union[str, Any]	       =		token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
														    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""  )

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      ,	datefmt="%m/%d/%Y %H:%M:%S"      ,	level=logging.INFO      ,	)
							logger.info(
							    "n_replicas: %s, distributed training: %s, 16-bits training: %s"      ,	training_args.n_replicas      ,	bool(training_args.n_replicas > 1  )      ,	training_args.fpaa      ,	)
							logger.info("Training/evaluation parameters %s"      ,	_SCREAMING_SNAKE_CASE  )

							# Prepare Token Classification task
							_UpperCAmelCase  :			Tuple	       =		token_classification_task.get_labels(data_args.labels  )
							_UpperCAmelCase  :			List[Any]	       =		dict(enumerate(_SCREAMING_SNAKE_CASE  )  )
							_UpperCAmelCase  :			str	       =		len(_SCREAMING_SNAKE_CASE  )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_UpperCAmelCase  :			Optional[Any]	       =		AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,	num_labels=_SCREAMING_SNAKE_CASE      ,	idalabel=_SCREAMING_SNAKE_CASE      ,	labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE  )}      ,	cache_dir=model_args.cache_dir      ,	)
							_UpperCAmelCase  :			int	       =		AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,	cache_dir=model_args.cache_dir      ,	use_fast=model_args.use_fast      ,	)

							with training_args.strategy.scope():
														_UpperCAmelCase  :			Any	       =		TFAutoModelForTokenClassification.from_pretrained(
														    model_args.model_name_or_path      ,	from_pt=bool(".bin" in model_args.model_name_or_path  )      ,	config=_SCREAMING_SNAKE_CASE      ,	cache_dir=model_args.cache_dir      ,	)

							# Get datasets
							_UpperCAmelCase  :			Any	       =		(
							    TFTokenClassificationDataset(
							        token_classification_task=_SCREAMING_SNAKE_CASE      ,	data_dir=data_args.data_dir      ,	tokenizer=_SCREAMING_SNAKE_CASE      ,	labels=_SCREAMING_SNAKE_CASE      ,	model_type=config.model_type      ,	max_seq_length=data_args.max_seq_length      ,	overwrite_cache=data_args.overwrite_cache      ,	mode=Split.train      ,	)
							    if training_args.do_train
							    else None
							)
							_UpperCAmelCase  :			Optional[Any]	       =		(
							    TFTokenClassificationDataset(
							        token_classification_task=_SCREAMING_SNAKE_CASE      ,	data_dir=data_args.data_dir      ,	tokenizer=_SCREAMING_SNAKE_CASE      ,	labels=_SCREAMING_SNAKE_CASE      ,	model_type=config.model_type      ,	max_seq_length=data_args.max_seq_length      ,	overwrite_cache=data_args.overwrite_cache      ,	mode=Split.dev      ,	)
							    if training_args.do_eval
							    else None
							)

							def align_predictions(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ) -> Tuple[List[int], List[int]]:
														_UpperCAmelCase  :			int	       =		np.argmax(_SCREAMING_SNAKE_CASE      ,	axis=2  )
														_UpperCAmelCase       ,_UpperCAmelCase  :			Dict	       =		preds.shape
														_UpperCAmelCase  :			Tuple	       =		[[] for _ in range(_SCREAMING_SNAKE_CASE  )]
														_UpperCAmelCase  :			Union[str, Any]	       =		[[] for _ in range(_SCREAMING_SNAKE_CASE  )]

														for i in range(_SCREAMING_SNAKE_CASE  ):
																					for j in range(_SCREAMING_SNAKE_CASE  ):
																												if label_ids[i, j] != -1_0_0:
																																			out_label_list[i].append(label_map[label_ids[i][j]]  )
																																			preds_list[i].append(label_map[preds[i][j]]  )

														return preds_list, out_label_list

							def compute_metrics(_SCREAMING_SNAKE_CASE  ) -> Dict:
														_UpperCAmelCase       ,_UpperCAmelCase  :			Dict	       =		align_predictions(p.predictions      ,	p.label_ids  )

														return {
														    "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  ),
														}

							# Initialize our Trainer
							_UpperCAmelCase  :			int	       =		TFTrainer(
							    model=_SCREAMING_SNAKE_CASE      ,	args=_SCREAMING_SNAKE_CASE      ,	train_dataset=train_dataset.get_dataset() if train_dataset else None      ,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,	compute_metrics=_SCREAMING_SNAKE_CASE      ,	)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir  )

							# Evaluation
							_UpperCAmelCase  :			Dict	       =		{}
							if training_args.do_eval:
														logger.info("*** Evaluate ***"  )

														_UpperCAmelCase  :			Optional[int]	       =		trainer.evaluate()
														_UpperCAmelCase  :			Optional[Any]	       =		os.path.join(training_args.output_dir      ,	"eval_results.txt"  )

														with open(_SCREAMING_SNAKE_CASE      ,	"w"  ) as writer:
																					logger.info("***** Eval results *****"  )

																					for key, value in result.items():
																												logger.info("  %s = %s"      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )
																												writer.write("%s = %s\n" % (key, value)  )

																					results.update(_SCREAMING_SNAKE_CASE  )

    # Predict
							if training_args.do_predict:
														_UpperCAmelCase  :			Optional[Any]	       =		TFTokenClassificationDataset(
														    token_classification_task=_SCREAMING_SNAKE_CASE      ,	data_dir=data_args.data_dir      ,	tokenizer=_SCREAMING_SNAKE_CASE      ,	labels=_SCREAMING_SNAKE_CASE      ,	model_type=config.model_type      ,	max_seq_length=data_args.max_seq_length      ,	overwrite_cache=data_args.overwrite_cache      ,	mode=Split.test      ,	)

														_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			str	       =		trainer.predict(test_dataset.get_dataset()  )
														_UpperCAmelCase       ,_UpperCAmelCase  :			Optional[Any]	       =		align_predictions(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )
														_UpperCAmelCase  :			Tuple	       =		classification_report(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )

														logger.info("\n%s"      ,	_SCREAMING_SNAKE_CASE  )

														_UpperCAmelCase  :			int	       =		os.path.join(training_args.output_dir      ,	"test_results.txt"  )

														with open(_SCREAMING_SNAKE_CASE      ,	"w"  ) as writer:
																					writer.write("%s\n" % report  )

														# Save predictions
														_UpperCAmelCase  :			List[str]	       =		os.path.join(training_args.output_dir      ,	"test_predictions.txt"  )

														with open(_SCREAMING_SNAKE_CASE      ,	"w"  ) as writer:
																					with open(os.path.join(data_args.data_dir      ,	"test.txt"  )      ,	"r"  ) as f:
																												_UpperCAmelCase  :			Union[str, Any]	       =		0

																												for line in f:
																																			if line.startswith("-DOCSTART-"  ) or line == "" or line == "\n":
																																										writer.write(_SCREAMING_SNAKE_CASE  )

																																										if not preds_list[example_id]:
																																																	example_id += 1
																																			elif preds_list[example_id]:
																																										_UpperCAmelCase  :			List[str]	       =		line.split()[0] + " " + preds_list[example_id].pop(0  ) + "\n"

																																										writer.write(_SCREAMING_SNAKE_CASE  )
																																			else:
																																										logger.warning("Maximum sequence length exceeded: No prediction for \'%s\'."      ,	line.split()[0]  )

							return results


if __name__ == "__main__":
				main()






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





