
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()


snake_case_      :					Any				      =     logging.getLogger(__name__)





@dataclass
class 		__lowerCamelCase     :
							lowerCamelCase__:				List[Any]      =							field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
							lowerCamelCase__:				Dict      =							field(
							    default=lowercase_							, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
							lowerCamelCase__:				str      =							field(
							    default='''NER'''							, metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}		)
							lowerCamelCase__:				Optional[Any]      =							field(
							    default=lowercase_							, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
							lowerCamelCase__:				List[Any]      =							field(default=lowercase_							, 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.
							lowerCamelCase__:				Union[str, Any]      =							field(
							    default=lowercase_							, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							, )





@dataclass
class 		__lowerCamelCase     :
							lowerCamelCase__:				Optional[Any]      =							field(
							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
							lowerCamelCase__:				Optional[Any]      =							field(
							    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
							lowerCamelCase__:				Union[str, Any]      =							field(
							    default=128							, metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }							, )
							lowerCamelCase__:				Dict      =							field(
							    default=lowercase_							, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
	UpperCAmelCase:					List[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:					List[str] 						=     import_module("tasks"				)

	try:
		UpperCAmelCase:					Optional[int] 						=     getattr(lowerCAmelCase__				,					model_args.task_type				)
		UpperCAmelCase:					TokenClassificationTask 						=     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"				,					lowerCAmelCase__				)

	# Prepare Token Classification task
	UpperCAmelCase:					Tuple 						=     token_classification_task.get_labels(data_args.labels				)
	UpperCAmelCase:					Dict[int, str] 						=     dict(enumerate(lowerCAmelCase__				)				)
	UpperCAmelCase:					Optional[Any] 						=     len(lowerCAmelCase__				)

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

	UpperCAmelCase:					Dict 						=     AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=lowerCAmelCase__				,					idalabel=lowerCAmelCase__				,					labelaid={label: i for i, label in enumerate(lowerCAmelCase__				)}				,					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:					Union[str, Any] 						=     TFAutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path				,					from_pt=bool(".bin" in model_args.model_name_or_path				)				,					config=lowerCAmelCase__				,					cache_dir=model_args.cache_dir				,					)

	# Get datasets
	UpperCAmelCase:					Optional[int] 						=     (
	    TFTokenClassificationDataset(
	        token_classification_task=lowerCAmelCase__				,					data_dir=data_args.data_dir				,					tokenizer=lowerCAmelCase__				,					labels=lowerCAmelCase__				,					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:					List[str] 						=     (
	    TFTokenClassificationDataset(
	        token_classification_task=lowerCAmelCase__				,					data_dir=data_args.data_dir				,					tokenizer=lowerCAmelCase__				,					labels=lowerCAmelCase__				,					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(snake_case_							:		np.ndarray				,					snake_case_							:		np.ndarray				) -> Tuple[List[int], List[int]]:
		UpperCAmelCase:					str 						=     np.argmax(lowerCAmelCase__				,					axis=2				)
		UpperCAmelCase:					List[Any] 						=     preds.shape
		UpperCAmelCase:					Tuple 						=     [[] for _ in range(lowerCAmelCase__				)]
		UpperCAmelCase:					Union[str, Any] 						=     [[] for _ in range(lowerCAmelCase__				)]

		for i in range(lowerCAmelCase__				):
			for j in range(lowerCAmelCase__				):
				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(snake_case_							:		EvalPrediction				) -> Dict:
		UpperCAmelCase:					Any 						=     align_predictions(p.predictions				,					p.label_ids				)

		return {
		    "precision": precision_score(lowerCAmelCase__				,					lowerCAmelCase__				),
		    "recall": recall_score(lowerCAmelCase__				,					lowerCAmelCase__				),
		    "f1": fa_score(lowerCAmelCase__				,					lowerCAmelCase__				),
		}

	# Initialize our Trainer
	UpperCAmelCase:					List[str] 						=     TFTrainer(
	    model=lowerCAmelCase__				,					args=lowerCAmelCase__				,					train_dataset=train_dataset.get_dataset() if train_dataset else None				,					eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,					compute_metrics=lowerCAmelCase__				,					)

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

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

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

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

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

			results.update(lowerCAmelCase__				)

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

		UpperCAmelCase:					Optional[Any] 						=     trainer.predict(test_dataset.get_dataset()				)
		UpperCAmelCase:					Any 						=     align_predictions(lowerCAmelCase__				,					lowerCAmelCase__				)
		UpperCAmelCase:					Any 						=     classification_report(lowerCAmelCase__				,					lowerCAmelCase__				)

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

		UpperCAmelCase:					Union[str, Any] 						=     os.path.join(training_args.output_dir				,					"test_results.txt"				)

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

		# Save predictions
		UpperCAmelCase:					Optional[Any] 						=     os.path.join(training_args.output_dir				,					"test_predictions.txt"				)

		with open(lowerCAmelCase__				,					"w"				) as writer:
			with open(os.path.join(data_args.data_dir				,					"test.txt"				)				,					"r"				) as f:
				UpperCAmelCase:					Optional[Any] 						=     0

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

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

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

	return results


if __name__ == "__main__":
	main()





"""simple docstring"""







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
     print("""#################### AVAILABLE DEVICES ####################""")
     print(jax.devices())
     print("""###########################################################""")

     # setup for wandb sweep
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
         """weight_decay""": args.weight_decay,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      Trainer(
         args=args,
         data_collator=data_collator,
         model_save_fn=model.save_pretrained,
         train_step_fn=train_step,
         val_step_fn=val_step,
         logger=logger,
         scheduler_fn=lr,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
     try:
          trainer.train(state, tr_dataset, val_dataset)
     except KeyboardInterrupt:
          print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

     print("""SAVING WEIGHTS IN `final-weights`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





