
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCAmelCase  							= logging.getLogger(__name__)


@dataclass
class     _lowerCAmelCase					:







							'''simple docstring'''

							lowerCAmelCase_      		=		field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
							lowerCAmelCase_      		=		field(
							    default=_SCREAMING_SNAKE_CASE  ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
							lowerCAmelCase_      		=		field(
							    default=_SCREAMING_SNAKE_CASE  ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
							lowerCAmelCase_      		=		field(
							    default=_SCREAMING_SNAKE_CASE  ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,							)


@dataclass
class     _lowerCAmelCase					:







							'''simple docstring'''

							lowerCAmelCase_      		=		field(
							    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()   )}   )
							lowerCAmelCase_      		=		field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
							lowerCAmelCase_      		=		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."
							        )
							    }  ,							)
							lowerCAmelCase_      		=		field(
							    default=_SCREAMING_SNAKE_CASE  ,							metadata={"help": "Overwrite the cached training and evaluation sets"}   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						default_data_collator(_SCREAMING_SNAKE_CASE   )
							_snake_case     =						batch.pop("""pairID"""							,      _SCREAMING_SNAKE_CASE   )
							return batch






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
							_snake_case,							_snake_case,							_snake_case     =						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."""   )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,      )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,      training_args.local_rank							,      training_args.device							,      training_args.n_gpu							,      bool(training_args.local_rank != -1   )							,      training_args.fpaa							,      )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank   ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""							,      _SCREAMING_SNAKE_CASE   )

							# Set seed
							set_seed(training_args.seed   )

							try:
														_snake_case     =						hans_tasks_num_labels[data_args.task_name]
							except KeyError:
														raise ValueError("""Task not found: %s""" % (data_args.task_name)   )

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

							_snake_case     =						AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=_SCREAMING_SNAKE_CASE							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
							_snake_case     =						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							,      )
							_snake_case     =						AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )							,      config=_SCREAMING_SNAKE_CASE							,      cache_dir=model_args.cache_dir							,      )

							# Get datasets
							_snake_case     =						(
							    HansDataset(
							        data_dir=data_args.data_dir							,      tokenizer=_SCREAMING_SNAKE_CASE							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      )
							    if training_args.do_train
							    else None
							)
							_snake_case     =						(
							    HansDataset(
							        data_dir=data_args.data_dir							,      tokenizer=_SCREAMING_SNAKE_CASE							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      evaluate=_SCREAMING_SNAKE_CASE							,      )
							    if training_args.do_eval
							    else None
							)

							# Initialize our Trainer
							_snake_case     =						Trainer(
							    model=_SCREAMING_SNAKE_CASE							,      args=_SCREAMING_SNAKE_CASE							,      train_dataset=_SCREAMING_SNAKE_CASE							,      eval_dataset=_SCREAMING_SNAKE_CASE							,      data_collator=_SCREAMING_SNAKE_CASE							,      )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""   )

														_snake_case     =						trainer.predict(_SCREAMING_SNAKE_CASE   )
														_snake_case     =						output.predictions
														_snake_case     =						np.argmax(_SCREAMING_SNAKE_CASE							,      axis=1   )

														_snake_case     =						[ex.pairID for ex in eval_dataset]
														_snake_case     =						os.path.join(training_args.output_dir							,      """hans_predictions.txt"""   )
														_snake_case     =						eval_dataset.get_labels()
														if trainer.is_world_master():
																					with open(_SCREAMING_SNAKE_CASE							,      """w"""   ) as writer:
																												writer.write("""pairID,gold_label\n"""   )
																												for pid, pred in zip(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
																																			writer.write("""ex""" + str(_SCREAMING_SNAKE_CASE   ) + """,""" + label_list[int(_SCREAMING_SNAKE_CASE   )] + """\n"""   )

														trainer._log(output.metrics   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							main()


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
				__lowerCAmelCase  							= Args()
				__lowerCAmelCase  							= wandb.init(project='bigbird-natural-questions', config=args.__dict__)
				__lowerCAmelCase  							= dict(logger.config)
				del wandb_args["batch_size"]
				__lowerCAmelCase  							= replace(args, **wandb_args)
				__lowerCAmelCase  							= args.base_dir + '-' + wandb.run.id
				__lowerCAmelCase  							= replace(args, base_dir=base_dir)
				print(args)

				__lowerCAmelCase  							= load_dataset('json', data_files=args.tr_data_path)['train']
				__lowerCAmelCase  							= load_dataset('json', data_files=args.val_data_path)['train']

				# drop extra batch for now
				__lowerCAmelCase  							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				__lowerCAmelCase  							= tr_dataset.shuffle().select(indices)
				__lowerCAmelCase  							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
				__lowerCAmelCase  							= val_dataset.shuffle().select(indices)

				if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
								__lowerCAmelCase  							= tr_dataset.shuffle().select(range(80_000))
								__lowerCAmelCase  							= val_dataset.shuffle().select(range(8_000))

				print(tr_dataset)
				print(val_dataset)

				__lowerCAmelCase  							= FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				__lowerCAmelCase  							= BigBirdTokenizerFast.from_pretrained(args.model_id)
				__lowerCAmelCase  							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

				__lowerCAmelCase  							= {
				    '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,
				}
				__lowerCAmelCase							,       __lowerCAmelCase  							= build_tx(**tx_args)

				__lowerCAmelCase  							= 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,
				)

				__lowerCAmelCase  							= None
				__lowerCAmelCase  							= 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`')
				__lowerCAmelCase  							= jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)