'''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


a=	logging.getLogger(__name__)






@dataclass
class       __lowercase		:







     """simple docstring"""






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






@dataclass
class       __lowercase		:







     """simple docstring"""






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







def 			_UpperCamelCase			(  _a						:						Tuple      ):




    """simple docstring"""



    __UpperCamelCase						:						str		     =	default_data_collator(lowercase_      )
    __UpperCamelCase						:						str		     =	batch.pop('pairID'					,  lowercase_      )
    return batch







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	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'					,  lowercase_      )

    # Set seed
    set_seed(training_args.seed      )

    try:
        __UpperCamelCase						:						int		     =	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.

    __UpperCamelCase						:						str		     =	AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,  num_labels=lowercase_					,  finetuning_task=data_args.task_name					,  cache_dir=model_args.cache_dir					,  )
    __UpperCamelCase						:						List[str]		     =	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					,  )
    __UpperCamelCase						:						List[str]		     =	AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,  config=lowercase_					,  cache_dir=model_args.cache_dir					,  )

    # Get datasets
    __UpperCamelCase						:						str		     =	(
        HansDataset(
            data_dir=data_args.data_dir					,  tokenizer=lowercase_					,  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
    )
    __UpperCamelCase						:						Optional[Any]		     =	(
        HansDataset(
            data_dir=data_args.data_dir					,  tokenizer=lowercase_					,  task=data_args.task_name					,  max_seq_length=data_args.max_seq_length					,  overwrite_cache=data_args.overwrite_cache					,  evaluate=lowercase_					,  )
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    __UpperCamelCase						:						int		     =	Trainer(
        model=lowercase_					,  args=lowercase_					,  train_dataset=lowercase_					,  eval_dataset=lowercase_					,  data_collator=lowercase_					,  )

    # 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 ***'      )

        __UpperCamelCase						:						Optional[int]		     =	trainer.predict(lowercase_      )
        __UpperCamelCase						:						Tuple		     =	output.predictions
        __UpperCamelCase						:						Dict		     =	np.argmax(lowercase_					,  axis=1      )

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

        trainer._log(output.metrics      )







def 			_UpperCamelCase			(  _a						:						Dict      ):




    """simple docstring"""



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

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """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,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			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,
				)

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





