
'''simple docstring'''
import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
__lowerCAmelCase  							= '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
__lowerCAmelCase  							= '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
__lowerCAmelCase  							= ''

# TODO: Add the licence for the dataset here if you can find it
__lowerCAmelCase  							= ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
__lowerCAmelCase  							= {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}


class     _lowerCAmelCase					(					datasets.GeneratorBasedBuilder   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		datasets.Version("1.1.0"   )

							# This is an example of a dataset with multiple configurations.
							# If you don't want/need to define several sub-sets in your dataset,
							# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

							# If you need to make complex sub-parts in the datasets with configurable options
							# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
							# BUILDER_CONFIG_CLASS = MyBuilderConfig

							# You will be able to load one or the other configurations in the following list with
							# data = datasets.load_dataset('my_dataset', 'first_domain')
							# data = datasets.load_dataset('my_dataset', 'second_domain')
							lowerCAmelCase_      		=		[
							    datasets.BuilderConfig(name="first_domain"  ,							version=__snake_case  ,							description="This part of my dataset covers a first domain"   ),
							    datasets.BuilderConfig(name="second_domain"  ,							version=__snake_case  ,							description="This part of my dataset covers a second domain"   ),
							]

							lowerCAmelCase_      		=		"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.

							def  lowercase							(self						)  ->     int:
														# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
														if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
																					_snake_case     =						datasets.Features(
																					    {
																					        """sentence""": datasets.Value("""string"""						),
																					        """option1""": datasets.Value("""string"""						),
																					        """answer""": datasets.Value("""string"""						)
																					        # These are the features of your dataset like images, labels ...
																					    }						)
														else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
																					_snake_case     =						datasets.Features(
																					    {
																					        """sentence""": datasets.Value("""string"""						),
																					        """option2""": datasets.Value("""string"""						),
																					        """second_domain_answer""": datasets.Value("""string"""						)
																					        # These are the features of your dataset like images, labels ...
																					    }						)
														return datasets.DatasetInfo(
														    # This is the description that will appear on the datasets page.
														    description=_DESCRIPTION						,						features=UpperCAmelCase						,						homepage=_HOMEPAGE						,						license=_LICENSE						,						citation=_CITATION						,						)

							def  lowercase							(self						,						UpperCAmelCase						)  ->     List[str]:
														# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
														# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

														# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
														# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
														# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
														_snake_case     =						_URLS[self.config.name]
														_snake_case     =						dl_manager.download_and_extract(UpperCAmelCase						)
														return [
														    datasets.SplitGenerator(
														        name=datasets.Split.TRAIN						,						gen_kwargs={
														            """filepath""": os.path.join(UpperCAmelCase						,						"""train.jsonl"""						),
														            """split""": """train""",
														        }						,						),
														    datasets.SplitGenerator(
														        name=datasets.Split.VALIDATION						,						gen_kwargs={
														            """filepath""": os.path.join(UpperCAmelCase						,						"""dev.jsonl"""						),
														            """split""": """dev""",
														        }						,						),
														    datasets.SplitGenerator(
														        name=datasets.Split.TEST						,						gen_kwargs={
														            """filepath""": os.path.join(UpperCAmelCase						,						"""test.jsonl"""						),
														            """split""": """test"""
														        }						,						),
														]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Any:
														# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
														# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
														with open(UpperCAmelCase						,						encoding="""utf-8"""						) as f:
																					for key, row in enumerate(UpperCAmelCase						):
																												_snake_case     =						json.loads(UpperCAmelCase						)
																												if self.config.name == "first_domain":
																																			# Yields examples as (key, example) tuples
																																			yield key, {
																																			    "sentence": data["sentence"],
																																			    "option1": data["option1"],
																																			    "answer": "" if split == "test" else data["answer"],
																																			}
																												else:
																																			yield key, {
																																			    "sentence": data["sentence"],
																																			    "option2": data["option2"],
																																			    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
																																			}

'''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)