




"""simple docstring"""




import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCAmelCase							:			Optional[int]     							=							logging.getLogger(__name__)

lowerCAmelCase							:			Optional[int]     							=							tf.data.AUTOTUNE



def     a__ (				)						->					int:
			lowerCamelCase			      =					argparse.ArgumentParser(description="""Train a masked language model on TPU."""				)
			parser.add_argument(
			    """--pretrained_model_config"""       , type=__A       , default="""roberta-base"""       , help="""The model config to use. Note that we don\'t copy the model\'s weights, only the config!"""       , )
			parser.add_argument(
			    """--tokenizer"""       , type=__A       , default="""unigram-tokenizer-wikitext"""       , help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size."""       , )

			parser.add_argument(
			    """--per_replica_batch_size"""       , type=__A       , default=8       , help="""Batch size per TPU core."""       , )

			parser.add_argument(
			    """--no_tpu"""       , action="""store_true"""       , help="""If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances."""       , )

			parser.add_argument(
			    """--tpu_name"""       , type=__A       , help="""Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs."""       , default="""local"""       , )

			parser.add_argument(
			    """--tpu_zone"""       , type=__A       , help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""       , )

			parser.add_argument(
			    """--gcp_project"""       , type=__A       , help="""Google cloud project name. Only used for non-Colab TPU nodes."""				)

			parser.add_argument(
			    """--bfloat16"""       , action="""store_true"""       , help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."""       , )

			parser.add_argument(
			    """--train_dataset"""       , type=__A       , help="""Path to training dataset to load. If the path begins with `gs://`"""
			    """ then the dataset will be loaded from a Google Cloud Storage bucket."""       , )

			parser.add_argument(
			    """--shuffle_buffer_size"""       , type=__A       , default=2**18       , help="""Size of the shuffle buffer (in samples)"""       , )

			parser.add_argument(
			    """--eval_dataset"""       , type=__A       , help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
			    """ then the dataset will be loaded from a Google Cloud Storage bucket."""       , )

			parser.add_argument(
			    """--num_epochs"""       , type=__A       , default=1       , help="""Number of epochs to train for."""       , )

			parser.add_argument(
			    """--learning_rate"""       , type=__A       , default=1E-4       , help="""Learning rate to use for training."""       , )

			parser.add_argument(
			    """--weight_decay_rate"""       , type=__A       , default=1E-3       , help="""Weight decay rate to use for training."""       , )

			parser.add_argument(
			    """--max_length"""       , type=__A       , default=5_12       , help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""       , )

			parser.add_argument(
			    """--mlm_probability"""       , type=__A       , default=0.15       , help="""Fraction of tokens to mask during training."""       , )

			parser.add_argument("""--output_dir"""       , type=__A       , required=__A       , help="""Path to save model checkpoints to."""				)
			parser.add_argument("""--hub_model_id"""       , type=__A       , help="""Model ID to upload to on the Hugging Face Hub."""				)

			lowerCamelCase			      =					parser.parse_args()
			return args



def     a__ (				snake_case__				)						->					Dict:
			try:
						if args.tpu_name:
									lowerCamelCase			      =					tf.distribute.cluster_resolver.TPUClusterResolver(
									    args.tpu_name       , zone=args.tpu_zone       , project=args.gcp_project				)
						else:
									lowerCamelCase			      =					tf.distribute.cluster_resolver.TPUClusterResolver()
			except ValueError:
						raise RuntimeError(
						    """Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
						    """--gcp_project. When running on a TPU VM, use --tpu_name local."""				)

			tf.config.experimental_connect_to_cluster(__A				)
			tf.tpu.experimental.initialize_tpu_system(__A				)

			return tpu



def     a__ (				snake_case__				)						->					int:
			lowerCamelCase			      =					0
			for file in file_list:
						lowerCamelCase			      =					file.split("""/"""				)[-1]
						lowerCamelCase			      =					re.search(R"""-\d+-(\d+)\.tfrecord"""       , __A				).group(1				)
						lowerCamelCase			      =					int(__A				)
						num_samples += sample_count

			return num_samples



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__=None				)						->					int:
			lowerCamelCase			      =					count_samples(__A				)
			lowerCamelCase			      =					tf.data.Dataset.from_tensor_slices(__A				)
			if shuffle:
						lowerCamelCase			      =					dataset.shuffle(len(__A				)				)
			lowerCamelCase			      =					tf.data.TFRecordDataset(__A       , num_parallel_reads=__A				)
			# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
			lowerCamelCase			      =					dataset.apply(tf.data.experimental.assert_cardinality(__A				)				)
			lowerCamelCase			      =					dataset.map(__A       , num_parallel_calls=__A				)
			if shuffle:
						assert shuffle_buffer_size is not None
						lowerCamelCase			      =					dataset.shuffle(args.shuffle_buffer_size				)
			lowerCamelCase			      =					dataset.batch(__A       , drop_remainder=__A				)
			lowerCamelCase			      =					dataset.map(__A       , num_parallel_calls=__A				)
			lowerCamelCase			      =					dataset.prefetch(__A				)
			return dataset



def     a__ (				snake_case__				)						->					str:
			if not args.no_tpu:
						lowerCamelCase			      =					initialize_tpu(__A				)
						lowerCamelCase			      =					tf.distribute.TPUStrategy(__A				)
			else:
						lowerCamelCase			      =					tf.distribute.OneDeviceStrategy(device="""/gpu:0"""				)

			if args.bfloataa:
						tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""				)

			lowerCamelCase			      =					AutoTokenizer.from_pretrained(args.tokenizer				)
			lowerCamelCase			      =					AutoConfig.from_pretrained(args.pretrained_model_config				)
			lowerCamelCase			      =					tokenizer.vocab_size

			lowerCamelCase			      =					tf.io.gfile.glob(os.path.join(args.train_dataset       , """*.tfrecord"""				)				)
			if not training_records:
						raise ValueError(F'No .tfrecord files found in {args.train_dataset}.'				)
			lowerCamelCase			      =					tf.io.gfile.glob(os.path.join(args.eval_dataset       , """*.tfrecord"""				)				)
			if not eval_records:
						raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.'				)

			lowerCamelCase			      =					count_samples(__A				)

			lowerCamelCase			      =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
			lowerCamelCase			      =					steps_per_epoch * args.num_epochs

			with strategy.scope():
						lowerCamelCase			      =					TFAutoModelForMaskedLM.from_config(__A				)
						model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
						lowerCamelCase      ,   lowerCamelCase			      =					create_optimizer(
						    num_train_steps=__A       , num_warmup_steps=total_train_steps // 20       , init_lr=args.learning_rate       , weight_decay_rate=args.weight_decay_rate       , )

						# Transformers models compute the right loss for their task by default when labels are passed, and will
						# use this for training unless you specify your own loss function in compile().
						model.compile(optimizer=__A       , metrics=["""accuracy"""]				)

			def decode_fn(snake_case__				):
						lowerCamelCase			      =					{
						    """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa       , shape=(args.max_length,)				),
						    """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa       , shape=(args.max_length,)				),
						}
						return tf.io.parse_single_example(__A       , __A				)

			# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
			# use their methods in our data pipeline.
			lowerCamelCase			      =					DataCollatorForLanguageModeling(
			    tokenizer=__A       , mlm_probability=args.mlm_probability       , mlm=__A       , return_tensors="""tf"""				)

			def mask_with_collator(snake_case__				):
						# TF really needs an isin() function
						lowerCamelCase			      =					(
						    ~tf.cast(batch["""attention_mask"""]       , tf.bool				)
						    | (batch["""input_ids"""] == tokenizer.cls_token_id)
						    | (batch["""input_ids"""] == tokenizer.sep_token_id)
						)
						lowerCamelCase      ,   lowerCamelCase			      =					data_collator.tf_mask_tokens(
						    batch["""input_ids"""]       , vocab_size=len(__A				)       , mask_token_id=tokenizer.mask_token_id       , special_tokens_mask=__A       , )
						return batch

			lowerCamelCase			      =					args.per_replica_batch_size * strategy.num_replicas_in_sync

			lowerCamelCase			      =					prepare_dataset(
			    __A       , decode_fn=__A       , mask_fn=__A       , batch_size=__A       , shuffle=__A       , shuffle_buffer_size=args.shuffle_buffer_size       , )

			lowerCamelCase			      =					prepare_dataset(
			    __A       , decode_fn=__A       , mask_fn=__A       , batch_size=__A       , shuffle=__A       , )

			lowerCamelCase			      =					[]
			if args.hub_model_id:
						callbacks.append(
						    PushToHubCallback(output_dir=args.output_dir       , hub_model_id=args.hub_model_id       , tokenizer=__A				)				)

			model.fit(
			    __A       , validation_data=__A       , epochs=args.num_epochs       , callbacks=__A       , )

			model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
					lowerCAmelCase							:			Any     							=							parse_args()
					main(args)





'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 100	)       ->	int:
	_snake_case   						=		n * (n + 1) * (2 * n + 1) / 6
	_snake_case   						=		(n * (n + 1) / 2) ** 2
	return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

