



'''simple docstring'''




import random



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_,   A_,   A_     :						Optional[Any]     =					[], [], []
			for element in data:
						if element < pivot:
									less.append(snake_case_   )
						elif element > pivot:
									greater.append(snake_case_   )
						else:
									equal.append(snake_case_   )
			return less, equal, greater



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if index >= len(snake_case_   ) or index < 0:
						return None

			A_     :						int     =					items[random.randint(0						,				len(snake_case_   ) - 1   )]
			A_     :						str     =					0
			A_,   A_,   A_     :						List[Any]     =					_partition(snake_case_						,				snake_case_   )
			A_     :						int     =					len(snake_case_   )
			A_     :						Any     =					len(snake_case_   )

			# index is the pivot
			if m <= index < m + count:
						return pivot
			# must be in smaller
			elif m > index:
						return quick_select(snake_case_						,				snake_case_   )
			# must be in larger
			else:
						return quick_select(snake_case_						,				index - (m + count)   )




'''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      (			):
			'''simple docstring'''





			A_     :						Any     =					argparse.ArgumentParser(description="""Train a masked language model on TPU."""   )
			parser.add_argument(
			    """--pretrained_model_config"""						,				type=lowerCamelCase__						,				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=lowerCamelCase__						,				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=lowerCamelCase__						,				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=lowerCamelCase__						,				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=lowerCamelCase__						,				help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""						,				)

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

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

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

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

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

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

			A_     :						Dict     =					parser.parse_args()
			return args



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			try:
						if args.tpu_name:
									A_     :						Optional[Any]     =					tf.distribute.cluster_resolver.TPUClusterResolver(
									    args.tpu_name						,				zone=args.tpu_zone						,				project=args.gcp_project   )
						else:
									A_     :						Union[str, Any]     =					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(lowerCamelCase__   )
			tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__   )

			return tpu



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[str]     =					0
			for file in file_list:
						A_     :						Optional[Any]     =					file.split("""/"""   )[-1]
						A_     :						Optional[int]     =					re.search(r"""-\d+-(\d+)\.tfrecord"""						,				lowerCamelCase__   ).group(1   )
						A_     :						Any     =					int(lowerCamelCase__   )
						num_samples += sample_count

			return num_samples



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None   ):
			'''simple docstring'''





			A_     :						List[Any]     =					count_samples(lowerCamelCase__   )
			A_     :						List[str]     =					tf.data.Dataset.from_tensor_slices(lowerCamelCase__   )
			if shuffle:
						A_     :						Optional[int]     =					dataset.shuffle(len(lowerCamelCase__   )   )
			A_     :						Tuple     =					tf.data.TFRecordDataset(lowerCamelCase__						,				num_parallel_reads=lowerCamelCase__   )
			# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
			A_     :						Union[str, Any]     =					dataset.apply(tf.data.experimental.assert_cardinality(lowerCamelCase__   )   )
			A_     :						Any     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			if shuffle:
						assert shuffle_buffer_size is not None
						A_     :						List[str]     =					dataset.shuffle(args.shuffle_buffer_size   )
			A_     :						str     =					dataset.batch(lowerCamelCase__						,				drop_remainder=lowerCamelCase__   )
			A_     :						str     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			A_     :						Optional[int]     =					dataset.prefetch(lowerCamelCase__   )
			return dataset



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if not args.no_tpu:
						A_     :						Dict     =					initialize_tpu(lowerCamelCase__   )
						A_     :						str     =					tf.distribute.TPUStrategy(lowerCamelCase__   )
			else:
						A_     :						Union[str, Any]     =					tf.distribute.OneDeviceStrategy(device="""/gpu:0"""   )

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

			A_     :						Optional[Any]     =					AutoTokenizer.from_pretrained(args.tokenizer   )
			A_     :						str     =					AutoConfig.from_pretrained(args.pretrained_model_config   )
			A_     :						Optional[int]     =					tokenizer.vocab_size

			A_     :						Dict     =					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}.'   )
			A_     :						Optional[int]     =					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}.'   )

			A_     :						Dict     =					count_samples(lowerCamelCase__   )

			A_     :						Optional[Any]     =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
			A_     :						Dict     =					steps_per_epoch * args.num_epochs

			with strategy.scope():
						A_     :						Optional[int]     =					TFAutoModelForMaskedLM.from_config(lowerCamelCase__   )
						model(model.dummy_inputs   )  # Pass some dummy inputs through the model to ensure all the weights are built
						A_,   A_     :						Any     =					create_optimizer(
						    num_train_steps=lowerCamelCase__						,				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=lowerCamelCase__						,				metrics=["""accuracy"""]   )

			def decode_fn(lowerCamelCase__   ):
						A_     :						Optional[int]     =					{
						    """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(lowerCamelCase__						,				lowerCamelCase__   )

			# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
			# use their methods in our data pipeline.
			A_     :						Optional[int]     =					DataCollatorForLanguageModeling(
			    tokenizer=lowerCamelCase__						,				mlm_probability=args.mlm_probability						,				mlm=lowerCamelCase__						,				return_tensors="""tf"""   )

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

			A_     :						str     =					args.per_replica_batch_size * strategy.num_replicas_in_sync

			A_     :						Tuple     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				shuffle_buffer_size=args.shuffle_buffer_size						,				)

			A_     :						Optional[Any]     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				)

			A_     :						Union[str, Any]     =					[]
			if args.hub_model_id:
						callbacks.append(
						    PushToHubCallback(output_dir=args.output_dir						,				hub_model_id=args.hub_model_id						,				tokenizer=lowerCamelCase__   )   )

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

			model.save_pretrained(args.output_dir   )


if __name__ == "__main__":
						lowerCamelCase							:Tuple       = parse_args()
						main(args)