


"""simple docstring"""


import argparse
import logging
import os
import re

import tensorflow as tf

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


A     :   Dict			    =			logging.getLogger(__name__)

A     :   int			    =			tf.data.AUTOTUNE


def _lowerCamelCase	(		):

			'''simple docstring'''




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

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

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

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

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

			parser.add_argument(
			    "--max_length"    , type=_UpperCamelCase    , default=512    , help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"    , )

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

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

			__lowerCAmelCase				    =			parser.parse_args()
			return args


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			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(_UpperCamelCase      )
			tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase      )

			return tpu


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for file in file_list:
						__lowerCAmelCase				    =			file.split("/"      )[-1]
						__lowerCAmelCase				    =			re.search(R"-\d+-(\d+)\.tfrecord"    , _UpperCamelCase      ).group(1      )
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
						num_samples += sample_count

			return num_samples


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			count_samples(_UpperCamelCase      )
			__lowerCAmelCase				    =			tf.data.Dataset.from_tensor_slices(_UpperCamelCase      )
			if shuffle:
						__lowerCAmelCase				    =			dataset.shuffle(len(_UpperCamelCase      )      )
			__lowerCAmelCase				    =			tf.data.TFRecordDataset(_UpperCamelCase    , num_parallel_reads=_UpperCamelCase      )
			# 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(_UpperCamelCase      )      )
			__lowerCAmelCase				    =			dataset.map(_UpperCamelCase    , num_parallel_calls=_UpperCamelCase      )
			if shuffle:
						assert shuffle_buffer_size is not None
						__lowerCAmelCase				    =			dataset.shuffle(args.shuffle_buffer_size      )
			__lowerCAmelCase				    =			dataset.batch(_UpperCamelCase    , drop_remainder=_UpperCamelCase      )
			__lowerCAmelCase				    =			dataset.map(_UpperCamelCase    , num_parallel_calls=_UpperCamelCase      )
			__lowerCAmelCase				    =			dataset.prefetch(_UpperCamelCase      )
			return dataset


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if not args.no_tpu:
						__lowerCAmelCase				    =			initialize_tpu(_UpperCamelCase      )
						__lowerCAmelCase				    =			tf.distribute.TPUStrategy(_UpperCamelCase      )
			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(_UpperCamelCase      )

			__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(_UpperCamelCase      )
						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=_UpperCamelCase    , 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=_UpperCamelCase    , metrics=["accuracy"]      )

			def decode_fn(_UpperCamelCase      ):
						__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(_UpperCamelCase    , _UpperCamelCase      )

			# 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=_UpperCamelCase    , mlm_probability=args.mlm_probability    , mlm=_UpperCamelCase    , return_tensors="tf"      )

			def mask_with_collator(_UpperCamelCase      ):
						# 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(_UpperCamelCase      )    , mask_token_id=tokenizer.mask_token_id    , special_tokens_mask=_UpperCamelCase    , )
						return batch

			__lowerCAmelCase				    =			args.per_replica_batch_size * strategy.num_replicas_in_sync

			__lowerCAmelCase				    =			prepare_dataset(
			    _UpperCamelCase    , decode_fn=_UpperCamelCase    , mask_fn=_UpperCamelCase    , batch_size=_UpperCamelCase    , shuffle=_UpperCamelCase    , shuffle_buffer_size=args.shuffle_buffer_size    , )

			__lowerCAmelCase				    =			prepare_dataset(
			    _UpperCamelCase    , decode_fn=_UpperCamelCase    , mask_fn=_UpperCamelCase    , batch_size=_UpperCamelCase    , shuffle=_UpperCamelCase    , )

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

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

			model.save_pretrained(args.output_dir      )


if __name__ == "__main__":
	A     :   List[Any]			    =			parse_args()
	main(args)








"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


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




