






import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCAmelCase__							  =						logging.getLogger(__name__)

UpperCAmelCase__							  =						tf.data.AUTOTUNE
def 		_a       ( )				->     str:
 a						    = 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=512      ,				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.'''					)

 a						    = parser.parse_args()
 return args
def 		_a       ( a					:int					)				->     Optional[Any]:





 try:
  if args.tpu_name:
   a						    = tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name      ,				zone=args.tpu_zone      ,				project=args.gcp_project					)
  else:
   a						    = 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       ( a					:Optional[int]					)				->     List[Any]:
 a						    = 0
 for file in file_list:
  a						    = file.split('''/'''					)[-1]
  a						    = re.search(r'''-\d+-(\d+)\.tfrecord'''      ,				_A					).group(1					)
  a						    = int(_A					)
  num_samples += sample_count

 return num_samples
def 		_a       ( a					:int      ,				a					:Dict      ,				a					:Any      ,				a					:Dict      ,				a					:Optional[Any]      ,				a					:int=None					)				->     str:
 a						    = count_samples(_A					)
 a						    = tf.data.Dataset.from_tensor_slices(_A					)
 if shuffle:
  a						    = dataset.shuffle(len(_A					)					)
 a						    = 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
 a						    = dataset.apply(tf.data.experimental.assert_cardinality(_A					)					)
 a						    = dataset.map(_A      ,				num_parallel_calls=_A					)
 if shuffle:
  assert shuffle_buffer_size is not None
  a						    = dataset.shuffle(args.shuffle_buffer_size					)
 a						    = dataset.batch(_A      ,				drop_remainder=_A					)
 a						    = dataset.map(_A      ,				num_parallel_calls=_A					)
 a						    = dataset.prefetch(_A					)
 return dataset
def 		_a       ( a					:Any					)				->     List[Any]:





 if not args.no_tpu:
  a						    = initialize_tpu(_A					)
  a						    = tf.distribute.TPUStrategy(_A					)
 else:
  a						    = tf.distribute.OneDeviceStrategy(device='''/gpu:0'''					)

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

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

 a						    = 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						    = 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						    = count_samples(_A					)

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

 with strategy.scope():
  a						    = TFAutoModelForMaskedLM.from_config(_A					)
  model(model.dummy_inputs					)  # Pass some dummy inputs through the model to ensure all the weights are built
  a    ,	a						    = 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(a					:List[str]					):
  a						    = {
      '''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.
 a						    = DataCollatorForLanguageModeling(
     tokenizer=_A      ,				mlm_probability=args.mlm_probability      ,				mlm=_A      ,				return_tensors='''tf'''					)

 def mask_with_collator(a					:Tuple					):
  # TF really needs an isin() function
  a						    = (
      ~tf.cast(batch['''attention_mask''']      ,				tf.bool					)
      | (batch['''input_ids'''] == tokenizer.cls_token_id)
      | (batch['''input_ids'''] == tokenizer.sep_token_id)
  )
  a    ,	a						    = 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

 a						    = args.per_replica_batch_size * strategy.num_replicas_in_sync

 a						    = prepare_dataset(
     _A      ,				decode_fn=_A      ,				mask_fn=_A      ,				batch_size=_A      ,				shuffle=_A      ,				shuffle_buffer_size=args.shuffle_buffer_size      ,				)

 a						    = prepare_dataset(
     _A      ,				decode_fn=_A      ,				mask_fn=_A      ,				batch_size=_A      ,				shuffle=_A      ,				)

 a						    = []
 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__":
     UpperCAmelCase__							  =						parse_args()
     main(args)








def 		lowerCamelCase__       (						):







	'''simple docstring'''





	for n in range(1	,      1000000				):
		yield n * (n + 1) // 2


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	1
	snake_case_						       =	2
	while i * i <= n:
		snake_case_						       =	0
		while n % i == 0:
			n //= i
			multiplicity += 1
		divisors_count *= multiplicity + 1
		i += 1
	if n > 1:
		divisors_count *= 2
	return divisors_count


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	return next(i for i in triangle_number_generator() if count_divisors(_A				) > 500				)


if __name__ == "__main__":
		print(solution())
