


import argparse
import logging
import os
import re

import tensorflow as tf

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


_lowercase       :      Dict	=	logging.getLogger(__name__)

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





def   _lowerCAmelCase				(   ) ->   List[Any]:





	"""simple docstring"""







	A								=				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=5_12							,					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."""						)

	A								=				parser.parse_args()
	return args





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   str:





	"""simple docstring"""







	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(UpperCamelCase__						)
	tf.tpu.experimental.initialize_tpu_system(UpperCamelCase__						)

	return tpu





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   str:





	"""simple docstring"""







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

	return num_samples





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     int=None						) ->   Tuple:





	"""simple docstring"""







	A								=				count_samples(UpperCamelCase__						)
	A								=				tf.data.Dataset.from_tensor_slices(UpperCamelCase__						)
	if shuffle:
		A								=				dataset.shuffle(len(UpperCamelCase__						)						)
	A								=				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
	A								=				dataset.apply(tf.data.experimental.assert_cardinality(UpperCamelCase__						)						)
	A								=				dataset.map(UpperCamelCase__							,					num_parallel_calls=UpperCamelCase__						)
	if shuffle:
		assert shuffle_buffer_size is not None
		A								=				dataset.shuffle(args.shuffle_buffer_size						)
	A								=				dataset.batch(UpperCamelCase__							,					drop_remainder=UpperCamelCase__						)
	A								=				dataset.map(UpperCamelCase__							,					num_parallel_calls=UpperCamelCase__						)
	A								=				dataset.prefetch(UpperCamelCase__						)
	return dataset





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Optional[int]:





	"""simple docstring"""







	if not args.no_tpu:
		A								=				initialize_tpu(UpperCamelCase__						)
		A								=				tf.distribute.TPUStrategy(UpperCamelCase__						)
	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(UpperCamelCase__						)

	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(UpperCamelCase__						)
		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=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__:     Optional[Any]						):
		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(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.
	A								=				DataCollatorForLanguageModeling(
	    tokenizer=UpperCamelCase__							,					mlm_probability=args.mlm_probability							,					mlm=UpperCamelCase__							,					return_tensors="""tf"""						)

	def mask_with_collator(UpperCamelCase__:     List[str]						):
		# 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(UpperCamelCase__						)							,					mask_token_id=tokenizer.mask_token_id							,					special_tokens_mask=UpperCamelCase__							,					)
		return batch

	A								=				args.per_replica_batch_size * strategy.num_replicas_in_sync

	A								=				prepare_dataset(
	    UpperCamelCase__							,					decode_fn=UpperCamelCase__							,					mask_fn=UpperCamelCase__							,					batch_size=UpperCamelCase__							,					shuffle=UpperCamelCase__							,					shuffle_buffer_size=args.shuffle_buffer_size							,					)

	A								=				prepare_dataset(
	    UpperCamelCase__							,					decode_fn=UpperCamelCase__							,					mask_fn=UpperCamelCase__							,					batch_size=UpperCamelCase__							,					shuffle=UpperCamelCase__							,					)

	A								=				[]
	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__":
				_lowercase       :      str	=	parse_args()
				main(args)







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	return str(UpperCamelCase__						) == str(UpperCamelCase__						)[::-1]





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return int(UpperCamelCase__						) + int(str(UpperCamelCase__						)[::-1]						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 1_00_00						) ->   int:





	"""simple docstring"""







	A								=				[]
	for num in range(1							,					UpperCamelCase__						):
		A								=				0
		A								=				num
		while iterations < 50:
			A								=				sum_reverse(UpperCamelCase__						)
			iterations += 1
			if is_palindrome(UpperCamelCase__						):
				break
		else:
			lychrel_nums.append(UpperCamelCase__						)
	return len(UpperCamelCase__						)


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



