


'''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				:str        =      logging.getLogger(__name__)

lowerCAmelCase				:Union[str, Any]        =      tf.data.AUTOTUNE





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Union[str, 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=512		,		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.'		)

	__magic_name__	:   str					     =					parser.parse_args()
	return args





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	try:
		if args.tpu_name:
			__magic_name__	:   Optional[int]					     =					tf.distribute.cluster_resolver.TPUClusterResolver(
			    args.tpu_name		,		zone=args.tpu_zone		,		project=args.gcp_project		)
		else:
			__magic_name__	:   Optional[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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					0
	for file in file_list:
		__magic_name__	:   Tuple					     =					file.split('/'		)[-1]
		__magic_name__	:   List[str]					     =					re.search(R'-\d+-(\d+)\.tfrecord'		,		lowerCAmelCase		).group(1		)
		__magic_name__	:   str					     =					int(lowerCAmelCase		)
		num_samples += sample_count

	return num_samples





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Optional[int]=None		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					count_samples(lowerCAmelCase		)
	__magic_name__	:   int					     =					tf.data.Dataset.from_tensor_slices(lowerCAmelCase		)
	if shuffle:
		__magic_name__	:   Optional[int]					     =					dataset.shuffle(len(lowerCAmelCase		)		)
	__magic_name__	:   List[Any]					     =					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
	__magic_name__	:   int					     =					dataset.apply(tf.data.experimental.assert_cardinality(lowerCAmelCase		)		)
	__magic_name__	:   Dict					     =					dataset.map(lowerCAmelCase		,		num_parallel_calls=lowerCAmelCase		)
	if shuffle:
		assert shuffle_buffer_size is not None
		__magic_name__	:   Optional[Any]					     =					dataset.shuffle(args.shuffle_buffer_size		)
	__magic_name__	:   Union[str, Any]					     =					dataset.batch(lowerCAmelCase		,		drop_remainder=lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					dataset.map(lowerCAmelCase		,		num_parallel_calls=lowerCAmelCase		)
	__magic_name__	:   Any					     =					dataset.prefetch(lowerCAmelCase		)
	return dataset





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	if not args.no_tpu:
		__magic_name__	:   str					     =					initialize_tpu(lowerCAmelCase		)
		__magic_name__	:   Optional[Any]					     =					tf.distribute.TPUStrategy(lowerCAmelCase		)
	else:
		__magic_name__	:   Any					     =					tf.distribute.OneDeviceStrategy(device='/gpu:0'		)

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

	__magic_name__	:   Union[str, Any]					     =					AutoTokenizer.from_pretrained(args.tokenizer		)
	__magic_name__	:   Tuple					     =					AutoConfig.from_pretrained(args.pretrained_model_config		)
	__magic_name__	:   Tuple					     =					tokenizer.vocab_size

	__magic_name__	:   Union[str, Any]					     =					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}.'		)
	__magic_name__	:   Union[str, Any]					     =					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}.'		)

	__magic_name__	:   Union[str, Any]					     =					count_samples(lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
	__magic_name__	:   int					     =					steps_per_epoch * args.num_epochs

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

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

	__magic_name__	:   Any					     =					args.per_replica_batch_size * strategy.num_replicas_in_sync

	__magic_name__	:   Union[str, Any]					     =					prepare_dataset(
	    lowerCAmelCase		,		decode_fn=lowerCAmelCase		,		mask_fn=lowerCAmelCase		,		batch_size=lowerCAmelCase		,		shuffle=lowerCAmelCase		,		shuffle_buffer_size=args.shuffle_buffer_size		,		)

	__magic_name__	:   Optional[Any]					     =					prepare_dataset(
	    lowerCAmelCase		,		decode_fn=lowerCAmelCase		,		mask_fn=lowerCAmelCase		,		batch_size=lowerCAmelCase		,		shuffle=lowerCAmelCase		,		)

	__magic_name__	:   int					     =					[]
	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)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	if not all(x.isalpha() for x in string		):
		raise ValueError('String must only contain alphabetic characters.'		)

	__magic_name__	:   int					     =					sorted(string.lower()		)
	return len(lowerCAmelCase		) == len(set(lowerCAmelCase		)		)


if __name__ == "__main__":
							lowerCAmelCase				:Any        =      input('''Enter a string ''').strip()

							lowerCAmelCase				:List[Any]        =      is_isogram(input_str)
							print(F'{input_str} is {"an" if isogram else "not an"} isogram.')