




import argparse
import logging
import os
import re

import tensorflow as tf

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


a__		     =     logging.getLogger(__name__)

a__		     =     tf.data.AUTOTUNE





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

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

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

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

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

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

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

					__UpperCamelCase					:	Any             =      parser.parse_args()
					return args





def 				A__ (snake_case   :		Optional[int]				)     ->						Union[str, Any]:
					try:
										if args.tpu_name:
															__UpperCamelCase					:	List[Any]             =      tf.distribute.cluster_resolver.TPUClusterResolver(
															    args.tpu_name		,	zone=args.tpu_zone		,	project=args.gcp_project				)
										else:
															__UpperCamelCase					:	List[str]             =      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(snake_case				)
					tf.tpu.experimental.initialize_tpu_system(snake_case				)

					return tpu





def 				A__ (snake_case   :		Any				)     ->						List[str]:
					__UpperCamelCase					:	Optional[Any]             =      0
					for file in file_list:
										__UpperCamelCase					:	Optional[Any]             =      file.split("""/"""				)[-1]
										__UpperCamelCase					:	Union[str, Any]             =      re.search(r"""-\d+-(\d+)\.tfrecord"""		,	snake_case				).group(1				)
										__UpperCamelCase					:	Tuple             =      int(snake_case				)
										num_samples += sample_count

					return num_samples





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





def 				A__ (snake_case   :		List[Any]				)     ->						int:
					if not args.no_tpu:
										__UpperCamelCase					:	Optional[Any]             =      initialize_tpu(snake_case				)
										__UpperCamelCase					:	Optional[Any]             =      tf.distribute.TPUStrategy(snake_case				)
					else:
										__UpperCamelCase					:	Any             =      tf.distribute.OneDeviceStrategy(device="""/gpu:0"""				)

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

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

					__UpperCamelCase					:	Optional[int]             =      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}.'''				)
					__UpperCamelCase					:	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}.'''				)

					__UpperCamelCase					:	List[str]             =      count_samples(snake_case				)

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

					with strategy.scope():
										__UpperCamelCase					:	Tuple             =      TFAutoModelForMaskedLM.from_config(snake_case				)
										model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
										__UpperCamelCase      ,			__UpperCamelCase					:	Union[str, Any]             =      create_optimizer(
										    num_train_steps=snake_case		,	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=snake_case		,	metrics=["""accuracy"""]				)

					def decode_fn(snake_case   :		Tuple				):
										__UpperCamelCase					:	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(snake_case		,	snake_case				)

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

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

					__UpperCamelCase					:	str             =      args.per_replica_batch_size * strategy.num_replicas_in_sync

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

					__UpperCamelCase					:	str             =      prepare_dataset(
					    snake_case		,	decode_fn=snake_case		,	mask_fn=snake_case		,	batch_size=snake_case		,	shuffle=snake_case		,	)

					__UpperCamelCase					:	Tuple             =      []
					if args.hub_model_id:
										callbacks.append(
										    PushToHubCallback(output_dir=args.output_dir		,	hub_model_id=args.hub_model_id		,	tokenizer=snake_case				)				)

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

					model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
	a__		     =     parse_args()
	main(args)












def 				A__ (snake_case   :		float		,	snake_case   :		int				)     ->						float:
					if digit_amount > 0:
										return round(number - int(snake_case				)		,	snake_case				)
					return number - int(snake_case				)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))






