



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

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

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

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

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

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

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

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

						__lowercase					=				parser.parse_args()
						return args






def  UpperCAmelCase__    (    lowercase__   ) ->      List[str]:
						try:
												if args.tpu_name:
																		__lowercase					=				tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name						,       zone=args.tpu_zone						,       project=args.gcp_project   )
												else:
																		__lowercase					=				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(lowercase__   )
						tf.tpu.experimental.initialize_tpu_system(lowercase__   )

						return tpu






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

						return num_samples






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=None   ) ->      Dict:
						__lowercase					=				count_samples(lowercase__   )
						__lowercase					=				tf.data.Dataset.from_tensor_slices(lowercase__   )
						if shuffle:
												__lowercase					=				dataset.shuffle(len(lowercase__   )   )
						__lowercase					=				tf.data.TFRecordDataset(lowercase__						,       num_parallel_reads=lowercase__   )
						# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
						__lowercase					=				dataset.apply(tf.data.experimental.assert_cardinality(lowercase__   )   )
						__lowercase					=				dataset.map(lowercase__						,       num_parallel_calls=lowercase__   )
						if shuffle:
												assert shuffle_buffer_size is not None
												__lowercase					=				dataset.shuffle(args.shuffle_buffer_size   )
						__lowercase					=				dataset.batch(lowercase__						,       drop_remainder=lowercase__   )
						__lowercase					=				dataset.map(lowercase__						,       num_parallel_calls=lowercase__   )
						__lowercase					=				dataset.prefetch(lowercase__   )
						return dataset






def  UpperCAmelCase__    (    lowercase__   ) ->      List[Any]:
						if not args.no_tpu:
												__lowercase					=				initialize_tpu(lowercase__   )
												__lowercase					=				tf.distribute.TPUStrategy(lowercase__   )
						else:
												__lowercase					=				tf.distribute.OneDeviceStrategy(device="""/gpu:0"""   )

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

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

						__lowercase					=				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}."   )
						__lowercase					=				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}."   )

						__lowercase					=				count_samples(lowercase__   )

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

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

						def decode_fn(lowercase__   ):
												__lowercase					=				{
												    """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(lowercase__						,       lowercase__   )

						# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
						# use their methods in our data pipeline.
						__lowercase					=				DataCollatorForLanguageModeling(
						    tokenizer=lowercase__						,       mlm_probability=args.mlm_probability						,       mlm=lowercase__						,       return_tensors="""tf"""   )

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

						__lowercase					=				args.per_replica_batch_size * strategy.num_replicas_in_sync

						__lowercase					=				prepare_dataset(
						    lowercase__						,       decode_fn=lowercase__						,       mask_fn=lowercase__						,       batch_size=lowercase__						,       shuffle=lowercase__						,       shuffle_buffer_size=args.shuffle_buffer_size						,       )

						__lowercase					=				prepare_dataset(
						    lowercase__						,       decode_fn=lowercase__						,       mask_fn=lowercase__						,       batch_size=lowercase__						,       shuffle=lowercase__						,       )

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

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

						model.save_pretrained(args.output_dir   )


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










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





