'''simple docstring'''




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 __lowerCAmelCase			(							)     ->   int:
    __lowerCamelCase          =  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=0.1_5    ,  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.'''    )

    __lowerCamelCase          =  parser.parse_args()
    return args






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

    return num_samples






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    )     ->   List[str]:
    __lowerCamelCase          =  count_samples(_UpperCAmelCase    )
    __lowerCamelCase          =  tf.data.Dataset.from_tensor_slices(_UpperCAmelCase    )
    if shuffle:
        __lowerCamelCase          =  dataset.shuffle(len(_UpperCAmelCase    )    )
    __lowerCamelCase          =  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
    __lowerCamelCase          =  dataset.apply(tf.data.experimental.assert_cardinality(_UpperCAmelCase    )    )
    __lowerCamelCase          =  dataset.map(_UpperCAmelCase    ,  num_parallel_calls=_UpperCAmelCase    )
    if shuffle:
        assert shuffle_buffer_size is not None
        __lowerCamelCase          =  dataset.shuffle(args.shuffle_buffer_size    )
    __lowerCamelCase          =  dataset.batch(_UpperCAmelCase    ,  drop_remainder=_UpperCAmelCase    )
    __lowerCamelCase          =  dataset.map(_UpperCAmelCase    ,  num_parallel_calls=_UpperCAmelCase    )
    __lowerCamelCase          =  dataset.prefetch(_UpperCAmelCase    )
    return dataset






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    if not args.no_tpu:
        __lowerCamelCase          =  initialize_tpu(_UpperCAmelCase    )
        __lowerCamelCase          =  tf.distribute.TPUStrategy(_UpperCAmelCase    )
    else:
        __lowerCamelCase          =  tf.distribute.OneDeviceStrategy(device='''/gpu:0'''    )

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

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

    __lowerCamelCase          =  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}."""    )
    __lowerCamelCase          =  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}."""    )

    __lowerCamelCase          =  count_samples(_UpperCAmelCase    )

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

    with strategy.scope():
        __lowerCamelCase          =  TFAutoModelForMaskedLM.from_config(_UpperCAmelCase    )
        model(model.dummy_inputs    )  # Pass some dummy inputs through the model to ensure all the weights are built
        __lowerCamelCase          =  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__    ):
        __lowerCamelCase          =  {
            '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.
    __lowerCamelCase          =  DataCollatorForLanguageModeling(
        tokenizer=_UpperCAmelCase    ,  mlm_probability=args.mlm_probability    ,  mlm=_UpperCAmelCase    ,  return_tensors='''tf'''    )

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

    __lowerCamelCase          =  args.per_replica_batch_size * strategy.num_replicas_in_sync

    __lowerCamelCase          =  prepare_dataset(
        _UpperCAmelCase    ,  decode_fn=_UpperCAmelCase    ,  mask_fn=_UpperCAmelCase    ,  batch_size=_UpperCAmelCase    ,  shuffle=_UpperCAmelCase    ,  shuffle_buffer_size=args.shuffle_buffer_size    ,  )

    __lowerCamelCase          =  prepare_dataset(
        _UpperCAmelCase    ,  decode_fn=_UpperCAmelCase    ,  mask_fn=_UpperCAmelCase    ,  batch_size=_UpperCAmelCase    ,  shuffle=_UpperCAmelCase    ,  )

    __lowerCamelCase          =  []
    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__":
  __UpperCAmelCase												=parse_args()
  main(args)

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    return "".join(chr(ord(UpperCamelCase__    ) - 32    ) if '''a''' <= char <= '''z''' else char for char in word    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()
