





import argparse
import logging
import os
import re

import tensorflow as tf

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


lowerCAmelCase_    =    logging.getLogger(__name__)

lowerCAmelCase_    =    tf.data.AUTOTUNE






def 					lowerCamelCase_						(    )		->						Optional[int]:






    """simple docstring"""


    snake_case_						:   Optional[Any]           =  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=512			,    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.'''			)

    snake_case_						:   str           =  parser.parse_args()
    return args






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






    """simple docstring"""


    try:
        if args.tpu_name:
            snake_case_						:   Dict           =  tf.distribute.cluster_resolver.TPUClusterResolver(
                args.tpu_name			,    zone=args.tpu_zone			,    project=args.gcp_project			)
        else:
            snake_case_						:   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(_UpperCamelCase			)
    tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase			)

    return tpu






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






    """simple docstring"""


    snake_case_						:   Union[str, Any]           =  0
    for file in file_list:
        snake_case_						:   str           =  file.split('''/'''			)[-1]
        snake_case_						:   List[Any]           =  re.search(R'''-\d+-(\d+)\.tfrecord'''			,    _UpperCamelCase			).group(1			)
        snake_case_						:   Dict           =  int(_UpperCamelCase			)
        num_samples += sample_count

    return num_samples






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=None			)		->						Union[str, Any]:






    """simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






    """simple docstring"""


    if not args.no_tpu:
        snake_case_						:   Any           =  initialize_tpu(_UpperCamelCase			)
        snake_case_						:   Any           =  tf.distribute.TPUStrategy(_UpperCamelCase			)
    else:
        snake_case_						:   str           =  tf.distribute.OneDeviceStrategy(device='''/gpu:0'''			)

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

    snake_case_						:   List[Any]           =  AutoTokenizer.from_pretrained(args.tokenizer			)
    snake_case_						:   List[Any]           =  AutoConfig.from_pretrained(args.pretrained_model_config			)
    snake_case_						:   int           =  tokenizer.vocab_size

    snake_case_						:   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}.'''			)
    snake_case_						:   List[str]           =  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}.'''			)

    snake_case_						:   int           =  count_samples(_UpperCamelCase			)

    snake_case_						:   Union[str, Any]           =  num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
    snake_case_						:   int           =  steps_per_epoch * args.num_epochs

    with strategy.scope():
        snake_case_						:   Tuple           =  TFAutoModelForMaskedLM.from_config(_UpperCamelCase			)
        model(model.dummy_inputs			)  # Pass some dummy inputs through the model to ensure all the weights are built
        snake_case_						:   int           =  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			):
        snake_case_						:   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(_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.
    snake_case_						:   List[Any]           =  DataCollatorForLanguageModeling(
        tokenizer=_UpperCamelCase			,    mlm_probability=args.mlm_probability			,    mlm=_UpperCamelCase			,    return_tensors='''tf'''			)

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

    snake_case_						:   List[Any]           =  args.per_replica_batch_size * strategy.num_replicas_in_sync

    snake_case_						:   Tuple           =  prepare_dataset(
        _UpperCamelCase			,    decode_fn=_UpperCamelCase			,    mask_fn=_UpperCamelCase			,    batch_size=_UpperCamelCase			,    shuffle=_UpperCamelCase			,    shuffle_buffer_size=args.shuffle_buffer_size			,    )

    snake_case_						:   Union[str, Any]           =  prepare_dataset(
        _UpperCamelCase			,    decode_fn=_UpperCamelCase			,    mask_fn=_UpperCamelCase			,    batch_size=_UpperCamelCase			,    shuffle=_UpperCamelCase			,    )

    snake_case_						:   Any           =  []
    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__":
 lowerCAmelCase_    =    parse_args()
 main(args)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Any	      =				'''biogpt'''






			def __init__(self ,							__magic_name__=4_2384 ,							__magic_name__=1024 ,							__magic_name__=24 ,							__magic_name__=16 ,							__magic_name__=4096 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=1024 ,							__magic_name__=0.02 ,							__magic_name__=1e-12 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=2 ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   List[str]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   List[Any]           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Optional[int]           =  layer_norm_eps
							snake_case_						:   str           =  scale_embedding
							snake_case_						:   Optional[Any]           =  use_cache
							snake_case_						:   Optional[Any]           =  layerdrop
							snake_case_						:   Optional[Any]           =  activation_dropout
							super().__init__(pad_token_id=__magic_name__ ,							bos_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							**__magic_name__   )
