

'''simple docstring'''

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_  ( ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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=512      ,       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." )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					parser.parse_args()
  return args





def    A_  ( snake_case ):
  try:
    if args.tpu_name:
      SCREAMING_SNAKE_CASE:List[str]						      =					tf.distribute.cluster_resolver.TPUClusterResolver(
          args.tpu_name      ,       zone=args.tpu_zone      ,       project=args.gcp_project )
    else:
      SCREAMING_SNAKE_CASE:int						      =					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 ):
  SCREAMING_SNAKE_CASE:Dict						      =					0
  for file in file_list:
    SCREAMING_SNAKE_CASE:Optional[int]						      =					file.split("/" )[-1]
    SCREAMING_SNAKE_CASE:List[str]						      =					re.search(r"-\d+-(\d+)\.tfrecord"      ,       snake_case ).group(1 )
    SCREAMING_SNAKE_CASE:Dict						      =					int(snake_case )
    num_samples += sample_count

  return num_samples





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





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

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

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					AutoTokenizer.from_pretrained(args.tokenizer )
  SCREAMING_SNAKE_CASE:List[str]						      =					AutoConfig.from_pretrained(args.pretrained_model_config )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer.vocab_size

  SCREAMING_SNAKE_CASE:Dict						      =					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}.''' )
  SCREAMING_SNAKE_CASE: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}.''' )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					count_samples(snake_case )

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

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

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

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

  SCREAMING_SNAKE_CASE:int						      =					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      ,       )

  SCREAMING_SNAKE_CASE:Tuple						      =					prepare_dataset(
      snake_case      ,       decode_fn=snake_case      ,       mask_fn=snake_case      ,       batch_size=snake_case      ,       shuffle=snake_case      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
  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)









'''simple docstring'''

def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					(1 + 24 * n) ** 0.5
  return ((1 + root) / 6) % 1 == 0





def    A_  ( snake_case = 5000 ):
  SCREAMING_SNAKE_CASE:int						      =					[(i * (3 * i - 1)) // 2 for i in range(1      ,       snake_case )]
  for i, pentagonal_i in enumerate(snake_case ):
    for j in range(snake_case      ,       len(snake_case ) ):
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_nums[j]
      SCREAMING_SNAKE_CASE:Any						      =					pentagonal_i + pentagonal_j
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_j - pentagonal_i
      if is_pentagonal(snake_case ) and is_pentagonal(snake_case ):
        return b

  return -1


if __name__ == "__main__":
    print(f'''{solution() = }''')






