

import argparse
import logging
import os
import re

import tensorflow as tf

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


a_    :						Optional[int]    		=      logging.getLogger(__name__)

a_    :						Any    		=      tf.data.AUTOTUNE


def        __lowerCAmelCase    (       )		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			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=0.15		,							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.'	)

 SCREAMING_SNAKE_CASE								=			parser.parse_args()
 return args


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							int:
 '''simple docstring'''


 try:
  if args.tpu_name:
   SCREAMING_SNAKE_CASE								=			tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name		,							zone=args.tpu_zone		,							project=args.gcp_project	)
  else:
   SCREAMING_SNAKE_CASE								=			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	)		->							List[Any]:
 '''simple docstring'''


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

 return num_samples


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  str=None	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			count_samples(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			tf.data.Dataset.from_tensor_slices(_UpperCamelCase	)
 if shuffle:
  SCREAMING_SNAKE_CASE								=			dataset.shuffle(len(_UpperCamelCase	)	)
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase	)	)
 SCREAMING_SNAKE_CASE								=			dataset.map(_UpperCamelCase		,							num_parallel_calls=_UpperCamelCase	)
 if shuffle:
  assert shuffle_buffer_size is not None
  SCREAMING_SNAKE_CASE								=			dataset.shuffle(args.shuffle_buffer_size	)
 SCREAMING_SNAKE_CASE								=			dataset.batch(_UpperCamelCase		,							drop_remainder=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			dataset.map(_UpperCamelCase		,							num_parallel_calls=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			dataset.prefetch(_UpperCamelCase	)
 return dataset


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]	)		->							int:
 '''simple docstring'''


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

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

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

 SCREAMING_SNAKE_CASE								=			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								=			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								=			count_samples(_UpperCamelCase	)

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

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

 def mask_with_collator(_UpperCamelCase		:  Any	):
  # TF really needs an isin() function
  SCREAMING_SNAKE_CASE								=			(
      ~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								=			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

 SCREAMING_SNAKE_CASE								=			args.per_replica_batch_size * strategy.num_replicas_in_sync

 SCREAMING_SNAKE_CASE								=			prepare_dataset(
     _UpperCamelCase		,							decode_fn=_UpperCamelCase		,							mask_fn=_UpperCamelCase		,							batch_size=_UpperCamelCase		,							shuffle=_UpperCamelCase		,							shuffle_buffer_size=args.shuffle_buffer_size		,							)

 SCREAMING_SNAKE_CASE								=			prepare_dataset(
     _UpperCamelCase		,							decode_fn=_UpperCamelCase		,							mask_fn=_UpperCamelCase		,							batch_size=_UpperCamelCase		,							shuffle=_UpperCamelCase		,							)

 SCREAMING_SNAKE_CASE								=			[]
 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__":
      a_    :						Union[str, Any]    		=      parse_args()
      main(args)









def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


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

      testmod()

