



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						(				):



 """simple docstring"""





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

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

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

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

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

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

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

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

 __SCREAMING_SNAKE_CASE       :    List[str]       =	parser.parse_args()
 return args






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 try:
  if args.tpu_name:
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name		,	zone=args.tpu_zone		,	project=args.gcp_project     )
  else:
   __SCREAMING_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(__lowerCamelCase     )
 tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase     )

 return tpu






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0
 for file in file_list:
  __SCREAMING_SNAKE_CASE       :    int       =	file.split("/"     )[-1]
  __SCREAMING_SNAKE_CASE       :    Dict       =	re.search(r"-\d+-(\d+)\.tfrecord"		,	__lowerCamelCase     ).group(1     )
  __SCREAMING_SNAKE_CASE       :    str       =	int(__lowerCamelCase     )
  num_samples += sample_count

 return num_samples






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int=None     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





 if not args.no_tpu:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	initialize_tpu(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	tf.distribute.TPUStrategy(__lowerCamelCase     )
 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       :    Optional[int]       =	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       :    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}."""     )

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	count_samples(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	steps_per_epoch * args.num_epochs

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

 def decode_fn(__lowerCamelCase   :     Union[str, Any]     ):
  __SCREAMING_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(__lowerCamelCase		,	__lowerCamelCase     )

 # 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       :    Tuple       =	DataCollatorForLanguageModeling(
     tokenizer=__lowerCamelCase		,	mlm_probability=args.mlm_probability		,	mlm=__lowerCamelCase		,	return_tensors="tf"     )

 def mask_with_collator(__lowerCamelCase   :     Optional[Any]     ):
  # TF really needs an isin() function
  __SCREAMING_SNAKE_CASE       :    Dict       =	(
      ~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       :    Any       =	data_collator.tf_mask_tokens(
      batch["input_ids"]		,	vocab_size=len(__lowerCamelCase     )		,	mask_token_id=tokenizer.mask_token_id		,	special_tokens_mask=__lowerCamelCase		,	)
  return batch

 __SCREAMING_SNAKE_CASE       :    Any       =	args.per_replica_batch_size * strategy.num_replicas_in_sync

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_dataset(
     __lowerCamelCase		,	decode_fn=__lowerCamelCase		,	mask_fn=__lowerCamelCase		,	batch_size=__lowerCamelCase		,	shuffle=__lowerCamelCase		,	shuffle_buffer_size=args.shuffle_buffer_size		,	)

 __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_dataset(
     __lowerCamelCase		,	decode_fn=__lowerCamelCase		,	mask_fn=__lowerCamelCase		,	batch_size=__lowerCamelCase		,	shuffle=__lowerCamelCase		,	)

 __SCREAMING_SNAKE_CASE       :    str       =	[]
 if args.hub_model_id:
  callbacks.append(
      PushToHubCallback(output_dir=args.output_dir		,	hub_model_id=args.hub_model_id		,	tokenizer=__lowerCamelCase     )     )

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

 model.save_pretrained(args.output_dir     )


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











def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if height >= 1:
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
  move_disk(__lowerCamelCase		,	__lowerCamelCase     )
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 print("moving disk from"		,	__lowerCamelCase		,	"to"		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	int(input("Height of hanoi: "     ).strip()     )
 move_tower(__lowerCamelCase		,	"A"		,	"B"		,	"C"     )


if __name__ == "__main__":
      main()






