






'''simple docstring'''





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      snake_case		(							):
				A	 =      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.'   )

				A	 =      parser.parse_args()
				return args







def      snake_case		(							UpperCAmelCase	:       Tuple   ):
				try:
								if args.tpu_name:
												A	 =      tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name,						zone=args.tpu_zone,						project=args.gcp_project   )
								else:
												A	 =      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      snake_case		(							UpperCAmelCase	:       int   ):
				A	 =      0
				for file in file_list:
								A	 =      file.split('/'   )[-1]
								A	 =      re.search(r'-\d+-(\d+)\.tfrecord',						UpperCAmelCase__   ).group(1   )
								A	 =      int(UpperCAmelCase__   )
								num_samples += sample_count

				return num_samples







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







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				if not args.no_tpu:
								A	 =      initialize_tpu(UpperCAmelCase__   )
								A	 =      tf.distribute.TPUStrategy(UpperCAmelCase__   )
				else:
								A	 =      tf.distribute.OneDeviceStrategy(device='/gpu:0'   )

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

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

				A	 =      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}.'   )
				A	 =      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}.'   )

				A	 =      count_samples(UpperCAmelCase__   )

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

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

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

				A	 =      args.per_replica_batch_size * strategy.num_replicas_in_sync

				A	 =      prepare_dataset(
				    UpperCAmelCase__,						decode_fn=UpperCAmelCase__,						mask_fn=UpperCAmelCase__,						batch_size=UpperCAmelCase__,						shuffle=UpperCAmelCase__,						shuffle_buffer_size=args.shuffle_buffer_size,						)

				A	 =      prepare_dataset(
				    UpperCAmelCase__,						decode_fn=UpperCAmelCase__,						mask_fn=UpperCAmelCase__,						batch_size=UpperCAmelCase__,						shuffle=UpperCAmelCase__,						)

				A	 =      []
				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 collections.abc import Callable
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Callable | None = None		)    ->   None:


						'''simple docstring'''
						# Stores actual heap items.
						A	 =      []
						# Stores indexes of each item for supporting updates and deletion.
						A	 =      {}
						# Stores current size of heap.
						A	 =      0
						# Stores function used to evaluate the score of an item on which basis ordering
						# will be done.
						A	 =      key or (lambda _SCREAMING_SNAKE_CASE		: x)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						return int((i - 1) / 2		) if i > 0 else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 1		)
						return left if 0 < left < self.size else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 2		)
						return right if 0 < right < self.size else None
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A ,		A	 =      (
						    self.pos_map[self.arr[j][0]],
						    self.pos_map[self.arr[i][0]],
						)
						# Then swap the items in the list.
						A ,		A	 =      self.arr[j], self.arr[i]
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   bool:


						'''simple docstring'''
						return self.arr[i][1] < self.arr[j][1]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      self._left(_SCREAMING_SNAKE_CASE		)
						A	 =      self._right(_SCREAMING_SNAKE_CASE		)
						A	 =      i

						if left is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      left
						if right is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      right

						return valid_parent
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._parent(_SCREAMING_SNAKE_CASE		)
						while parent is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      parent, self._parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
						while valid_parent != index:
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      valid_parent, self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						# Make sure heap is right in both up and down direction.
						# Ideally only one of them will make any change.
						self._heapify_up(_SCREAMING_SNAKE_CASE		)
						self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						del self.pos_map[item]
						A	 =      self.arr[self.size - 1]
						A	 =      index
						self.size -= 1
						# Make sure heap is right in both up and down direction. Ideally only one
						# of them will make any change- so no performance loss in calling both.
						if self.size > index:
										self._heapify_up(_SCREAMING_SNAKE_CASE		)
										self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      len(self.arr		)
						if arr_len == self.size:
										self.arr.append([item, self.key(_SCREAMING_SNAKE_CASE		)]		)
						else:
										A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						A	 =      self.size
						self.size += 1
						self._heapify_up(self.size - 1		)
		def       A(  self		:       str		)    ->   tuple | None:


						'''simple docstring'''
						return self.arr[0] if self.size else None







		def       A(  self		:       Any		)    ->   tuple | None:


						'''simple docstring'''
						A	 =      self.get_top()
						if top_item_tuple:
										self.delete_item(top_item_tuple[0]		)
						return top_item_tuple







def      snake_case		(							):
				pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()





