
'''simple docstring'''
import argparse
import logging
import os
import re

import tensorflow as tf

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


lowercase__						       =      logging.getLogger(__name__)

lowercase__						       =      tf.data.AUTOTUNE




def    _UpperCamelCase						(       )  ->							str:


		'''simple docstring'''


		snake_case  :       Dict     			=						argparse.ArgumentParser(description='''Train a masked language model on TPU.'''			)
		parser.add_argument(
		    '''--pretrained_model_config''' ,       type=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__ ,       default=2**18 ,       help='''Size of the shuffle buffer (in samples)''' ,       )

		parser.add_argument(
		    '''--eval_dataset''' ,       type=SCREAMING_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=SCREAMING_SNAKE_CASE__ ,       default=1 ,       help='''Number of epochs to train for.''' ,       )

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

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

		parser.add_argument(
		    '''--max_length''' ,       type=SCREAMING_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=SCREAMING_SNAKE_CASE__ ,       default=0.15 ,       help='''Fraction of tokens to mask during training.''' ,       )

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

		snake_case  :       Tuple     			=						parser.parse_args()
		return args




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''simple docstring'''


		try:
				if args.tpu_name:
						snake_case  :       Any     			=						tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name ,       zone=args.tpu_zone ,       project=args.gcp_project			)
				else:
						snake_case  :       str     			=						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(SCREAMING_SNAKE_CASE__			)
		tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE__			)

		return tpu




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


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

		return num_samples




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							str:


		'''simple docstring'''


		snake_case  :       int     			=						count_samples(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Tuple     			=						tf.data.Dataset.from_tensor_slices(SCREAMING_SNAKE_CASE__			)
		if shuffle:
				snake_case  :       int     			=						dataset.shuffle(len(SCREAMING_SNAKE_CASE__			)			)
		snake_case  :       Optional[int]     			=						tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE__ ,       num_parallel_reads=SCREAMING_SNAKE_CASE__			)
		# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
		snake_case  :       Optional[Any]     			=						dataset.apply(tf.data.experimental.assert_cardinality(SCREAMING_SNAKE_CASE__			)			)
		snake_case  :       Tuple     			=						dataset.map(SCREAMING_SNAKE_CASE__ ,       num_parallel_calls=SCREAMING_SNAKE_CASE__			)
		if shuffle:
				assert shuffle_buffer_size is not None
				snake_case  :       List[str]     			=						dataset.shuffle(args.shuffle_buffer_size			)
		snake_case  :       Dict     			=						dataset.batch(SCREAMING_SNAKE_CASE__ ,       drop_remainder=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[Any]     			=						dataset.map(SCREAMING_SNAKE_CASE__ ,       num_parallel_calls=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[int]     			=						dataset.prefetch(SCREAMING_SNAKE_CASE__			)
		return dataset




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		if not args.no_tpu:
				snake_case  :       List[Any]     			=						initialize_tpu(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Union[str, Any]     			=						tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       str     			=						tf.distribute.OneDeviceStrategy(device='''/gpu:0'''			)

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

		snake_case  :       Union[str, Any]     			=						AutoTokenizer.from_pretrained(args.tokenizer			)
		snake_case  :       int     			=						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  :       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}.'			)

		snake_case  :       str     			=						count_samples(SCREAMING_SNAKE_CASE__			)

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

		with strategy.scope():
				snake_case  :       Any     			=						TFAutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE__			)
				model(model.dummy_inputs			)  # Pass some dummy inputs through the model to ensure all the weights are built
				snake_case			,snake_case  :       Union[str, Any]     			=						create_optimizer(
				    num_train_steps=SCREAMING_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=SCREAMING_SNAKE_CASE__ ,       metrics=['''accuracy''']			)

		def decode_fn(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Union[str, 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(SCREAMING_SNAKE_CASE__ ,       SCREAMING_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.
		snake_case  :       List[Any]     			=						DataCollatorForLanguageModeling(
		    tokenizer=SCREAMING_SNAKE_CASE__ ,       mlm_probability=args.mlm_probability ,       mlm=SCREAMING_SNAKE_CASE__ ,       return_tensors='''tf'''			)

		def mask_with_collator(SCREAMING_SNAKE_CASE__			):
				# TF really needs an isin() function
				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)
				)
				snake_case			,snake_case  :       Tuple     			=						data_collator.tf_mask_tokens(
				    batch['''input_ids'''] ,       vocab_size=len(SCREAMING_SNAKE_CASE__			) ,       mask_token_id=tokenizer.mask_token_id ,       special_tokens_mask=SCREAMING_SNAKE_CASE__ ,       )
				return batch

		snake_case  :       Optional[int]     			=						args.per_replica_batch_size * strategy.num_replicas_in_sync

		snake_case  :       str     			=						prepare_dataset(
		    SCREAMING_SNAKE_CASE__ ,       decode_fn=SCREAMING_SNAKE_CASE__ ,       mask_fn=SCREAMING_SNAKE_CASE__ ,       batch_size=SCREAMING_SNAKE_CASE__ ,       shuffle=SCREAMING_SNAKE_CASE__ ,       shuffle_buffer_size=args.shuffle_buffer_size ,       )

		snake_case  :       Any     			=						prepare_dataset(
		    SCREAMING_SNAKE_CASE__ ,       decode_fn=SCREAMING_SNAKE_CASE__ ,       mask_fn=SCREAMING_SNAKE_CASE__ ,       batch_size=SCREAMING_SNAKE_CASE__ ,       shuffle=SCREAMING_SNAKE_CASE__ ,       )

		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=SCREAMING_SNAKE_CASE__			)			)

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

		model.save_pretrained(args.output_dir			)


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




'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__						       =      TypeVar("T")






class 	snake_case__       (      Generic[T]					):







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			list[T]  ,	UpperCamelCase__				:			Callable[[T, T], T]      )						->			None:






				"""simple docstring"""







				snake_case  :       Any | T     			=						None

				snake_case  :       int     			=						len(UpperCamelCase__      )
				snake_case  :       list[T]     			=						[any_type for _ in range(self.N      )] + arr
				snake_case  :       Optional[int]     			=						fnc
				self.build()



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			None:






				"""simple docstring"""







				for p in range(self.N - 1  ,	0  ,	-1      ):
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			T      )						->			None:






				"""simple docstring"""







				p += self.N
				snake_case  :       int     			=						v
				while p > 1:
						snake_case  :       List[Any]     			=						p // 2
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int      )						->			T | None:  # noqa: E741






				"""simple docstring"""







				snake_case			,snake_case  :       Union[str, Any]     			=						l + self.N, r + self.N

				snake_case  :       T | None     			=						None
				while l <= r:
						if l % 2 == 1:
								snake_case  :       str     			=						self.st[l] if res is None else self.fn(UpperCamelCase__  ,	self.st[l]      )
						if r % 2 == 0:
								snake_case  :       Tuple     			=						self.st[r] if res is None else self.fn(UpperCamelCase__  ,	self.st[r]      )
						snake_case			,snake_case  :       Dict     			=						(l + 1) // 2, (r - 1) // 2
				return res


if __name__ == "__main__":
						from functools import reduce

						lowercase__						       =      [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

						lowercase__						       =      {
						    0: 7,
						    1: 2,
						    2: 6,
						    3: -1_4,
						    4: 5,
						    5: 4,
						    6: 7,
						    7: -1_0,
						    8: 9,
						    9: 1_0,
						    1_0: 1_2,
						    1_1: 1,
						}

						lowercase__						       =      SegmentTree(test_array, min)
						lowercase__						       =      SegmentTree(test_array, max)
						lowercase__						       =      SegmentTree(test_array, lambda a, b: a + b)




						def    _UpperCamelCase						(       )  ->							None:


								'''simple docstring'''


								for i in range(len(SCREAMING_SNAKE_CASE__			)			):
										for j in range(SCREAMING_SNAKE_CASE__ ,       len(SCREAMING_SNAKE_CASE__			)			):
												snake_case  :       int     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       List[str]     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       Tuple     			=						reduce(lambda SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			: a + b ,       test_array[i : j + 1]			)
												assert min_range == min_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert max_range == max_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert sum_range == sum_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

						test_all_segments()

						for index, value in test_updates.items():
												lowercase__						       =      value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()


