
from math import sqrt




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    number >= 0
				), "'number' must been an int and positive"

				lowercase_	    =   True

				# 0 and 1 are none primes.
				if number <= 1:
								lowercase_	    =   False

				for divisor in range(2		,  int(round(sqrt(snake_case__   )   )   ) + 1   ):
								# if 'number' divisible by 'divisor' then sets 'status'
								# of false and break up the loop.
								if number % divisor == 0:
												lowercase_	    =   False
												break

    # precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'status' must been from type bool"

				return status




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n > 2), "'N' must been an int and > 2"

				# beginList: contains all natural numbers from 2 up to N
				lowercase_	    =   list(range(2		,  n + 1   )   )

				lowercase_	    =   []  # this list will be returns.

				# actual sieve of erathostenes
				for i in range(len(snake_case__   )   ):
								for j in range(i + 1		,  len(snake_case__   )   ):
												if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
																lowercase_	    =   0

    # filters actual prime numbers.
				lowercase_	    =   [x for x in begin_list if x != 0]

				# precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'ans' must been from type list"

				return ans




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n > 2), "'N' must been an int and > 2"

				lowercase_	    =   []

				# iterates over all numbers between 2 up to N+1
				# if a number is prime then appends to list 'ans'
				for number in range(2		,  n + 1   ):
								if is_prime(snake_case__   ):
												ans.append(snake_case__   )

    # precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'ans' must been from type list"

				return ans




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and number >= 0, "'number' must been an int and >= 0"

				lowercase_	    =   []  # this list will be returns of the function.

				# potential prime number factors.

				lowercase_	    =   2

				lowercase_	    =   number

				if number == 0 or number == 1:
								ans.append(snake_case__   )

				# if 'number' not prime then builds the prime factorization of 'number'
				elif not is_prime(snake_case__   ):
								while quotient != 1:
												if is_prime(snake_case__   ) and (quotient % factor == 0):
																ans.append(snake_case__   )
																quotient /= factor
												else:
																factor += 1

				else:
								ans.append(snake_case__   )

				# precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'ans' must been from type list"

				return ans




def 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    number >= 0
				), "'number' bust been an int and >= 0"

				lowercase_	    =   0

				# prime factorization of 'number'
				lowercase_	    =   prime_factorization(snake_case__   )

				lowercase_	    =   max(snake_case__   )

				# precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'ans' must been from type int"

				return ans




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    number >= 0
				), "'number' bust been an int and >= 0"

				lowercase_	    =   0

				# prime factorization of 'number'
				lowercase_	    =   prime_factorization(snake_case__   )

				lowercase_	    =   min(snake_case__   )

				# precondition
				assert isinstance(snake_case__		,  snake_case__   ), "'ans' must been from type int"

				return ans




def 							a		(				snake_case__:   Optional[Any]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ), "'number' must been an int"
				assert isinstance(number % 2 == 0		,  snake_case__   ), "compare bust been from type bool"

				return number % 2 == 0




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ), "'number' must been an int"
				assert isinstance(number % 2 != 0		,  snake_case__   ), "compare bust been from type bool"

				return number % 2 != 0




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				assert (
				    isinstance(snake_case__		,  snake_case__   ) and (number > 2) and is_even(snake_case__   )
				), "'number' must been an int, even and > 2"

				lowercase_	    =   []  # this list will returned

				# creates a list of prime numbers between 2 up to 'number'
				lowercase_	    =   get_prime_numbers(snake_case__   )
				lowercase_	    =   len(snake_case__   )

				# run variable for while-loops.
				lowercase_	    =   0
				lowercase_	    =   None

				# exit variable. for break up the loops
				lowercase_	    =   True

				while i < len_pn and loop:
								lowercase_	    =   i + 1

								while j < len_pn and loop:
												if prime_numbers[i] + prime_numbers[j] == number:
																lowercase_	    =   False
																ans.append(prime_numbers[i]   )
																ans.append(prime_numbers[j]   )

												j += 1

								i += 1

				# precondition
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and (len(snake_case__   ) == 2)
				    and (ans[0] + ans[1] == number)
				    and is_prime(ans[0]   )
				    and is_prime(ans[1]   )
				), "'ans' must contains two primes. And sum of elements must been eq 'number'"

				return ans




def 							a		(				snake_case__:   int		,  snake_case__:   Tuple   ):


				'''simple docstring'''
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and isinstance(snake_case__		,  snake_case__   )
				    and (numbera >= 0)
				    and (numbera >= 0)
				), "'number1' and 'number2' must been positive integer."

				lowercase_	    =   0

				while numbera != 0:
								lowercase_	    =   numbera % numbera
								lowercase_	    =   numbera
								lowercase_	    =   rest

				# precondition
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    numbera >= 0
				), "'number' must been from type int and positive"

				return numbera




def 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   str   ):


				'''simple docstring'''
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and isinstance(snake_case__		,  snake_case__   )
				    and (numbera >= 1)
				    and (numbera >= 1)
				), "'number1' and 'number2' must been positive integer."

				lowercase_	    =   1  # actual answer that will be return.

				# for kgV (x,1)
				if numbera > 1 and numbera > 1:
								# builds the prime factorization of 'number1' and 'number2'
								lowercase_	    =   prime_factorization(snake_case__   )
								lowercase_	    =   prime_factorization(snake_case__   )

				elif numbera == 1 or numbera == 1:
								lowercase_	    =   []
								lowercase_	    =   []
								lowercase_	    =   max(snake_case__		,  snake_case__   )

				lowercase_	    =   0
				lowercase_	    =   0

				lowercase_	    =   []  # captured numbers int both 'primeFac1' and 'primeFac2'

				# iterates through primeFac1
				for n in prime_fac_a:
								if n not in done:
												if n in prime_fac_a:
																lowercase_	    =   prime_fac_a.count(snake_case__   )
																lowercase_	    =   prime_fac_a.count(snake_case__   )

																for _ in range(max(snake_case__		,  snake_case__   )   ):
																				ans *= n

												else:
																lowercase_	    =   prime_fac_a.count(snake_case__   )

																for _ in range(snake_case__   ):
																				ans *= n

												done.append(snake_case__   )

    # iterates through primeFac2
				for n in prime_fac_a:
								if n not in done:
												lowercase_	    =   prime_fac_a.count(snake_case__   )

												for _ in range(snake_case__   ):
																ans *= n

												done.append(snake_case__   )

    # precondition
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    ans >= 0
				), "'ans' must been from type int and positive"

				return ans




def 							a		(				snake_case__:   Optional[Any]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n >= 0), "'number' must been a positive int"

				lowercase_	    =   0
				lowercase_	    =   2  # this variable holds the answer

				while index < n:
								index += 1

								ans += 1  # counts to the next number

								# if ans not prime then
								# runs to the next prime number.
								while not is_prime(snake_case__   ):
												ans += 1

    # precondition
				assert isinstance(snake_case__		,  snake_case__   ) and is_prime(
				    snake_case__   ), "'ans' must been a prime number and from type int"

				return ans




def 							a		(				snake_case__:   Dict		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				assert (
				    is_prime(snake_case__   ) and is_prime(snake_case__   ) and (p_number_a < p_number_a)
				), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

				lowercase_	    =   p_number_a + 1  # jump to the next number

				lowercase_	    =   []  # this list will be returns.

				# if number is not prime then
				# fetch the next prime number.
				while not is_prime(snake_case__   ):
								number += 1

				while number < p_number_a:
								ans.append(snake_case__   )

								number += 1

								# fetch the next prime number.
								while not is_prime(snake_case__   ):
												number += 1

    # precondition
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and ans[0] != p_number_a
				    and ans[len(snake_case__   ) - 1] != p_number_a
				), "'ans' must been a list without the arguments"

				# 'ans' contains not 'pNumber1' and 'pNumber2' !
				return ans




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n >= 1), "'n' must been int and >= 1"

				lowercase_	    =   []  # will be returned.

				for divisor in range(1		,  n + 1   ):
								if n % divisor == 0:
												ans.append(snake_case__   )

    # precondition
				assert ans[0] == 1 and ans[len(snake_case__   ) - 1] == n, "Error in function getDivisiors(...)"

				return ans




def 							a		(				snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (
				    number > 1
				), "'number' must been an int and >= 1"

				lowercase_	    =   get_divisors(snake_case__   )

				# precondition
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and (divisors[0] == 1)
				    and (divisors[len(snake_case__   ) - 1] == number)
				), "Error in help-function getDivisiors(...)"

				# summed all divisors up to 'number' (exclusive), hence [:-1]
				return sum(divisors[:-1]   ) == number




def 							a		(				snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and isinstance(snake_case__		,  snake_case__   )
				    and (denominator != 0)
				), "The arguments must been from type int and 'denominator' != 0"

				# build the greatest common divisor of numerator and denominator.
				lowercase_	    =   gcd(abs(snake_case__   )		,  abs(snake_case__   )   )

				# precondition
				assert (
				    isinstance(snake_case__		,  snake_case__   )
				    and (numerator % gcd_of_fraction == 0)
				    and (denominator % gcd_of_fraction == 0)
				), "Error in function gcd(...,...)"

				return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n >= 0), "'n' must been a int and >= 0"

				lowercase_	    =   1  # this will be return.

				for factor in range(1		,  n + 1   ):
								ans *= factor

				return ans




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				assert isinstance(snake_case__		,  snake_case__   ) and (n >= 0), "'n' must been an int and >= 0"

				lowercase_	    =   0
				lowercase_	    =   1
				lowercase_	    =   1  # this will be return

				for _ in range(n - 1   ):
								lowercase_	    =   ans
								ans += fiba
								lowercase_	    =   tmp

				return ans






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		(				):


				'''simple docstring'''
				lowercase_	    =   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=0.1_5		,  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.'''   )

				lowercase_	    =   parser.parse_args()
				return args




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				try:
								if args.tpu_name:
												lowercase_	    =   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name		,  zone=args.tpu_zone		,  project=args.gcp_project   )
								else:
												lowercase_	    =   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__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   0
				for file in file_list:
								lowercase_	    =   file.split('''/'''   )[-1]
								lowercase_	    =   re.search(r'''-\d+-(\d+)\.tfrecord'''		,  snake_case__   ).group(1   )
								lowercase_	    =   int(snake_case__   )
								num_samples += sample_count

				return num_samples




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   Union[str, Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Dict=None   ):


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




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				if not args.no_tpu:
								lowercase_	    =   initialize_tpu(snake_case__   )
								lowercase_	    =   tf.distribute.TPUStrategy(snake_case__   )
				else:
								lowercase_	    =   tf.distribute.OneDeviceStrategy(device='''/gpu:0'''   )

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

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

				lowercase_	    =   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}.'''   )
				lowercase_	    =   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}.'''   )

				lowercase_	    =   count_samples(snake_case__   )

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

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

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

				lowercase_	    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

				lowercase_	    =   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		,  )

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  )

				lowercase_	    =   []
				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)




