



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase							:Any       = {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			pass

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase							:Any       = [
			    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoCBertForCausalLM''',
			    '''RoCBertForMaskedLM''',
			    '''RoCBertForMultipleChoice''',
			    '''RoCBertForPreTraining''',
			    '''RoCBertForQuestionAnswering''',
			    '''RoCBertForSequenceClassification''',
			    '''RoCBertForTokenClassification''',
			    '''RoCBertLayer''',
			    '''RoCBertModel''',
			    '''RoCBertPreTrainedModel''',
			    '''load_tf_weights_in_roc_bert''',
			]

if TYPE_CHECKING:
			from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
			from .tokenization_roc_bert import RoCBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						raise OptionalDependencyNotAvailable()

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roc_bert import (
						    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoCBertForCausalLM,
						    RoCBertForMaskedLM,
						    RoCBertForMultipleChoice,
						    RoCBertForPreTraining,
						    RoCBertForQuestionAnswering,
						    RoCBertForSequenceClassification,
						    RoCBertForTokenClassification,
						    RoCBertLayer,
						    RoCBertModel,
						    RoCBertPreTrainedModel,
						    load_tf_weights_in_roc_bert,
						)


else:
			import sys

			lowerCamelCase							:Dict       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase							:List[str]       = 1_0_0

lowerCamelCase							:Dict       = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase							:int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
						if prime not in primes:
												continue
						primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00   )
def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			A_     :						set[int]     =					set()
			A_     :						int
			A_     :						int

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime   ):
									ret.add(sub * prime   )

			return ret



def      a      (			lowerCamelCase__ = 50_00   ):
			'''simple docstring'''





			for number_to_partition in range(1						,				lowerCamelCase__   ):
						if len(partition(lowerCamelCase__   )   ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
						print(F"{solution() = }")