


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case											=					{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					['''RemBertTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					['''RemBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RemBertForCausalLM''',
		    '''RemBertForMaskedLM''',
		    '''RemBertForMultipleChoice''',
		    '''RemBertForQuestionAnswering''',
		    '''RemBertForSequenceClassification''',
		    '''RemBertForTokenClassification''',
		    '''RemBertLayer''',
		    '''RemBertModel''',
		    '''RemBertPreTrainedModel''',
		    '''load_tf_weights_in_rembert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRemBertForCausalLM''',
		    '''TFRemBertForMaskedLM''',
		    '''TFRemBertForMultipleChoice''',
		    '''TFRemBertForQuestionAnswering''',
		    '''TFRemBertForSequenceClassification''',
		    '''TFRemBertForTokenClassification''',
		    '''TFRemBertLayer''',
		    '''TFRemBertModel''',
		    '''TFRemBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert import RemBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert_fast import RemBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rembert import (
				    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RemBertForCausalLM,
				    RemBertForMaskedLM,
				    RemBertForMultipleChoice,
				    RemBertForQuestionAnswering,
				    RemBertForSequenceClassification,
				    RemBertForTokenClassification,
				    RemBertLayer,
				    RemBertModel,
				    RemBertPreTrainedModel,
				    load_tf_weights_in_rembert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rembert import (
				    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRemBertForCausalLM,
				    TFRemBertForMaskedLM,
				    TFRemBertForMultipleChoice,
				    TFRemBertForQuestionAnswering,
				    TFRemBertForSequenceClassification,
				    TFRemBertForTokenClassification,
				    TFRemBertLayer,
				    TFRemBertModel,
				    TFRemBertPreTrainedModel,
				)


else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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

import math


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5			, int(math.sqrt(__a       ) + 1       )			, 6       ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True


def       a				(      __a       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			str(__a       )
					UpperCamelCase__       :Dict							 =			[n]
					for i in range(1			, len(__a       )       ):
										list_nums.append(int(str_num[i:]       )       )
										list_nums.append(int(str_num[:-i]       )       )
					return list_nums


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					if len(str(__a       )       ) > 3:
										if not is_prime(int(str(__a       )[-3:]       )       ) or not is_prime(int(str(__a       )[:3]       )       ):
															return False
					return True


def       a				(      __a = 11       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :list[int]							 =			[]
					UpperCamelCase__       :int							 =			13
					while len(__a       ) != count:
										if validate(__a       ):
															UpperCamelCase__       :Optional[int]							 =			list_truncated_nums(__a       )
															if all(is_prime(__a       ) for i in list_nums       ):
																				list_truncated_primes.append(__a       )
										num += 2
					return list_truncated_primes


def       a				(      )  ->     int:



					'''simple docstring'''






					return sum(compute_truncated_primes(11       )       )


if __name__ == "__main__":
		print(F"""{sum(compute_truncated_primes(11)) = }""")