
from typing import TYPE_CHECKING

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


_snake_case		  =   {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case		  =   ["DebertaV2TokenizerFast"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case		  =   [
     "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDebertaV2ForMaskedLM",
     "TFDebertaV2ForQuestionAnswering",
     "TFDebertaV2ForSequenceClassification",
     "TFDebertaV2ForTokenClassification",
     "TFDebertaV2Model",
     "TFDebertaV2PreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case		  =   [
     "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DebertaV2ForMaskedLM",
     "DebertaV2ForMultipleChoice",
     "DebertaV2ForQuestionAnswering",
     "DebertaV2ForSequenceClassification",
     "DebertaV2ForTokenClassification",
     "DebertaV2Model",
     "DebertaV2PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

 _snake_case		  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import math
def lowerCamelCase_       (			A			:      int						):




      """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 lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


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



