






'''simple docstring'''





import math
import unittest






def    lowerCamelCase_     (    lowercase__):
 assert isinstance(lowercase__				,     lowercase__) and (
     number >= 0
 ), "'number' must been an int and positive"

 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(lowercase__) + 1)				,     6):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def    a__				(					self     :     List[Any]     )							->							str:

     '''simple docstring'''




     self.assertTrue(is_prime(2     )     )
     self.assertTrue(is_prime(3     )     )
     self.assertTrue(is_prime(5     )     )
     self.assertTrue(is_prime(7     )     )
     self.assertTrue(is_prime(11     )     )
     self.assertTrue(is_prime(13     )     )
     self.assertTrue(is_prime(17     )     )
     self.assertTrue(is_prime(19     )     )
     self.assertTrue(is_prime(23     )     )
     self.assertTrue(is_prime(29     )     )




    def    a__				(					self     :     Union[str, Any]     )							->							Optional[int]:

     '''simple docstring'''




     with self.assertRaises(__lowerCamelCase     ):
      is_prime(-19     )
     self.assertFalse(
         is_prime(0     )				,     "Zero doesn't have any positive factors, primes must have exactly two."				,     )
     self.assertFalse(
         is_prime(1     )				,     "One only has 1 positive factor, primes must have exactly two."				,     )
     self.assertFalse(is_prime(2 * 2     )     )
     self.assertFalse(is_prime(2 * 3     )     )
     self.assertFalse(is_prime(3 * 3     )     )
     self.assertFalse(is_prime(3 * 5     )     )
     self.assertFalse(is_prime(3 * 5 * 7     )     )


if __name__ == "__main__":
 unittest.main()







'''simple docstring'''





from typing import TYPE_CHECKING

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


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

