




from math import factorial



def 				_A  (		lowerCAmelCase_	:   int = 20     ):
     """simple docstring"""

     lowerCAmelCase__            =					2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
     # 2, 3,...
     lowerCAmelCase__            =					n // 2

     return int(factorial(lowerCAmelCase_     ) / (factorial(lowerCAmelCase_     ) * factorial(n - k     ))     )


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(20))
     else:
          try:
               UpperCamelCase								=	int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """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:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """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

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