


'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :		List[str]         =				{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

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

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

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

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





import math

SCREAMING_SNAKE_CASE       :		List[str]         =				10
SCREAMING_SNAKE_CASE       :		Dict         =				7
SCREAMING_SNAKE_CASE       :		int         =				BALLS_PER_COLOUR * NUM_COLOURS




def     UpperCamelCase    (      _a = 2_0		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       math.comb(_a       ,		_a		)
     lowercase_       :Tuple     =       math.comb(NUM_BALLS - BALLS_PER_COLOUR       ,		_a		)

     lowercase_       :Dict     =       NUM_COLOURS * (1 - missing_colour / total)

     return f"{result:.9f}"


if __name__ == "__main__":
   print(solution(20))

