code: 
  
  
  """simple docstring"""
  
  
  
  
  from itertools import product
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[Any] = 15    ):
  			__lowerCamelCase        =			0.0
  			__lowerCamelCase        =			42
  			__lowerCamelCase        =			42
  			__lowerCamelCase        =			42
  			__lowerCamelCase        =			42
  			__lowerCamelCase        =			42
  			__lowerCamelCase        =			42
  
  			for series in product(range(2    ), repeat=lowercase__    ):
  						__lowerCamelCase        =			series.count(1    )
  						__lowerCamelCase        =			num_turns - num_blue
  						if num_red >= num_blue:
  									continue
  						__lowerCamelCase        =			1.0
  						for ind, col in enumerate(lowercase__, 2    ):
  									if col == 0:
  												prob *= (ind - 1) / ind
  									else:
  												prob *= 1 / ind
  
  						total_prob += prob
  
  			return int(1 / total_prob    )
  
  
  if __name__ == "__main__":
  					print(f"""{solution() = }""")
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __magic_name__	:   Optional[Any]          =							{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   List[Any]          =							[
  					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'FlaubertForMultipleChoice',
  					    'FlaubertForQuestionAnswering',
  					    'FlaubertForQuestionAnsweringSimple',
  					    'FlaubertForSequenceClassification',
  					    'FlaubertForTokenClassification',
  					    'FlaubertModel',
  					    'FlaubertWithLMHeadModel',
  					    'FlaubertPreTrainedModel',
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TFFlaubertForMultipleChoice',
  					    'TFFlaubertForQuestionAnsweringSimple',
  					    'TFFlaubertForSequenceClassification',
  					    'TFFlaubertForTokenClassification',
  					    'TFFlaubertModel',
  					    'TFFlaubertPreTrainedModel',
  					    'TFFlaubertWithLMHeadModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  					from .tokenization_flaubert import FlaubertTokenizer
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_flaubert import (
  										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    FlaubertForMultipleChoice,
  										    FlaubertForQuestionAnswering,
  										    FlaubertForQuestionAnsweringSimple,
  										    FlaubertForSequenceClassification,
  										    FlaubertForTokenClassification,
  										    FlaubertModel,
  										    FlaubertPreTrainedModel,
  										    FlaubertWithLMHeadModel,
  										)
  
  					try:
  										if not is_tf_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tf_flaubert import (
  										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TFFlaubertForMultipleChoice,
  										    TFFlaubertForQuestionAnsweringSimple,
  										    TFFlaubertForSequenceClassification,
  										    TFFlaubertForTokenClassification,
  										    TFFlaubertModel,
  										    TFFlaubertPreTrainedModel,
  										    TFFlaubertWithLMHeadModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  