



'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    1  # To kept the Calculated Value
   # Since C(n, k) = C(n, n-k)
   if k > (n - k):
      __a				     =    n - k
   # Calculate C(n,k)
   for i in range(_SCREAMING_SNAKE_CASE    ):
      result *= n - i
      result //= i + 1
   return result



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			str:

   """simple docstring"""
   return binomial_coefficient(2 * node_count,					_SCREAMING_SNAKE_CASE    ) // (node_count + 1)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			str:

   """simple docstring"""



   if n < 0:
      raise ValueError('factorial() not defined for negative values'    )
   __a				     =    1
   for i in range(1,					n + 1    ):
      result *= i
   return result



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			Any:

   """simple docstring"""
   return catalan_number(_SCREAMING_SNAKE_CASE    ) * factorial(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
 __UpperCamelCase      :       str      			=      int(input("""Enter the number of nodes: """).strip() or 0)
 if node_count <= 0:
  raise ValueError("""We need some nodes to work with.""")
 print(
     f"""Given {node_count} nodes, there are {binary_tree_count(node_count)} """
     f"""binary trees and {catalan_number(node_count)} binary search trees."""
 )







'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

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