





import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
     import torch_xla.core.xla_model as xm  # noqa: F401

     _UpperCamelCase   :							str     =	True
except ImportError:
     _UpperCamelCase   :							str     =	False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_UpperCamelCase   :							Tuple     =	torch.distributed.is_available()
def 				__UpperCamelCase  (		snake_case     )  ->						Optional[Any]:


   '''simple docstring'''







   __A				 =       importlib.util.find_spec(SCREAMING_SNAKE_CASE_     ) is not None
   if package_exists:
      try:
         __A				 =       importlib.metadata.metadata(SCREAMING_SNAKE_CASE_     )
         return True
      except importlib.metadata.PackageNotFoundError:
         return False
def 				__UpperCamelCase  (		)  ->						bool:


   '''simple docstring'''







   return _torch_distributed_available
def 				__UpperCamelCase  (		)  ->						Optional[int]:


   '''simple docstring'''







   try:
      pass
   except ImportError:
      print(
          '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
          ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
          ''' Bindings for PyTorch*.'''     )
   return (
       importlib.util.find_spec('''torch_ccl'''     ) is not None
       or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''     ) is not None
   )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return importlib.metadata.version('''oneccl_bind_pt'''     )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return _is_package_available('''transformer_engine'''     )
@lru_cache
def 				__UpperCamelCase  (		snake_case=True     )  ->						List[Any]:


   '''simple docstring'''







   if torch.cuda.is_available():
      return False
   if _tpu_available and check_device:
      try:
         # Will raise a RuntimeError if no XLA configuration is found
         __A				 =       xm.xla_device()
         return True
      except RuntimeError:
         return False
   return _tpu_available
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return _is_package_available('''deepspeed'''     )
def 				__UpperCamelCase  (		snake_case=False     )  ->						Optional[Any]:


   '''simple docstring'''







   if is_tpu_available():
      return not ignore_tpu
   if torch.cuda.is_available():
      return torch.cuda.is_bfaa_supported()
   if is_npu_available():
      return False
   return True
def 				__UpperCamelCase  (		)  ->						Tuple:


   '''simple docstring'''







   __A				 =       _is_package_available('''bitsandbytes'''     )
   if package_exists:
      __A				 =       version.parse(importlib.metadata.version('''bitsandbytes'''     )     )
      return compare_versions(SCREAMING_SNAKE_CASE_    ,	'''>='''    ,	'''0.39.0'''     )
   return False
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   __A				 =       _is_package_available('''bitsandbytes'''     )
   if package_exists:
      __A				 =       version.parse(importlib.metadata.version('''bitsandbytes'''     )     )
      return compare_versions(SCREAMING_SNAKE_CASE_    ,	'''>='''    ,	'''0.37.2'''     )
   return False
def 				__UpperCamelCase  (		)  ->						str:


   '''simple docstring'''







   return _is_package_available('''bitsandbytes'''     )
def 				__UpperCamelCase  (		)  ->						Dict:


   '''simple docstring'''







   if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''    ,	'''False'''     )     ) == 1:
      __A				 =       importlib.util.find_spec('''megatron'''     ) is not None
      if package_exists:
         try:
            __A				 =       parse(importlib.metadata.version('''megatron-lm'''     )     )
            return compare_versions(SCREAMING_SNAKE_CASE_    ,	'''>='''    ,	'''2.2.0'''     )
         except Exception as e:
            warnings.warn(F"Parse Megatron version failed. Exception:{e}"     )
            return False
def 				__UpperCamelCase  (		)  ->						Optional[Any]:


   '''simple docstring'''







   return _is_package_available('''safetensors'''     )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return _is_package_available('''transformers'''     )
def 				__UpperCamelCase  (		)  ->						List[str]:


   '''simple docstring'''







   return _is_package_available('''datasets'''     )
def 				__UpperCamelCase  (		)  ->						str:


   '''simple docstring'''







   return _is_package_available('''aim'''     )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return _is_package_available('''tensorboard'''     ) or _is_package_available('''tensorboardX'''     )
def 				__UpperCamelCase  (		)  ->						List[str]:


   '''simple docstring'''







   return _is_package_available('''wandb'''     )
def 				__UpperCamelCase  (		)  ->						List[str]:


   '''simple docstring'''







   return _is_package_available('''comet_ml'''     )
def 				__UpperCamelCase  (		)  ->						Tuple:


   '''simple docstring'''







   return _is_package_available('''boto3'''     )
def 				__UpperCamelCase  (		)  ->						Dict:


   '''simple docstring'''







   if _is_package_available('''rich'''     ):
      if "ACCELERATE_DISABLE_RICH" in os.environ:
         warnings.warn(
             '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''     )
         return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''    ,	SCREAMING_SNAKE_CASE_     )
      return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''    ,	SCREAMING_SNAKE_CASE_     )
   return False
def 				__UpperCamelCase  (		)  ->						Optional[int]:


   '''simple docstring'''







   return _is_package_available('''sagemaker'''     )
def 				__UpperCamelCase  (		)  ->						Optional[Any]:


   '''simple docstring'''







   return _is_package_available('''tqdm'''     )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return _is_package_available('''mlflow'''     )
def 				__UpperCamelCase  (		)  ->						List[Any]:


   '''simple docstring'''







   return is_torch_version('''>='''    ,	'''1.12'''     ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
def 				__UpperCamelCase  (		)  ->						Union[str, Any]:


   '''simple docstring'''







   def get_major_and_minor_from_version(snake_case     ):
      return str(version.parse(SCREAMING_SNAKE_CASE_     ).major     ) + "." + str(version.parse(SCREAMING_SNAKE_CASE_     ).minor     )

   __A				 =       importlib.metadata.version('''torch'''     )
   if importlib.util.find_spec('''intel_extension_for_pytorch'''     ) is None:
      return False
   __A				 =       "N/A"
   try:
      __A				 =       importlib.metadata.version('''intel_extension_for_pytorch'''     )
   except importlib.metadata.PackageNotFoundError:
      return False
   __A				 =       get_major_and_minor_from_version(_torch_version     )
   __A				 =       get_major_and_minor_from_version(_ipex_version     )
   if torch_major_and_minor != ipex_major_and_minor:
      warnings.warn(
          F"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
          F" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."     )
      return False
   return True
@lru_cache
def 				__UpperCamelCase  (		snake_case=False     )  ->						Dict:


   '''simple docstring'''







   if importlib.util.find_spec('''torch'''     ) is None or importlib.util.find_spec('''torch_npu'''     ) is None:
      return False

   import torch
   import torch_npu  # noqa: F401

   if check_device:
      try:
         # Will raise a RuntimeError if no NPU is found
         __A				 =       torch.npu.device_count()
         return torch.npu.is_available()
      except RuntimeError:
         return False
   return hasattr(SCREAMING_SNAKE_CASE_    ,	'''npu'''     ) and torch.npu.is_available()
@lru_cache
def 				__UpperCamelCase  (		snake_case=False     )  ->						Union[str, Any]:


   '''simple docstring'''







   if not parse_flag_from_env('''ACCELERATE_USE_XPU'''    ,	default=SCREAMING_SNAKE_CASE_     ):
      return False
   if is_ipex_available():
      import torch

      if is_torch_version('''<='''    ,	'''1.12'''     ):
         return False
   else:
      return False

   import intel_extension_for_pytorch  # noqa: F401

   if check_device:
      try:
         # Will raise a RuntimeError if no XPU  is found
         __A				 =       torch.xpu.device_count()
         return torch.xpu.is_available()
      except RuntimeError:
         return False
   return hasattr(SCREAMING_SNAKE_CASE_    ,	'''xpu'''     ) and torch.xpu.is_available()








def 				__UpperCamelCase  (		snake_case = 1_0_0     )  ->						int:


   '''simple docstring'''







   __A				 =       [[0 for _ in range(snake_case     )] for _ in range(m + 1     )]
   for i in range(m + 1     ):
      __A				 =       1

   for n in range(m + 1     ):
      for k in range(1    ,	snake_case     ):
         memo[n][k] += memo[n][k - 1]
         if n > k:
            memo[n][k] += memo[n - k - 1][k]

   return memo[m][m - 1] - 1


if __name__ == "__main__":
    print(solution(int(input("""Enter a number: """).strip())))

