code: 
  
  
  
  
  
  import os
  import pathlib
  import tempfile
  import uuid
  
  import numpy as np
  
  from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging
  
  
  __lowerCAmelCase     =			logging.get_logger(__name__)
  
  if is_vision_available():
  				import PIL.Image
  				from PIL import Image
  				from PIL.Image import Image as ImageType
  else:
  				__lowerCAmelCase     =			object
  
  if is_torch_available():
  				import torch
  
  if is_soundfile_availble():
  				import soundfile as sf
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase       )						->  Optional[int]:
  							UpperCamelCase__         =	value
  
  
  
  
  					def __str__(						self       )						->  Union[str, Any]:
  							return self.to_string()
  
  
  
  
  					def 	snake_case_		(						self       )						->  Dict:
  							logger.error(
  							    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""       )
  							return self._value
  
  
  
  
  					def 	snake_case_		(						self       )						->  str:
  							logger.error(
  							    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""       )
  							return str(self._value       )
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase ,       _lowercase					):
  
  
  
  
  					def 	snake_case_		(						self       )						->  int:
  							return self._value
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  							return self._value
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase ,       _lowercase					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase       )						->  Optional[Any]:
  							super().__init__(__lowerCAmelCase       )
  
  							if not is_vision_available():
  									raise ImportError("""PIL must be installed in order to handle images."""       )
  
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	None
  
  							if isinstance(__lowerCAmelCase    ,      __lowerCAmelCase       ):
  									UpperCamelCase__         =	value
  							elif isinstance(__lowerCAmelCase    ,      (str, pathlib.Path)       ):
  									UpperCamelCase__         =	value
  							elif isinstance(__lowerCAmelCase    ,      torch.Tensor       ):
  									UpperCamelCase__         =	value
  							else:
  									raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(__lowerCAmelCase       )}"""       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None       )						->  Union[str, Any]:
  							from IPython.display import Image, display
  
  							display(Image(self.to_string()       )       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Optional[int]:
  							if self._raw is not None:
  									return self._raw
  
  							if self._path is not None:
  									UpperCamelCase__         =	Image.open(self._path       )
  									return self._raw
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  							if self._path is not None:
  									return self._path
  
  							if self._raw is not None:
  									UpperCamelCase__         =	tempfile.mkdtemp()
  									UpperCamelCase__         =	os.path.join(__lowerCAmelCase    ,      str(uuid.uuida()       ) + """.png"""       )
  									self._raw.save(self._path       )
  
  									return self._path
  
  							if self._tensor is not None:
  									UpperCamelCase__         =	self._tensor.cpu().detach().numpy()
  
  									# There is likely simpler than load into image into save
  									UpperCamelCase__         =	Image.fromarray((array * 255).astype(np.uinta       )       )
  
  									UpperCamelCase__         =	tempfile.mkdtemp()
  									UpperCamelCase__         =	os.path.join(__lowerCAmelCase    ,      str(uuid.uuida()       ) + """.png"""       )
  
  									img.save(self._path       )
  
  									return self._path
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase=16_000       )						->  List[str]:
  							super().__init__(__lowerCAmelCase       )
  
  							if not is_soundfile_availble():
  									raise ImportError("""soundfile must be installed in order to handle audio."""       )
  
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	None
  
  							UpperCamelCase__         =	samplerate
  
  							if isinstance(__lowerCAmelCase    ,      (str, pathlib.Path)       ):
  									UpperCamelCase__         =	value
  							elif isinstance(__lowerCAmelCase    ,      torch.Tensor       ):
  									UpperCamelCase__         =	value
  							else:
  									raise ValueError(F"""Unsupported audio type: {type(__lowerCAmelCase       )}"""       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None       )						->  str:
  							from IPython.display import Audio, display
  
  							display(Audio(self.to_string()    ,      rate=self.samplerate       )       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  							if self._tensor is not None:
  									return self._tensor
  
  							if self._path is not None:
  									UpperCamelCase__  ,			UpperCamelCase__         =	sf.read(self._path       )
  									UpperCamelCase__         =	torch.tensor(__lowerCAmelCase       )
  									return self._tensor
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  							if self._path is not None:
  									return self._path
  
  							if self._tensor is not None:
  									UpperCamelCase__         =	tempfile.mkdtemp()
  									UpperCamelCase__         =	os.path.join(__lowerCAmelCase    ,      str(uuid.uuida()       ) + """.wav"""       )
  									sf.write(self._path    ,      self._tensor    ,      samplerate=self.samplerate       )
  									return self._path
  
  
  
  __lowerCAmelCase     =			{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
  __lowerCAmelCase     =			{str: AgentText}
  
  if is_vision_available():
  				__lowerCAmelCase     =			AgentImage
  
  def     a						(      *_lowercase   :     Dict ,						**_lowercase   :     Dict       )     ->int:
  		UpperCamelCase__         =	[(arg.to_raw() if isinstance(_lowercase ,						_lowercase       ) else arg) for arg in args]
  		UpperCamelCase__         =	{k: (v.to_raw() if isinstance(_lowercase ,						_lowercase       ) else v) for k, v in kwargs.items()}
  		return args, kwargs
  
  def     a						(      _lowercase   :     Optional[int] ,						_lowercase   :     Optional[Any]=None       )     ->Dict:
  		if isinstance(_lowercase ,						_lowercase       ):
  				UpperCamelCase__         =	{}
  				for i, (k, v) in enumerate(outputs.items()       ):
  						if output_types is not None:
  								# If the class has defined outputs, we can map directly according to the class definition
  								if output_types[i] in AGENT_TYPE_MAPPING:
  										UpperCamelCase__         =	AGENT_TYPE_MAPPING[output_types[i]](_lowercase       )
  								else:
  										UpperCamelCase__         =	AgentType(_lowercase       )
  
  						else:
  								# If the class does not have defined output, then we map according to the type
  								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  										if isinstance(_lowercase ,						_k       ):
  												UpperCamelCase__         =	_v(_lowercase       )
  								if k not in decoded_outputs:
  										UpperCamelCase__         =	AgentType[v]
  
  		elif isinstance(_lowercase ,						(list, tuple)       ):
  				UpperCamelCase__         =	type(_lowercase       )()
  				for i, v in enumerate(_lowercase       ):
  						if output_types is not None:
  								# If the class has defined outputs, we can map directly according to the class definition
  								if output_types[i] in AGENT_TYPE_MAPPING:
  										decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](_lowercase       )       )
  								else:
  										decoded_outputs.append(AgentType(_lowercase       )       )
  						else:
  								# If the class does not have defined output, then we map according to the type
  								UpperCamelCase__         =	False
  								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  										if isinstance(_lowercase ,						_k       ):
  												decoded_outputs.append(_v(_lowercase       )       )
  												UpperCamelCase__         =	True
  
  								if not found:
  										decoded_outputs.append(AgentType(_lowercase       )       )
  
  		else:
  				if output_types[0] in AGENT_TYPE_MAPPING:
  						# If the class has defined outputs, we can map directly according to the class definition
  						UpperCamelCase__         =	AGENT_TYPE_MAPPING[output_types[0]](_lowercase       )
  
  				else:
  						# If the class does not have defined output, then we map according to the type
  						for _k, _v in INSTANCE_TYPE_MAPPING.items():
  								if isinstance(_lowercase ,						_k       ):
  										return _v(_lowercase       )
  						return AgentType(_lowercase       )
  
  		return decoded_outputs
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     int       )     ->int:
  		UpperCamelCase__         =	1.0
  		for i in range(1 ,						r + 1       ):
  				ret *= (n + 1 - i) / i
  		return round(_lowercase       )
  
  def     a						(      _lowercase   :     int       )     ->int:
  		return choose(8 + n ,						_lowercase       ) + choose(9 + n ,						_lowercase       ) - 1_0
  
  def     a						(      _lowercase   :     int       )     ->int:
  		return sum(non_bouncy_exact(_lowercase       ) for i in range(1 ,						n + 1       )       )
  
  def     a						(      _lowercase   :     int = 1_0_0       )     ->int:
  		return non_bouncy_upto(_lowercase       )
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
  
  
  
  
  