
'''simple docstring'''

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_			:			Union[str, Any]			   =     logging.get_logger(__name__)

if is_vision_available():
   import PIL.Image
   from PIL import Image
   from PIL.Image import Image as ImageType
else:
   lowerCAmelCase_			:			List[str]			   =     object

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf







class 	__lowerCAmelCase    :



     def __init__(self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  int         =    value



     def __str__(self	):
          return self.to_string()



     def     snake_case_ (self	):
          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	):
          logger.error(
              """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""	)
          return str(self._value	)







class 	__lowerCAmelCase    (  lowerCamelCase__       ,					lowerCamelCase__				):



     def     snake_case_ (self	):
          return self._value



     def     snake_case_ (self	):
          return self._value







class 	__lowerCAmelCase    (  lowerCamelCase__       ,					lowerCamelCase__				):



     def __init__(self						,	lowerCAmelCase__	):
          super().__init__(_UpperCAmelCase	)

          if not is_vision_available():
               raise ImportError("""PIL must be installed in order to handle images."""	)

          _UpperCAmelCase					:  List[Any]         =    None
          _UpperCAmelCase					:  Tuple         =    None
          _UpperCAmelCase					:  int         =    None

          if isinstance(_UpperCAmelCase						,	_UpperCAmelCase	):
               _UpperCAmelCase					:  Optional[int]         =    value
          elif isinstance(_UpperCAmelCase						,	(str, pathlib.Path)	):
               _UpperCAmelCase					:  List[Any]         =    value
          elif isinstance(_UpperCAmelCase						,	torch.Tensor	):
               _UpperCAmelCase					:  List[Any]         =    value
          else:
               raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(_UpperCAmelCase	)}"	)



     def     snake_case_ (self						,	lowerCAmelCase__=None						,	lowerCAmelCase__=None	):
          from IPython.display import Image, display

          display(Image(self.to_string()	)	)



     def     snake_case_ (self	):
          if self._raw is not None:
               return self._raw

          if self._path is not None:
               _UpperCAmelCase					:  Any         =    Image.open(self._path	)
               return self._raw



     def     snake_case_ (self	):
          if self._path is not None:
               return self._path

          if self._raw is not None:
               _UpperCAmelCase					:  int         =    tempfile.mkdtemp()
               _UpperCAmelCase					:  List[Any]         =    os.path.join(_UpperCAmelCase						,	str(uuid.uuida()	) + """.png"""	)
               self._raw.save(self._path	)

               return self._path

          if self._tensor is not None:
               _UpperCAmelCase					:  str         =    self._tensor.cpu().detach().numpy()

               # There is likely simpler than load into image into save
               _UpperCAmelCase					:  Union[str, Any]         =    Image.fromarray((array * 2_5_5).astype(np.uinta	)	)

               _UpperCAmelCase					:  Dict         =    tempfile.mkdtemp()
               _UpperCAmelCase					:  Optional[Any]         =    os.path.join(_UpperCAmelCase						,	str(uuid.uuida()	) + """.png"""	)

               img.save(self._path	)

               return self._path







class 	__lowerCAmelCase    (  lowerCamelCase__				):



     def __init__(self						,	lowerCAmelCase__						,	lowerCAmelCase__=1_6_0_0_0	):
          super().__init__(_UpperCAmelCase	)

          if not is_soundfile_availble():
               raise ImportError("""soundfile must be installed in order to handle audio."""	)

          _UpperCAmelCase					:  Any         =    None
          _UpperCAmelCase					:  Optional[int]         =    None

          _UpperCAmelCase					:  List[Any]         =    samplerate

          if isinstance(_UpperCAmelCase						,	(str, pathlib.Path)	):
               _UpperCAmelCase					:  Optional[Any]         =    value
          elif isinstance(_UpperCAmelCase						,	torch.Tensor	):
               _UpperCAmelCase					:  str         =    value
          else:
               raise ValueError(F"Unsupported audio type: {type(_UpperCAmelCase	)}"	)



     def     snake_case_ (self						,	lowerCAmelCase__=None						,	lowerCAmelCase__=None	):
          from IPython.display import Audio, display

          display(Audio(self.to_string()						,	rate=self.samplerate	)	)



     def     snake_case_ (self	):
          if self._tensor is not None:
               return self._tensor

          if self._path is not None:
               _UpperCAmelCase  ,     _UpperCAmelCase					:  List[str]         =    sf.read(self._path	)
               _UpperCAmelCase					:  Union[str, Any]         =    torch.tensor(_UpperCAmelCase	)
               return self._tensor



     def     snake_case_ (self	):
          if self._path is not None:
               return self._path

          if self._tensor is not None:
               _UpperCAmelCase					:  Any         =    tempfile.mkdtemp()
               _UpperCAmelCase					:  List[Any]         =    os.path.join(_UpperCAmelCase						,	str(uuid.uuida()	) + """.wav"""	)
               sf.write(self._path						,	self._tensor						,	samplerate=self.samplerate	)
               return self._path




lowerCAmelCase_			:			Tuple			   =     {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
lowerCAmelCase_			:			Optional[int]			   =     {str: AgentText}

if is_vision_available():
   lowerCAmelCase_			:			Union[str, Any]			   =     AgentImage







def __A  (    *lowerCAmelCase_ ,       **lowerCAmelCase_       ):
     _UpperCAmelCase					:  Optional[Any]         =    [(arg.to_raw() if isinstance(lowerCAmelCase_ ,       lowerCAmelCase_       ) else arg) for arg in args]
     _UpperCAmelCase					:  List[str]         =    {k: (v.to_raw() if isinstance(lowerCAmelCase_ ,       lowerCAmelCase_       ) else v) for k, v in kwargs.items()}
     return args, kwargs







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=None       ):

     if isinstance(lowerCAmelCase_ ,       lowerCAmelCase_       ):
          _UpperCAmelCase					:  Optional[Any]         =    {}
          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					:  Union[str, Any]         =    AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase_       )
                    else:
                         _UpperCAmelCase					:  Optional[Any]         =    AgentType(lowerCAmelCase_       )

               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(lowerCAmelCase_ ,       _k       ):
                              _UpperCAmelCase					:  Dict         =    _v(lowerCAmelCase_       )
                    if k not in decoded_outputs:
                         _UpperCAmelCase					:  Optional[Any]         =    AgentType[v]

     elif isinstance(lowerCAmelCase_ ,       (list, tuple)       ):
          _UpperCAmelCase					:  Optional[int]         =    type(lowerCAmelCase_       )()
          for i, v in enumerate(lowerCAmelCase_       ):
               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]](lowerCAmelCase_       )       )
                    else:
                         decoded_outputs.append(AgentType(lowerCAmelCase_       )       )
               else:
                    # If the class does not have defined output, then we map according to the type
                    _UpperCAmelCase					:  List[str]         =    False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowerCAmelCase_ ,       _k       ):
                              decoded_outputs.append(_v(lowerCAmelCase_       )       )
                              _UpperCAmelCase					:  List[Any]         =    True

                    if not found:
                         decoded_outputs.append(AgentType(lowerCAmelCase_       )       )

     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					:  Optional[Any]         =    AGENT_TYPE_MAPPING[output_types[0]](lowerCAmelCase_       )

          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(lowerCAmelCase_ ,       _k       ):
                         return _v(lowerCAmelCase_       )
               return AgentType(lowerCAmelCase_       )

     return decoded_outputs






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')