'''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


_SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)

if is_vision_available():
     import PIL.Image
     from PIL import Image
     from PIL.Image import Image as ImageType
else:
     _SCREAMING_SNAKE_CASE   						=    object

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf







class 					UpperCAmelCase_				:




    def __init__(					self  :   Dict      ,      lowerCAmelCase  :   List[Any]   )	-> Optional[int]:
      a__     		=     value




    def __str__(					self  :   Optional[Any]   )	-> List[Any]:
      return self.to_string()




    def 						__lowerCamelCase (					self  :   Tuple   )	-> List[str]:
      logger.error(
          "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"   )
      return self._value




    def 						__lowerCamelCase (					self  :   List[str]   )	-> 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_				(							_a     ,     _a    ):




    def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> str:
      return self._value




    def 						__lowerCamelCase (					self  :   int   )	-> str:
      return self._value







class 					UpperCAmelCase_				(							_a     ,     _a    ):




    def __init__(					self  :   List[str]      ,      lowerCAmelCase  :   List[str]   )	-> Optional[int]:
      super().__init__(lowerCAmelCase   )

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

      a__     		=     None
      a__     		=     None
      a__     		=     None

      if isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
        a__     		=     value
      elif isinstance(lowerCAmelCase      ,      (str, pathlib.Path)   ):
        a__     		=     value
      elif isinstance(lowerCAmelCase      ,      torch.Tensor   ):
        a__     		=     value
      else:
        raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(lowerCAmelCase   )}"   )




    def 						__lowerCamelCase (					self  :   Tuple      ,      lowerCAmelCase  :   Tuple=None      ,      lowerCAmelCase  :   int=None   )	-> Dict:
      from IPython.display import Image, display

      display(Image(self.to_string()   )   )




    def 						__lowerCamelCase (					self  :   Any   )	-> str:
      if self._raw is not None:
        return self._raw

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




    def 						__lowerCamelCase (					self  :   Any   )	-> Union[str, Any]:
      if self._path is not None:
        return self._path

      if self._raw is not None:
        a__     		=     tempfile.mkdtemp()
        a__     		=     os.path.join(lowerCAmelCase      ,      str(uuid.uuida()   ) + ".png"   )
        self._raw.save(self._path   )

        return self._path

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

        # There is likely simpler than load into image into save
        a__     		=     Image.fromarray((array * 255).astype(np.uinta   )   )

        a__     		=     tempfile.mkdtemp()
        a__     		=     os.path.join(lowerCAmelCase      ,      str(uuid.uuida()   ) + ".png"   )

        img.save(self._path   )

        return self._path







class 					UpperCAmelCase_				(							_a    ):




    def __init__(					self  :   List[Any]      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   List[Any]=16000   )	-> str:
      super().__init__(lowerCAmelCase   )

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

      a__     		=     None
      a__     		=     None

      a__     		=     samplerate

      if isinstance(lowerCAmelCase      ,      (str, pathlib.Path)   ):
        a__     		=     value
      elif isinstance(lowerCAmelCase      ,      torch.Tensor   ):
        a__     		=     value
      else:
        raise ValueError(f"Unsupported audio type: {type(lowerCAmelCase   )}"   )




    def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   Any=None      ,      lowerCAmelCase  :   Tuple=None   )	-> int:
      from IPython.display import Audio, display

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




    def 						__lowerCamelCase (					self  :   Any   )	-> int:
      if self._tensor is not None:
        return self._tensor

      if self._path is not None:
        a__		,a__     		=     sf.read(self._path   )
        a__     		=     torch.tensor(lowerCAmelCase   )
        return self._tensor




    def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> Union[str, Any]:
      if self._path is not None:
        return self._path

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

_SCREAMING_SNAKE_CASE   						=    {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
_SCREAMING_SNAKE_CASE   						=    {str: AgentText}

if is_vision_available():
     _SCREAMING_SNAKE_CASE   						=    AgentImage
def 				__lowercase	(						*lowerCamelCase__ ,				**lowerCamelCase__					)				->List[str]:

  '''simple docstring'''




  a__     		=     [(arg.to_raw() if isinstance(lowerCamelCase__ ,				lowerCamelCase__					) else arg) for arg in args]
  a__     		=     {k: (v.to_raw() if isinstance(lowerCamelCase__ ,				lowerCamelCase__					) else v) for k, v in kwargs.items()}
  return args, kwargs







def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__=None					)				->Union[str, Any]:

  '''simple docstring'''




  if isinstance(lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     {}
    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:
          a__     		=     AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase__					)
        else:
          a__     		=     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					):
            a__     		=     _v(lowerCamelCase__					)
        if k not in decoded_outputs:
          a__     		=     AgentType[v]

  elif isinstance(lowerCamelCase__ ,				(list, tuple)					):
    a__     		=     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
        a__     		=     False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowerCamelCase__ ,				_k					):
            decoded_outputs.append(_v(lowerCamelCase__					)					)
            a__     		=     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
      a__     		=     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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  sequence.append(lowerCamelCase__					)

  a__     		=     0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(lowerCamelCase__					):
    return None

  return index


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
     _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]

     _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
     _SCREAMING_SNAKE_CASE   						=    int(target_input)
     _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
     if result is not None:
          print(F"""{target} found at positions: {result}""")
     else:
          print("""Not found""")

