



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 						_snake_case :





 """simple docstring"""



 def __init__(  self		,      a   )		->							str:




   """simple docstring"""







   _A          =      value
 def __str__(  self   )		->							List[Any]:




   """simple docstring"""







   return self.to_string()
 def lowercase_						(  self   )		->							Any:




   """simple docstring"""







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



 def lowercase_						(  self   )		->							str:




   """simple docstring"""







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







class 						_snake_case (     lowerCamelCase   ,lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   return self._value



 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   return self._value







class 						_snake_case (     lowerCamelCase   ,lowerCamelCase			):





 """simple docstring"""



 def __init__(  self		,      a   )		->							List[str]:




   """simple docstring"""







   super().__init__(a   )

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

   _A          =      None
   _A          =      None
   _A          =      None

   if isinstance(a		,      a   ):
     _A          =      value
   elif isinstance(a		,      (str, pathlib.Path)   ):
     _A          =      value
   elif isinstance(a		,      torch.Tensor   ):
     _A          =      value
   else:
     raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(a   )}'''   )
 def lowercase_						(  self		,      a=None		,      a=None   )		->							List[str]:




   """simple docstring"""







   from IPython.display import Image, display

   display(Image(self.to_string()   )   )
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   if self._raw is not None:
     return self._raw

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



 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   if self._path is not None:
     return self._path

   if self._raw is not None:
     _A          =      tempfile.mkdtemp()
     _A          =      os.path.join(a		,      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 * 2_5_5).astype(np.uinta   )   )

     _A          =      tempfile.mkdtemp()
     _A          =      os.path.join(a		,      str(uuid.uuida()   ) + '''.png'''   )

     img.save(self._path   )

     return self._path







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def __init__(  self		,      a		,      a=1_6_0_0_0   )		->							Union[str, Any]:




   """simple docstring"""







   super().__init__(a   )

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

   _A          =      None
   _A          =      None

   _A          =      samplerate

   if isinstance(a		,      (str, pathlib.Path)   ):
     _A          =      value
   elif isinstance(a		,      torch.Tensor   ):
     _A          =      value
   else:
     raise ValueError(f'''Unsupported audio type: {type(a   )}'''   )
 def lowercase_						(  self		,      a=None		,      a=None   )		->							Optional[Any]:




   """simple docstring"""







   from IPython.display import Audio, display

   display(Audio(self.to_string()		,      rate=self.samplerate   )   )
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   if self._tensor is not None:
     return self._tensor

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



 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   if self._path is not None:
     return self._path

   if self._tensor is not None:
     _A          =      tempfile.mkdtemp()
     _A          =      os.path.join(a		,      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       UpperCAmelCase__	(							*__snake_case  ,			**__snake_case							)				->	Tuple:
  _A          =      [(arg.to_raw() if isinstance(__snake_case  ,			__snake_case							) else arg) for arg in args]
  _A          =      {k: (v.to_raw() if isinstance(__snake_case  ,			__snake_case							) else v) for k, v in kwargs.items()}
  return args, kwargs




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case=None							)				->	Tuple:
  if isinstance(__snake_case  ,			__snake_case							):
    _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]](__snake_case							)
        else:
          _A          =      AgentType(__snake_case							)

      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(__snake_case  ,			_k							):
            _A          =      _v(__snake_case							)
        if k not in decoded_outputs:
          _A          =      AgentType[v]

  elif isinstance(__snake_case  ,			(list, tuple)							):
    _A          =      type(__snake_case							)()
    for i, v in enumerate(__snake_case							):
      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]](__snake_case							)							)
        else:
          decoded_outputs.append(AgentType(__snake_case							)							)
      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(__snake_case  ,			_k							):
            decoded_outputs.append(_v(__snake_case							)							)
            _A          =      True

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

  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]](__snake_case							)

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

  return decoded_outputs




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case							)				->	list[int]:
  _A          =      [0] * len(__snake_case							)
  for i in range(1  ,			len(__snake_case							)							):
    _A          =      duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("""Zero amount of processes""")
        raise SystemExit(0)

    # duration time of all processes
    __lowerCamelCase								=   [1_9, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("""Unable to match all id's with their duration time""")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
    for i, process in enumerate(processes):
        print(
            f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
            f'''{turnaround_times[i]}'''
        )
    print(f'''Average waiting time = {average_waiting_time}''')
    print(f'''Average turn around time = {average_turnaround_time}''')