





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


_UpperCamelCase   :							Optional[Any]     =	logging.get_logger(__name__)

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

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase     )-> Optional[int]:
     __A				 =       value

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''     )
     return self._value





  def 		SCREAMING_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 						_lowerCAmelCase(						__UpperCAmelCase		,   __UpperCAmelCase):

  """simple docstring"""







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





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     return self._value







class 						_lowerCAmelCase(						__UpperCAmelCase		,   __UpperCAmelCase):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase     )-> List[str]:
     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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase=None  ,    UpperCAmelCase=None     )-> Dict:
     from IPython.display import Image, display

     display(Image(self.to_string()     )     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:



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

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





  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:



     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 * 2_55).astype(np.uinta     )     )

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

        img.save(self._path     )

        return self._path







class 						_lowerCAmelCase(						__UpperCAmelCase):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase=1_60_00     )-> Tuple:
     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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase=None  ,    UpperCAmelCase=None     )-> Union[str, Any]:
     from IPython.display import Audio, display

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:



     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 		SCREAMING_SNAKE_CASE__	( self     )-> List[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
_UpperCamelCase   :							Optional[int]     =	{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
_UpperCamelCase   :							List[Any]     =	{str: AgentText}

if is_vision_available():
 _UpperCamelCase   :							Union[str, Any]     =	AgentImage
def 				__UpperCamelCase  (		*snake_case    ,	**snake_case     )  ->						List[str]:


   '''simple docstring'''







   __A				 =       [(arg.to_raw() if isinstance(lowercase__    ,	lowercase__     ) else arg) for arg in args]
   __A				 =       {k: (v.to_raw() if isinstance(lowercase__    ,	lowercase__     ) else v) for k, v in kwargs.items()}
   return args, kwargs
def 				__UpperCamelCase  (		snake_case    ,	snake_case=None     )  ->						str:


   '''simple docstring'''







   if isinstance(lowercase__    ,	lowercase__     ):
      __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]](lowercase__     )
            else:
               __A				 =       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     ):
                  __A				 =       _v(lowercase__     )
            if k not in decoded_outputs:
               __A				 =       AgentType[v]

   elif isinstance(lowercase__    ,	(list, tuple)     ):
      __A				 =       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
            __A				 =       False
            for _k, _v in INSTANCE_TYPE_MAPPING.items():
               if isinstance(lowercase__    ,	_k     ):
                  decoded_outputs.append(_v(lowercase__     )     )
                  __A				 =       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
         __A				 =       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








from __future__ import annotations
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						list:


   '''simple docstring'''







   __A				 =       [None] * (len(snake_case     ) + len(snake_case     ))

   __A				 =       0  # pointer to current index for left Half
   __A				 =       0  # pointer to current index for the right Half
   __A				 =       0  # pointer to current index for the sorted array Half

   while pointera < len(snake_case     ) and pointera < len(snake_case     ):
      if left_half[pointera] < right_half[pointera]:
         __A				 =       left_half[pointera]
         pointera += 1
         index += 1
      else:
         __A				 =       right_half[pointera]
         pointera += 1
         index += 1
   while pointera < len(snake_case     ):
      __A				 =       left_half[pointera]
      pointera += 1
      index += 1

   while pointera < len(snake_case     ):
      __A				 =       right_half[pointera]
      pointera += 1
      index += 1

   return sorted_array
def 				__UpperCamelCase  (		snake_case     )  ->						list:


   '''simple docstring'''







   if len(snake_case     ) <= 1:
      return array
   # the actual formula to calculate the middle element = left + (right - left) // 2
   # this avoids integer overflow in case of large N
   __A				 =       0 + (len(snake_case     ) - 0) // 2

   # Split the array into halves till the array length becomes equal to One
   # merge the arrays of single length returned by mergeSort function and
   # pass them into the merge arrays function which merges the array
   __A				 =       array[:middle]
   __A				 =       array[middle:]

   return merge(merge_sort(snake_case     )    ,	merge_sort(snake_case     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

