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     						=			logging.get_logger(__name__)

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

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf




class 				__lowerCAmelCase					:



 '''simple docstring'''

 def __init__(     self    :						Optional[int]    ,     lowerCamelCase    :						List[str]					)	->							Any:
        _UpperCamelCase       :    List[str]					  =					value

 def __str__(     self    :						Optional[int]					)	->							Tuple:
        return self.to_string()

 def    __snake_case   (     self    :						int					)	->							Tuple:
        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    :						int					)	->							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					(    a_,		a_  ):



 '''simple docstring'''

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

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







class 				__lowerCAmelCase					(    a_,		a_  ):



 '''simple docstring'''

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

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

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

        if isinstance(lowerCamelCase    ,     lowerCamelCase					):
               _UpperCamelCase       :    Tuple					  =					value
        elif isinstance(lowerCamelCase    ,     (str, pathlib.Path)					):
               _UpperCamelCase       :    Union[str, Any]					  =					value
        elif isinstance(lowerCamelCase    ,     torch.Tensor					):
               _UpperCamelCase       :    Tuple					  =					value
        else:
               raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase					)}"					)

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						Optional[int]=None    ,     lowerCamelCase    :						Optional[Any]=None					)	->							str:
        from IPython.display import Image, display

        display(Image(self.to_string()					)					)

 def    __snake_case   (     self    :						int					)	->							Union[str, Any]:
        if self._raw is not None:
               return self._raw

        if self._path is not None:
               _UpperCamelCase       :    List[str]					  =					Image.open(self._path					)
               return self._raw

 def    __snake_case   (     self    :						str					)	->							Dict:
        if self._path is not None:
               return self._path

        if self._raw is not None:
               _UpperCamelCase       :    Union[str, Any]					  =					tempfile.mkdtemp()
               _UpperCamelCase       :    Any					  =					os.path.join(lowerCamelCase    ,     str(uuid.uuida()					) + '.png'					)
               self._raw.save(self._path					)

               return self._path

        if self._tensor is not None:
               _UpperCamelCase       :    Optional[int]					  =					self._tensor.cpu().detach().numpy()

               # There is likely simpler than load into image into save
               _UpperCamelCase       :    Optional[int]					  =					Image.fromarray((array * 2_5_5).astype(np.uinta					)					)

               _UpperCamelCase       :    List[Any]					  =					tempfile.mkdtemp()
               _UpperCamelCase       :    Tuple					  =					os.path.join(lowerCamelCase    ,     str(uuid.uuida()					) + '.png'					)

               img.save(self._path					)

               return self._path







class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						Optional[Any]    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Dict=1_6_0_0_0					)	->							Optional[int]:
        super().__init__(lowerCamelCase					)

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

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

        _UpperCamelCase       :    str					  =					samplerate

        if isinstance(lowerCamelCase    ,     (str, pathlib.Path)					):
               _UpperCamelCase       :    List[Any]					  =					value
        elif isinstance(lowerCamelCase    ,     torch.Tensor					):
               _UpperCamelCase       :    List[str]					  =					value
        else:
               raise ValueError(f"Unsupported audio type: {type(lowerCamelCase					)}"					)

 def    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						List[str]=None    ,     lowerCamelCase    :						Dict=None					)	->							Union[str, Any]:
        from IPython.display import Audio, display

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

 def    __snake_case   (     self    :						Dict					)	->							Union[str, Any]:
        if self._tensor is not None:
               return self._tensor

        if self._path is not None:
               _UpperCamelCase  , _UpperCamelCase       :    Dict					  =					sf.read(self._path					)
               _UpperCamelCase       :    List[Any]					  =					torch.tensor(lowerCamelCase					)
               return self._tensor

 def    __snake_case   (     self    :						str					)	->							Tuple:
        if self._path is not None:
               return self._path

        if self._tensor is not None:
               _UpperCamelCase       :    Optional[Any]					  =					tempfile.mkdtemp()
               _UpperCamelCase       :    Union[str, Any]					  =					os.path.join(lowerCamelCase    ,     str(uuid.uuida()					) + '.wav'					)
               sf.write(self._path    ,     self._tensor    ,     samplerate=self.samplerate					)
               return self._path







UpperCAmelCase     						=			{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
UpperCAmelCase     						=			{str: AgentText}

if is_vision_available():
 UpperCAmelCase     						=			AgentImage


def SCREAMING_SNAKE_CASE			(       *lowercase_:		Tuple						,  **lowercase_:		Any			):
       _UpperCamelCase       :    List[str]					  =					[(arg.to_raw() if isinstance(lowercase_						,  lowercase_			) else arg) for arg in args]
       _UpperCamelCase       :    Any					  =					{k: (v.to_raw() if isinstance(lowercase_						,  lowercase_			) else v) for k, v in kwargs.items()}
       return args, kwargs





def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		str=None			):
       if isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Tuple					  =					{}
              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       :    Any					  =					AGENT_TYPE_MAPPING[output_types[i]](lowercase_			)
                            else:
                                   _UpperCamelCase       :    str					  =					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       :    Dict					  =					_v(lowercase_			)
                            if k not in decoded_outputs:
                                   _UpperCamelCase       :    List[str]					  =					AgentType[v]

       elif isinstance(lowercase_						,  (list, tuple)			):
              _UpperCamelCase       :    Optional[int]					  =					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       :    int					  =					False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(lowercase_						,  _k			):
                                          decoded_outputs.append(_v(lowercase_			)			)
                                          _UpperCamelCase       :    List[str]					  =					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       :    int					  =					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




def SCREAMING_SNAKE_CASE			(       lowercase_:		int = 4000000			):
       _UpperCamelCase       :    str					  =					1
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Tuple					  =					0
       while j <= n:
              if j % 2 == 0:
                     total += j
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					j, i + j

       return total


if __name__ == "__main__":
 print(f'''{solution() = }''')



