


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 			A						:






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  Optional[int]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Dict						=value






      def __str__(      self		:  Optional[int]				)      ->		Any:
             """simple docstring"""




             return self.to_string()






      def  lowerCamelCase (      self		:  str				)      ->		Dict:
             """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  lowerCamelCase (      self		:  str				)      ->		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 			A						(     __SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE  ):






      def  lowerCamelCase (      self		:  Dict				)      ->		int:
             """simple docstring"""




             return self._value






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             return self._value




class 			A						(     __SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE  ):






      def __init__(      self		:  List[Any]    ,     lowercase_		:  Optional[Any]				)      ->		Optional[int]:
             """simple docstring"""




             super().__init__(UpperCamelCase__				)

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

             _lowerCamelCase :	Optional[int]						=None
             _lowerCamelCase :	Any						=None
             _lowerCamelCase :	Union[str, Any]						=None

             if isinstance(UpperCamelCase__    ,     UpperCamelCase__				):
                    _lowerCamelCase :	List[str]						=value
             elif isinstance(UpperCamelCase__    ,     (str, pathlib.Path)				):
                    _lowerCamelCase :	List[str]						=value
             elif isinstance(UpperCamelCase__    ,     torch.Tensor				):
                    _lowerCamelCase :	str						=value
             else:
                    raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(UpperCamelCase__				)}'''				)






      def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  Optional[Any]=None    ,     lowercase_		:  Optional[int]=None				)      ->		List[Any]:
             """simple docstring"""




             from IPython.display import Image, display

             display(Image(self.to_string()				)				)






      def  lowerCamelCase (      self		:  Tuple				)      ->		Union[str, Any]:
             """simple docstring"""




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

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






      def  lowerCamelCase (      self		:  str				)      ->		Union[str, Any]:
             """simple docstring"""




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

             if self._raw is not None:
                    _lowerCamelCase :	int						=tempfile.mkdtemp()
                    _lowerCamelCase :	str						=os.path.join(UpperCamelCase__    ,     str(uuid.uuida()				) + '.png'				)
                    self._raw.save(self._path				)

                    return self._path

             if self._tensor is not None:
                    _lowerCamelCase :	Union[str, Any]						=self._tensor.cpu().detach().numpy()

                    # There is likely simpler than load into image into save
                    _lowerCamelCase :	Optional[int]						=Image.fromarray((array * 255).astype(np.uinta				)				)

                    _lowerCamelCase :	Union[str, Any]						=tempfile.mkdtemp()
                    _lowerCamelCase :	Optional[Any]						=os.path.join(UpperCamelCase__    ,     str(uuid.uuida()				) + '.png'				)

                    img.save(self._path				)

                    return self._path




class 			A						(     __SCREAMING_SNAKE_CASE  ):






      def __init__(      self		:  int    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]=1_6000				)      ->		Any:
             """simple docstring"""




             super().__init__(UpperCamelCase__				)

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

             _lowerCamelCase :	Any						=None
             _lowerCamelCase :	int						=None

             _lowerCamelCase :	int						=samplerate

             if isinstance(UpperCamelCase__    ,     (str, pathlib.Path)				):
                    _lowerCamelCase :	Dict						=value
             elif isinstance(UpperCamelCase__    ,     torch.Tensor				):
                    _lowerCamelCase :	Union[str, Any]						=value
             else:
                    raise ValueError(F'''Unsupported audio type: {type(UpperCamelCase__				)}'''				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Optional[int]=None    ,     lowercase_		:  Union[str, Any]=None				)      ->		Tuple:
             """simple docstring"""




             from IPython.display import Audio, display

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






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




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

             if self._path is not None:
                    _lowerCamelCase :	Optional[Any]						=sf.read(self._path				)
                    _lowerCamelCase :	List[Any]						=torch.tensor(UpperCamelCase__				)
                    return self._tensor






      def  lowerCamelCase (      self		:  Any				)      ->		Any:
             """simple docstring"""




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

             if self._tensor is not None:
                    _lowerCamelCase :	Any						=tempfile.mkdtemp()
                    _lowerCamelCase :	Optional[Any]						=os.path.join(UpperCamelCase__    ,     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 			a_   (					*SCREAMING_SNAKE_CASE__      :   Any	,    **SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) else arg) for arg in args]
       _lowerCamelCase :	Any						={k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) else v) for k, v in kwargs.items()}
       return args, kwargs




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   List[Any]=None  ):
       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              _lowerCamelCase :	Union[str, 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:
                                   _lowerCamelCase :	Any						=AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__  )
                            else:
                                   _lowerCamelCase :	List[str]						=AgentType(SCREAMING_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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                          _lowerCamelCase :	List[Any]						=_v(SCREAMING_SNAKE_CASE__  )
                            if k not in decoded_outputs:
                                   _lowerCamelCase :	Dict						=AgentType[v]

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

                            if not found:
                                   decoded_outputs.append(AgentType(SCREAMING_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
                     _lowerCamelCase :	int						=AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                   return _v(SCREAMING_SNAKE_CASE__  )
                     return AgentType(SCREAMING_SNAKE_CASE__  )

       return decoded_outputs






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


