




"""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


_lowercase						   =     logging.get_logger(__name__)

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

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf

class    __lowercase							:


				'''simple docstring'''


				def __init__( self :  str   ,SCREAMING_SNAKE_CASE_ :  List[str]   ):
										snake_case__							:       Any				      =       value

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

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







class    __lowercase							(      snake_case_    , snake_case_						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										return self._value





				def    _lowerCamelCase       ( self :  Tuple   ):
										return self._value







class    __lowercase							(      snake_case_    , snake_case_						):


				'''simple docstring'''


				def __init__( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  Dict   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case__							:       Union[str, Any]				      =       None
										snake_case__							:       int				      =       None
										snake_case__							:       Tuple				      =       None

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

				def    _lowerCamelCase       ( self :  List[Any]   ,SCREAMING_SNAKE_CASE_ :  Dict=None   ,SCREAMING_SNAKE_CASE_ :  Dict=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )

				def    _lowerCamelCase       ( self :  Any   ):
										if self._raw is not None:
																return self._raw

										if self._path is not None:
																snake_case__							:       Union[str, Any]				      =       Image.open(self._path   )
																return self._raw





				def    _lowerCamelCase       ( self :  Any   ):
										if self._path is not None:
																return self._path

										if self._raw is not None:
																snake_case__							:       Tuple				      =       tempfile.mkdtemp()
																snake_case__							:       Any				      =       os.path.join(SCREAMING_SNAKE_CASE_   ,str(uuid.uuida()   ) + '''.png'''   )
																self._raw.save(self._path   )

																return self._path

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

																# There is likely simpler than load into image into save
																snake_case__							:       Any				      =       Image.fromarray((array * 2_5_5).astype(np.uinta   )   )

																snake_case__							:       List[Any]				      =       tempfile.mkdtemp()
																snake_case__							:       Optional[Any]				      =       os.path.join(SCREAMING_SNAKE_CASE_   ,str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path







class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				def __init__( self :  str   ,SCREAMING_SNAKE_CASE_ :  List[str]   ,SCREAMING_SNAKE_CASE_ :  List[str]=1_6_0_0_0   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case__							:       Optional[Any]				      =       None
										snake_case__							:       Dict				      =       None

										snake_case__							:       Optional[Any]				      =       samplerate

										if isinstance(SCREAMING_SNAKE_CASE_   ,(str, pathlib.Path)   ):
																snake_case__							:       Dict				      =       value
										elif isinstance(SCREAMING_SNAKE_CASE_   ,torch.Tensor   ):
																snake_case__							:       Dict				      =       value
										else:
																raise ValueError(F'''Unsupported audio type: {type(SCREAMING_SNAKE_CASE_   )}'''   )

				def    _lowerCamelCase       ( self :  int   ,SCREAMING_SNAKE_CASE_ :  Tuple=None   ,SCREAMING_SNAKE_CASE_ :  int=None   ):
										from IPython.display import Audio, display

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

				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										if self._tensor is not None:
																return self._tensor

										if self._path is not None:
																snake_case__      ,						snake_case__							:       Tuple				      =       sf.read(self._path   )
																snake_case__							:       Union[str, Any]				      =       torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor





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

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







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

if is_vision_available():
							_lowercase						   =     AgentImage

def 		lowerCAmelCase				(			*lowerCamelCase   ,      **lowerCamelCase		):
						'''simple docstring'''


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

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase=None		):
						'''simple docstring'''


						if isinstance(lowerCamelCase   ,      lowerCamelCase		):
												snake_case__							:       List[str]				      =       {}
												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:
																														snake_case__							:       Optional[Any]				      =       AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase		)
																								else:
																														snake_case__							:       Dict				      =       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		):
																																				snake_case__							:       Tuple				      =       _v(lowerCamelCase		)
																								if k not in decoded_outputs:
																														snake_case__							:       Any				      =       AgentType[v]

						elif isinstance(lowerCamelCase   ,      (list, tuple)		):
												snake_case__							:       Dict				      =       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
																								snake_case__							:       str				      =       False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowerCamelCase   ,      _k		):
																																				decoded_outputs.append(_v(lowerCamelCase		)		)
																																				snake_case__							:       int				      =       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
																		snake_case__							:       Union[str, Any]				      =       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








"""simple docstring"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


