





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


__A       : Union[str, Any]						    = logging.get_logger(__name__)

if is_vision_available():
	import PIL.Image
	from PIL import Image
	from PIL.Image import Image as ImageType
else:
	__A       : Union[str, Any]						    = object

if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf







class     lowerCamelCase:
					'''simple docstring'''


					def __init__(						self							,    snake_case_       ):
									_A    =       value




					def __str__(						self       ):
									return self.to_string()




					def 					lowerCAmelCase__		(						self       ):
									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       ):
									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 					lowerCAmelCase__		(						self       ):
									return self._value




					def 					lowerCAmelCase__		(						self       ):
									return self._value







class     lowerCamelCase(	UpperCamelCase__ ,   UpperCamelCase__  ):
					'''simple docstring'''


					def __init__(						self							,    snake_case_       ):
									super().__init__(snake_case_       )

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

									_A    =       None
									_A    =       None
									_A    =       None

									if isinstance(snake_case_							,    snake_case_       ):
													_A    =       value
									elif isinstance(snake_case_							,    (str, pathlib.Path)       ):
													_A    =       value
									elif isinstance(snake_case_							,    torch.Tensor       ):
													_A    =       value
									else:
													raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(snake_case_       )}"       )




					def 					lowerCAmelCase__		(						self							,    snake_case_=None							,    snake_case_=None       ):
									from IPython.display import Image, display

									display(Image(self.to_string()       )       )




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

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




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

									if self._raw is not None:
													_A    =       tempfile.mkdtemp()
													_A    =       os.path.join(snake_case_							,    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 * 255).astype(np.uinta       )       )

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

													img.save(self._path       )

													return self._path







class     lowerCamelCase(	UpperCamelCase__  ):
					'''simple docstring'''


					def __init__(						self							,    snake_case_							,    snake_case_=1_6000       ):
									super().__init__(snake_case_       )

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

									_A    =       None
									_A    =       None

									_A    =       samplerate

									if isinstance(snake_case_							,    (str, pathlib.Path)       ):
													_A    =       value
									elif isinstance(snake_case_							,    torch.Tensor       ):
													_A    =       value
									else:
													raise ValueError(F"Unsupported audio type: {type(snake_case_       )}"       )




					def 					lowerCAmelCase__		(						self							,    snake_case_=None							,    snake_case_=None       ):
									from IPython.display import Audio, display

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




					def 					lowerCAmelCase__		(						self       ):
									if self._tensor is not None:
													return self._tensor

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




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

									if self._tensor is not None:
													_A    =       tempfile.mkdtemp()
													_A    =       os.path.join(snake_case_							,    str(uuid.uuida()       ) + '.wav'       )
													sf.write(self._path							,    self._tensor							,    samplerate=self.samplerate       )
													return self._path




__A       : Optional[int]						    = {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__A       : Any						    = {str: AgentText}

if is_vision_available():
	__A       : Any						    = AgentImage





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   List[str]:




				"""simple docstring"""




				_A    =       [(arg.to_raw() if isinstance(__a       ,    __a							) else arg) for arg in args]
				_A    =       {k: (v.to_raw() if isinstance(__a       ,    __a							) else v) for k, v in kwargs.items()}
				return args, kwargs





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=None							)       ->   Optional[Any]:




				"""simple docstring"""




				if isinstance(__a       ,    __a							):
								_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]](__a							)
																else:
																				_A    =       AgentType(__a							)

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

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

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

				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]](__a							)

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

				return decoded_outputs












from ..utils import DummyObject, requires_backends


__A       : str						    = None





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    requires_backends(_SCREAMING_SNAKE_CASE       ,    ['sentencepiece', 'tokenizers']							)





