




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_      :							List[Any]    							=					logging.get_logger(__name__)

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

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class        __lowerCAmelCase					:







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




									"""simple docstring"""


									_UpperCAmelCase						=	value







			def __str__(			self       :     List[Any] ):




									"""simple docstring"""


									return self.to_string()







			def UpperCamelCase     (			self       :     List[str] ):




									"""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 UpperCamelCase     (			self       :     List[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        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__					):







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									return self._value







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									return self._value






class        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__					):







			def __init__(			self       :     Union[str, Any]       ,       snake_case__       :     str ):




									"""simple docstring"""


									super().__init__(_A )

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

									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	None

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







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Union[str, Any]=None       ,       snake_case__       :     Optional[Any]=None ):




									"""simple docstring"""


									from IPython.display import Image, display

									display(Image(self.to_string() ) )







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


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

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







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


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

									if self._raw is not None:
															_UpperCAmelCase						=	tempfile.mkdtemp()
															_UpperCAmelCase						=	os.path.join(_A       ,       str(uuid.uuida() ) + ".png" )
															self._raw.save(self._path )

															return self._path

									if self._tensor is not None:
															_UpperCAmelCase						=	self._tensor.cpu().detach().numpy()

															# There is likely simpler than load into image into save
															_UpperCAmelCase						=	Image.fromarray((array * 255).astype(np.uinta ) )

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

															img.save(self._path )

															return self._path






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     List[Any]       ,       snake_case__       :     Any       ,       snake_case__       :     str=16_000 ):




									"""simple docstring"""


									super().__init__(_A )

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

									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	None

									_UpperCAmelCase						=	samplerate

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







			def UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     Optional[int]=None       ,       snake_case__       :     Optional[Any]=None ):




									"""simple docstring"""


									from IPython.display import Audio, display

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







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


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

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







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


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

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




lowercase_      :							str    							=					{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowercase_      :							List[str]    							=					{str: AgentText}

if is_vision_available():
				lowercase_      :							Tuple    							=					AgentImage


def 				__SCREAMING_SNAKE_CASE    (     *snake_case_      ,       **snake_case_		):

						'''simple docstring'''


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





def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_=None		):

						'''simple docstring'''


						if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_		):
												_UpperCAmelCase						=	{}
												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						=	AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase_		)
																								else:
																														_UpperCAmelCase						=	AgentType(UpperCAmelCase_		)

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

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

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

						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						=	AGENT_TYPE_MAPPING[output_types[0]](UpperCAmelCase_		)

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

						return decoded_outputs





"""simple docstring"""







def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     str:

	'''simple docstring'''





	return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





