"""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


UpperCamelCase_       :       Dict							   =		logging.get_logger(__name__)

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

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf


class 			_lowercase		:

				def __init__(     self       :    Optional[Any]       ,    _lowercase       :    Optional[int]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		value

				def __str__(     self       :    Optional[int]     ):


										'''simple docstring'''
										return self.to_string()

				def        A (     self       :    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        A (     self       :    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 			_lowercase		(   __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):

				def        A (     self       :    Dict     ):


										'''simple docstring'''
										return self._value

				def        A (     self       :    Optional[Any]     ):


										'''simple docstring'''
										return self._value


class 			_lowercase		(   __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):

				def __init__(     self       :    List[Any]       ,    _lowercase       :    Optional[Any]     ):


										'''simple docstring'''
										super().__init__(UpperCamelCase__     )

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

										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		None

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

				def        A (     self       :    Optional[Any]       ,    _lowercase       :    Optional[Any]=None       ,    _lowercase       :    Optional[int]=None     ):


										'''simple docstring'''
										from IPython.display import Image, display

										display(Image(self.to_string()     )     )

				def        A (     self       :    Tuple     ):


										'''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        A (     self       :    str     ):


										'''simple docstring'''







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

										if self._raw is not None:
																UpperCAmelCase__						=		tempfile.mkdtemp()
																UpperCAmelCase__						=		os.path.join(UpperCamelCase__       ,    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 * 2_5_5).astype(np.uinta     )     )

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

																img.save(self._path     )

																return self._path


class 			_lowercase		(   __SCREAMING_SNAKE_CASE  ):

				def __init__(     self       :    int       ,    _lowercase       :    Dict       ,    _lowercase       :    Optional[int]=1_6_0_0_0     ):


										'''simple docstring'''
										super().__init__(UpperCamelCase__     )

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

										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		None

										UpperCAmelCase__						=		samplerate

										if isinstance(UpperCamelCase__       ,    (str, pathlib.Path)     ):
																UpperCAmelCase__						=		value
										elif isinstance(UpperCamelCase__       ,    torch.Tensor     ):
																UpperCAmelCase__						=		value
										else:
																raise ValueError(f'Unsupported audio type: {type(UpperCamelCase__     )}'     )

				def        A (     self       :    str       ,    _lowercase       :    Optional[int]=None       ,    _lowercase       :    Union[str, Any]=None     ):


										'''simple docstring'''
										from IPython.display import Audio, display

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

				def        A (     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(UpperCamelCase__     )
																return self._tensor

				def        A (     self       :    Any     ):


										'''simple docstring'''







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

										if self._tensor is not None:
																UpperCAmelCase__						=		tempfile.mkdtemp()
																UpperCAmelCase__						=		os.path.join(UpperCamelCase__       ,    str(uuid.uuida()     ) + """.wav"""     )
																sf.write(self._path       ,    self._tensor       ,    samplerate=self.samplerate     )
																return self._path
UpperCamelCase_       :       Tuple							   =		{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
UpperCamelCase_       :       int							   =		{str: AgentText}

if is_vision_available():
						UpperCamelCase_       :       str							   =		AgentImage


def   a__      (		*UpperCAmelCase__       :			Tuple		,			**UpperCAmelCase__       :			Any			):
						UpperCAmelCase__						=		[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__			) else arg) for arg in args]
						UpperCAmelCase__						=		{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__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			List[Any]=None			):


						if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__			):
												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]](SCREAMING_SNAKE_CASE__			)
																								else:
																														UpperCAmelCase__						=		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			):
																																				UpperCAmelCase__						=		_v(SCREAMING_SNAKE_CASE__			)
																								if k not in decoded_outputs:
																														UpperCAmelCase__						=		AgentType[v]

						elif isinstance(SCREAMING_SNAKE_CASE__		,			(list, tuple)			):
												UpperCAmelCase__						=		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
																								UpperCAmelCase__						=		False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(SCREAMING_SNAKE_CASE__		,			_k			):
																																				decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__			)			)
																																				UpperCAmelCase__						=		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
																		UpperCAmelCase__						=		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


"""simple docstring"""







def   a__      (		UpperCAmelCase__       :			list[int]			)->	int:
						UpperCAmelCase__						=		len(UpperCAmelCase__			)
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

