






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_ :


							'''simple docstring'''






							def __init__(		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     value

							def __str__(		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return self.to_string()

							def      _lowercase       (		self   )					->					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      _lowercase       (		self   )					->					Any:
														'''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_ (						__snake_case	,	__snake_case		):


							'''simple docstring'''






							def      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														return self._value






							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														return self._value




class 							a_ (						__snake_case	,	__snake_case		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														super().__init__(lowercase_   )

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

														lowerCAmelCase_										=     None
														lowerCAmelCase_										=     None
														lowerCAmelCase_										=     None

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

							def      _lowercase       (		self			,			lowercase_=None			,			lowercase_=None   )					->					Any:
														'''simple docstring'''

														from IPython.display import Image, display

														display(Image(self.to_string()   )   )

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

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

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






							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

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

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

																					return self._path

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

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

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

																					img.save(self._path   )

																					return self._path




class 							a_ (						__snake_case		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=1_6_0_0_0   )					->					List[Any]:
														'''simple docstring'''

														super().__init__(lowercase_   )

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

														lowerCAmelCase_										=     None
														lowerCAmelCase_										=     None

														lowerCAmelCase_										=     samplerate

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

							def      _lowercase       (		self			,			lowercase_=None			,			lowercase_=None   )					->					Any:
														'''simple docstring'''

														from IPython.display import Audio, display

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

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

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

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






							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

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

														if self._tensor is not None:
																					lowerCAmelCase_										=     tempfile.mkdtemp()
																					lowerCAmelCase_										=     os.path.join(lowercase_			,			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      lowerCamelCase					(						*a_ ,						**a_	)	->			List[Any]:
							lowerCAmelCase_										=     [(arg.to_raw() if isinstance(__lowercase ,						__lowercase	) else arg) for arg in args]
							lowerCAmelCase_										=     {k: (v.to_raw() if isinstance(__lowercase ,						__lowercase	) else v) for k, v in kwargs.items()}
							return args, kwargs



def      lowerCamelCase					(						a_ ,						a_=None	)	->			Union[str, Any]:



							if isinstance(__lowercase ,						__lowercase	):
														lowerCAmelCase_										=     {}
														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_										=     AGENT_TYPE_MAPPING[output_types[i]](__lowercase	)
																												else:
																																			lowerCAmelCase_										=     AgentType(__lowercase	)

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

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

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

							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_										=     AGENT_TYPE_MAPPING[output_types[0]](__lowercase	)

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

							return decoded_outputs




def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						if not isinstance(__lowercase					,			__lowercase						) or number < 0:
												raise ValueError("Input must be a non-negative integer"						)

						_snake_case						:     List[Any]				=    number & ~(number - 1)
						_snake_case						:     Union[str, Any]				=    0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)