



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


SCREAMING_SNAKE_CASE							:Any											=       logging.get_logger(__name__)

if is_vision_available():
					import PIL.Image
					from PIL import Image
					from PIL.Image import Image as ImageType
else:
					SCREAMING_SNAKE_CASE							:List[Any]											=       object

if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				List[Any]				):
					__A         =				value






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






	def        UpperCamelCase_						( self      :				Tuple				):
					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]				):
					logger.error(
					    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"				)
					return str(self._value				)





class 							UpperCAmelCase   (						_a    ,    _a						):




	'''simple docstring'''





	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					return self._value







	def        UpperCamelCase_						( self      :				int				):
					return self._value





class 							UpperCAmelCase   (						_a    ,    _a						):




	'''simple docstring'''





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

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

					__A         =				None
					__A         =				None
					__A         =				None

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






	def        UpperCamelCase_						( self      :				Tuple							,A      :				Tuple=None							,A      :				int=None				):
					from IPython.display import Image, display

					display(Image(self.to_string()				)				)






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

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







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

					if self._raw is not None:
									__A         =				tempfile.mkdtemp()
									__A         =				os.path.join(A							,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 * 2_55).astype(np.uinta				)				)

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

									img.save(self._path				)

									return self._path





class 							UpperCAmelCase   (						_a						):




	'''simple docstring'''





	def __init__( self      :				List[Any]							,A      :				int							,A      :				List[Any]=1_60_00				):
					super().__init__(A				)

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

					__A         =				None
					__A         =				None

					__A         =				samplerate

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






	def        UpperCamelCase_						( self      :				List[str]							,A      :				Any=None							,A      :				Tuple=None				):
					from IPython.display import Audio, display

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






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

					if self._path is not None:
									__A    ,							__A         =				sf.read(self._path				)
									__A         =				torch.tensor(A				)
									return self._tensor







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

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





SCREAMING_SNAKE_CASE							:Any											=       {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
SCREAMING_SNAKE_CASE							:Tuple											=       {str: AgentText}

if is_vision_available():
					SCREAMING_SNAKE_CASE							:List[Any]											=       AgentImage


def 		UpperCAmelCase    (			*a_	,     **a_		)	->			List[str]:







				"""simple docstring"""



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


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







				"""simple docstring"""



				if isinstance(lowerCamelCase__	,     lowerCamelCase__		):
								__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]](lowerCamelCase__		)
																else:
																				__A         =				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		):
																								__A         =				_v(lowerCamelCase__		)
																if k not in decoded_outputs:
																				__A         =				AgentType[v]

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


def 		UpperCAmelCase    (			a_		)	->			int:







				"""simple docstring"""



				__A         =				str(a_		).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"		)
				__A         =				oct_string[0] == "-"
				if is_negative:
								__A         =				oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(a_		) <= 7 for char in oct_string		):
								raise ValueError("Non-octal value was passed to the function"		)
				__A         =				0
				for char in oct_string:
								__A         =				8 * decimal_number + int(a_		)
				if is_negative:
								__A         =				-decimal_number
				return decimal_number


if __name__ == "__main__":
					from doctest import testmod

					testmod()
