


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


snake_case_  :     str							  =							logging.get_logger(__name__)

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

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf

class   A__							:




						def __init__(    self   : Optional[Any]			,      _a   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=value




						def __str__(    self   : Dict					)							->       Optional[Any]:


										"""simple docstring"""





										return self.to_string()




						def 							__UpperCamelCase			(    self   : Tuple					)							->       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[Any]					)							->       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   A__							(						UpperCamelCase__				,       UpperCamelCase__					):




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


										"""simple docstring"""





										return self._value




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[str]:


										"""simple docstring"""





										return self._value

class   A__							(						UpperCamelCase__				,       UpperCamelCase__					):




						def __init__(    self   : Tuple			,      _a   : List[str]					)							->       int:


										"""simple docstring"""





										super().__init__(_a					)

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

										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None

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




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Optional[Any]=None			,      _a   : Optional[Any]=None					)							->       List[Any]:


										"""simple docstring"""





										from IPython.display import Image, display

										display(Image(self.to_string()					)					)




						def 							__UpperCamelCase			(    self   : str					)							->       List[Any]:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase			(    self   : Any					)							->       Union[str, Any]:


										"""simple docstring"""





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

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

														return self._path

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

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

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

														img.save(self._path					)

														return self._path

class   A__							(						UpperCamelCase__					):




						def __init__(    self   : Union[str, Any]			,      _a   : Dict			,      _a   : Dict=1_6000					)							->       Optional[int]:


										"""simple docstring"""





										super().__init__(_a					)

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

										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None

										_SCREAMING_SNAKE_CASE     					=samplerate

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




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Union[str, Any]=None			,      _a   : int=None					)							->       Any:


										"""simple docstring"""





										from IPython.display import Audio, display

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




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


										"""simple docstring"""





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

										if self._tensor is not None:
														_SCREAMING_SNAKE_CASE     					=tempfile.mkdtemp()
														_SCREAMING_SNAKE_CASE     					=os.path.join(_a			,      str(uuid.uuida()					) + '''.wav'''					)
														sf.write(self._path			,      self._tensor			,      samplerate=self.samplerate					)
														return self._path
snake_case_  :     Dict							  =							{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case_  :     Dict							  =							{str: AgentText}

if is_vision_available():
							snake_case_  :     Union[str, Any]							  =							AgentImage







def   lowerCamelCase(				*a__		,**a__):
				_SCREAMING_SNAKE_CASE     					=[(arg.to_raw() if isinstance(a__		,a__) else arg) for arg in args]
				_SCREAMING_SNAKE_CASE     					={k: (v.to_raw() if isinstance(a__		,a__) else v) for k, v in kwargs.items()}
				return args, kwargs







def   lowerCamelCase(				a__		,a__=None):
				if isinstance(a__		,a__):
								_SCREAMING_SNAKE_CASE     					={}
								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:
																				_SCREAMING_SNAKE_CASE     					=AGENT_TYPE_MAPPING[output_types[i]](a__)
																else:
																				_SCREAMING_SNAKE_CASE     					=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):
																								_SCREAMING_SNAKE_CASE     					=_v(a__)
																if k not in decoded_outputs:
																				_SCREAMING_SNAKE_CASE     					=AgentType[v]

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

class   A__							(						metaclass=UpperCamelCase__					):
						UpperCAmelCase     =  ["torch", "librosa"]




						def __init__(    self   : Dict			,      *_a   : Union[str, Any]			,      **_a   : List[str]					)							->       Optional[int]:


										"""simple docstring"""





										requires_backends(self			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : Dict			,      *_a   : Tuple			,      **_a   : Tuple					)							->       Optional[int]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : List[Any]			,      *_a   : Any			,      **_a   : List[str]					)							->       Dict:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)

class   A__							(						metaclass=UpperCamelCase__					):
						UpperCAmelCase     =  ["torch", "librosa"]




						def __init__(    self   : List[Any]			,      *_a   : int			,      **_a   : int					)							->       Optional[Any]:


										"""simple docstring"""





										requires_backends(self			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : str			,      *_a   : Dict			,      **_a   : Optional[Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : List[str]			,      *_a   : Optional[int]			,      **_a   : Union[str, Any]					)							->       Optional[Any]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)