code: 
  
  
  
  
  
  """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
  
  
  lowercase__      =				logging.get_logger(__name__)
  
  if is_vision_available():
  					import PIL.Image
  					from PIL import Image
  					from PIL.Image import Image as ImageType
  else:
  					lowercase__      =				object
  
  if is_torch_available():
  					import torch
  
  if is_soundfile_availble():
  					import soundfile as sf
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Any		,    a_							:			Union[str, Any]			):
  				lowerCAmelCase_     :							str             =    value
  
  
  
  
  
  		def __str__(     self							:			List[Any]			):
  				return self.to_string()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				logger.error(
  				    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"			)
  				return self._value
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				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    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				return self._value
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				return self._value
  
  
  
  
  
  
  
  class 	__lowerCamelCase       (				__UpperCAmelCase				,							__UpperCAmelCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			Optional[int]			):
  				super().__init__(lowerCamelCase__			)
  
  				if not is_vision_available():
  						raise ImportError("PIL must be installed in order to handle images."			)
  
  				lowerCAmelCase_     :							List[Any]             =    None
  				lowerCAmelCase_     :							int             =    None
  				lowerCAmelCase_     :							int             =    None
  
  				if isinstance(lowerCamelCase__		,    lowerCamelCase__			):
  						lowerCAmelCase_     :							Any             =    value
  				elif isinstance(lowerCamelCase__		,    (str, pathlib.Path)			):
  						lowerCAmelCase_     :							Tuple             =    value
  				elif isinstance(lowerCamelCase__		,    torch.Tensor			):
  						lowerCAmelCase_     :							Any             =    value
  				else:
  						raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__			)}'''			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int		,    a_							:			Union[str, Any]=None		,    a_							:			List[str]=None			):
  				from IPython.display import Image, display
  
  				display(Image(self.to_string()			)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  
  
  
  				if self._raw is not None:
  						return self._raw
  
  				if self._path is not None:
  						lowerCAmelCase_     :							Any             =    Image.open(self._path			)
  						return self._raw
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  
  
  
  				if self._path is not None:
  						return self._path
  
  				if self._raw is not None:
  						lowerCAmelCase_     :							Any             =    tempfile.mkdtemp()
  						lowerCAmelCase_     :							List[Any]             =    os.path.join(lowerCamelCase__		,    str(uuid.uuida()			) + ".png"			)
  						self._raw.save(self._path			)
  
  						return self._path
  
  				if self._tensor is not None:
  						lowerCAmelCase_     :							Optional[int]             =    self._tensor.cpu().detach().numpy()
  
  						# There is likely simpler than load into image into save
  						lowerCAmelCase_     :							Optional[int]             =    Image.fromarray((array * 2_55).astype(np.uinta			)			)
  
  						lowerCAmelCase_     :							Union[str, Any]             =    tempfile.mkdtemp()
  						lowerCAmelCase_     :							str             =    os.path.join(lowerCamelCase__		,    str(uuid.uuida()			) + ".png"			)
  
  						img.save(self._path			)
  
  						return self._path
  
  
  
  
  
  
  
  class 	__lowerCamelCase       (				__UpperCAmelCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			List[str]		,    a_							:			Tuple=1_60_00			):
  				super().__init__(lowerCamelCase__			)
  
  				if not is_soundfile_availble():
  						raise ImportError("soundfile must be installed in order to handle audio."			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    None
  				lowerCAmelCase_     :							Union[str, Any]             =    None
  
  				lowerCAmelCase_     :							Dict             =    samplerate
  
  				if isinstance(lowerCamelCase__		,    (str, pathlib.Path)			):
  						lowerCAmelCase_     :							Union[str, Any]             =    value
  				elif isinstance(lowerCamelCase__		,    torch.Tensor			):
  						lowerCAmelCase_     :							Optional[int]             =    value
  				else:
  						raise ValueError(f'''Unsupported audio type: {type(lowerCamelCase__			)}'''			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]		,    a_							:			Union[str, Any]=None		,    a_							:			str=None			):
  				from IPython.display import Audio, display
  
  				display(Audio(self.to_string()		,    rate=self.samplerate			)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  
  
  
  				if self._tensor is not None:
  						return self._tensor
  
  				if self._path is not None:
  						lowerCAmelCase_       ,			lowerCAmelCase_     :							Dict             =    sf.read(self._path			)
  						lowerCAmelCase_     :							str             =    torch.tensor(lowerCamelCase__			)
  						return self._tensor
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  
  
  
  				if self._path is not None:
  						return self._path
  
  				if self._tensor is not None:
  						lowerCAmelCase_     :							str             =    tempfile.mkdtemp()
  						lowerCAmelCase_     :							List[Any]             =    os.path.join(lowerCamelCase__		,    str(uuid.uuida()			) + ".wav"			)
  						sf.write(self._path		,    self._tensor		,    samplerate=self.samplerate			)
  						return self._path
  
  
  
  
  
  
  
  lowercase__      =				{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
  lowercase__      =				{str: AgentText}
  
  if is_vision_available():
  					lowercase__      =				AgentImage
  
  def 	__lowerCamelCase			(						*__UpperCamelCase   , **__UpperCamelCase )		->					int:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							str             =    [(arg.to_raw() if isinstance(lowercase__   , lowercase__ ) else arg) for arg in args]
  		lowerCAmelCase_     :							Dict             =    {k: (v.to_raw() if isinstance(lowercase__   , lowercase__ ) else v) for k, v in kwargs.items()}
  		return args, kwargs
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=None )		->					int:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if isinstance(lowercase__   , lowercase__ ):
  				lowerCAmelCase_     :							Any             =    {}
  				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_     :							List[str]             =    AGENT_TYPE_MAPPING[output_types[i]](lowercase__ )
  								else:
  										lowerCAmelCase_     :							List[str]             =    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_     :							Optional[Any]             =    _v(lowercase__ )
  								if k not in decoded_outputs:
  										lowerCAmelCase_     :							int             =    AgentType[v]
  
  		elif isinstance(lowercase__   , (list, tuple) ):
  				lowerCAmelCase_     :							Dict             =    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_     :							List[str]             =    False
  								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  										if isinstance(lowercase__   , _k ):
  												decoded_outputs.append(_v(lowercase__ ) )
  												lowerCAmelCase_     :							Tuple             =    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_     :							int             =    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
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from .pipeline_kandinskya_a import KandinskyVaaPipeline
  from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
  from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
  from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
  from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
  from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
  from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
  