code: 
  
  
  
  
  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
  
  
  _A							    =	logging.get_logger(__name__)
  
  if is_vision_available():
  							import PIL.Image
  							from PIL import Image
  							from PIL.Image import Image as ImageType
  else:
  							_A							    =	object
  
  if is_torch_available():
  							import torch
  
  if is_soundfile_availble():
  							import soundfile as sf
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  
  
  
  
  				def __str__( self							)    ->      str:
  											return self.to_string()
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											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:
  											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__	):
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											return self._value
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											return self._value
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      str:
  											super().__init__(snake_case_							)
  
  											if not is_vision_available():
  																		raise ImportError('''PIL must be installed in order to handle images.'''							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: str	     =None
  
  											if isinstance(snake_case_		,		snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =value
  											elif isinstance(snake_case_		,		(str, pathlib.Path)							):
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =value
  											elif isinstance(snake_case_		,		torch.Tensor							):
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  											else:
  																		raise ValueError(F'Unsupported type for {self.__class__.__name__}: {type(snake_case_							)}'							)
  
  
  
  
  				def     __a		( self		,		snake_case_=None		,		snake_case_=None							)    ->      Union[str, Any]:
  											from IPython.display import Image, display
  
  											display(Image(self.to_string()							)							)
  
  
  
  
  				def     __a		( self							)    ->      int:
  											if self._raw is not None:
  																		return self._raw
  
  											if self._path is not None:
  																		SCREAMING_SNAKE_CASE			: Dict	     =Image.open(self._path							)
  																		return self._raw
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											if self._path is not None:
  																		return self._path
  
  											if self._raw is not None:
  																		SCREAMING_SNAKE_CASE			: Dict	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Any	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.png'''							)
  																		self._raw.save(self._path							)
  
  																		return self._path
  
  											if self._tensor is not None:
  																		SCREAMING_SNAKE_CASE			: int	     =self._tensor.cpu().detach().numpy()
  
  																		# There is likely simpler than load into image into save
  																		SCREAMING_SNAKE_CASE			: Any	     =Image.fromarray((array * 255).astype(np.uinta							)							)
  
  																		SCREAMING_SNAKE_CASE			: str	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.png'''							)
  
  																		img.save(self._path							)
  
  																		return self._path
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_=16_000							)    ->      int:
  											super().__init__(snake_case_							)
  
  											if not is_soundfile_availble():
  																		raise ImportError('''soundfile must be installed in order to handle audio.'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											SCREAMING_SNAKE_CASE			: int	     =samplerate
  
  											if isinstance(snake_case_		,		(str, pathlib.Path)							):
  																		SCREAMING_SNAKE_CASE			: Tuple	     =value
  											elif isinstance(snake_case_		,		torch.Tensor							):
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  											else:
  																		raise ValueError(F'Unsupported audio type: {type(snake_case_							)}'							)
  
  
  
  
  				def     __a		( self		,		snake_case_=None		,		snake_case_=None							)    ->      Union[str, Any]:
  											from IPython.display import Audio, display
  
  											display(Audio(self.to_string()		,		rate=self.samplerate							)							)
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											if self._tensor is not None:
  																		return self._tensor
  
  											if self._path is not None:
  																		SCREAMING_SNAKE_CASE			: str	     =sf.read(self._path							)
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor(snake_case_							)
  																		return self._tensor
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											if self._path is not None:
  																		return self._path
  
  											if self._tensor is not None:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Tuple	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.wav'''							)
  																		sf.write(self._path		,		self._tensor		,		samplerate=self.samplerate							)
  																		return self._path
  
  
  
  
  
  
  
  _A							    =	{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
  _A							    =	{str: AgentText}
  
  if is_vision_available():
  							_A							    =	AgentImage
  def 						lowerCAmelCase_       (	*__a	,	**__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =[(arg.to_raw() if isinstance(__a	,	__a ) else arg) for arg in args]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={k: (v.to_raw() if isinstance(__a	,	__a ) else v) for k, v in kwargs.items()}
  							return args, kwargs
  def 						lowerCAmelCase_       (	__a	,	__a=None ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if isinstance(__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     ={}
  														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			: int	     =AGENT_TYPE_MAPPING[output_types[i]](__a )
  																												else:
  																																			SCREAMING_SNAKE_CASE			: List[Any]	     =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			: Union[str, Any]	     =_v(__a )
  																												if k not in decoded_outputs:
  																																			SCREAMING_SNAKE_CASE			: str	     =AgentType[v]
  
  							elif isinstance(__a	,	(list, tuple) ):
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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			: List[Any]	     =False
  																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																																			if isinstance(__a	,	_k ):
  																																										decoded_outputs.append(_v(__a ) )
  																																										SCREAMING_SNAKE_CASE			: List[Any]	     =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			: Any	     =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
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  