


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      value




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







								'''simple docstring'''




								return self.to_string()




							def        UpperCAmelCase (						self)   ->	int:







								'''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)   ->	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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return self._value







							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								return self._value


class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__)

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

								snake_case__  :						Optional[int]		=      None
								snake_case__  :						List[str]		=      None
								snake_case__  :						int		=      None

								if isinstance(lowerCamelCase__	,					lowerCamelCase__):
									snake_case__  :						str		=      value
								elif isinstance(lowerCamelCase__	,					(str, pathlib.Path)):
									snake_case__  :						Optional[Any]		=      value
								elif isinstance(lowerCamelCase__	,					torch.Tensor):
									snake_case__  :						Union[str, Any]		=      value
								else:
									raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__)}""")




							def        UpperCAmelCase (						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None)   ->	Tuple:







								'''simple docstring'''




								from IPython.display import Image, display

								display(Image(self.to_string()))




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




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

								if self._path is not None:
									snake_case__  :						Optional[Any]		=      Image.open(self._path)
									return self._raw







							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




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

								if self._raw is not None:
									snake_case__  :						Dict		=      tempfile.mkdtemp()
									snake_case__  :						Any		=      os.path.join(lowerCamelCase__	,					str(uuid.uuida()) + ".png")
									self._raw.save(self._path)

									return self._path

								if self._tensor is not None:
									snake_case__  :						Union[str, Any]		=      self._tensor.cpu().detach().numpy()

									# There is likely simpler than load into image into save
									snake_case__  :						Union[str, Any]		=      Image.fromarray((array * 255).astype(np.uinta))

									snake_case__  :						List[str]		=      tempfile.mkdtemp()
									snake_case__  :						Tuple		=      os.path.join(lowerCamelCase__	,					str(uuid.uuida()) + ".png")

									img.save(self._path)

									return self._path


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=16_000)   ->	Dict:







								'''simple docstring'''




								super().__init__(lowerCamelCase__)

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

								snake_case__  :						Any		=      None
								snake_case__  :						Dict		=      None

								snake_case__  :						Tuple		=      samplerate

								if isinstance(lowerCamelCase__	,					(str, pathlib.Path)):
									snake_case__  :						Dict		=      value
								elif isinstance(lowerCamelCase__	,					torch.Tensor):
									snake_case__  :						Optional[Any]		=      value
								else:
									raise ValueError(f"""Unsupported audio type: {type(lowerCamelCase__)}""")




							def        UpperCAmelCase (						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None)   ->	Tuple:







								'''simple docstring'''




								from IPython.display import Audio, display

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




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




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

								if self._path is not None:
									snake_case__,					snake_case__  :						Optional[Any]		=      sf.read(self._path)
									snake_case__  :						Optional[Any]		=      torch.tensor(lowerCamelCase__)
									return self._tensor







							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




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

								if self._tensor is not None:
									snake_case__  :						List[str]		=      tempfile.mkdtemp()
									snake_case__  :						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 			A__		(					*_UpperCAmelCase    :      Any				,    **_UpperCAmelCase    :      Any	)							->	List[str]:

	'''simple docstring'''


	snake_case__  :						List[str]		=      [(arg.to_raw() if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	) else arg) for arg in args]
	snake_case__  :						Optional[int]		=      {k: (v.to_raw() if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	) else v) for k, v in kwargs.items()}
	return args, kwargs



def 			A__		(					_UpperCAmelCase    :      int				,    _UpperCAmelCase    :      List[str]=None	)							->	str:

	'''simple docstring'''


	if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	):
		snake_case__  :						str		=      {}
		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:
					snake_case__  :						List[str]		=      AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase	)
				else:
					snake_case__  :						Optional[int]		=      AgentType(_UpperCAmelCase	)

			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(_UpperCAmelCase				,    _k	):
						snake_case__  :						List[str]		=      _v(_UpperCAmelCase	)
				if k not in decoded_outputs:
					snake_case__  :						str		=      AgentType[v]

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

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

	else:
		if output_types[0] in AGENT_TYPE_MAPPING:
			# If the class has defined outputs, we can map directly according to the class definition
			snake_case__  :						List[str]		=      AGENT_TYPE_MAPPING[output_types[0]](_UpperCAmelCase	)

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

	return decoded_outputs








def  _snake_case		(snake_case_					:     float  )							->		int:
  if num < 0:
    return -1
  return 1 if num else 0





def  _snake_case		()							->		None:
  assert signum(5  ) == 1
  assert signum(-5  ) == -1
  assert signum(0  ) == 0


if __name__ == "__main__":
      print(signum(1_2))
      print(signum(-1_2))
      print(signum(0))





