



"""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__						:							Union[str, Any]        =      logging.get_logger(__name__)

if is_vision_available():
     import PIL.Image
     from PIL import Image
     from PIL.Image import Image as ImageType
else:
     lowercase__						:							str        =      object

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf







class  UpperCamelCase__  :




      """simple docstring"""




      def __init__( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Any			):
        lowerCAmelCase_  :							List[Any]								=					value



      def __str__( self							: Any			):
        return self.to_string()



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





      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        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__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        return self._value





      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        return self._value







class  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      def __init__( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Tuple			):
        super().__init__(SCREAMING_SNAKE_CASE_			)

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

        lowerCAmelCase_  :							Tuple								=					None
        lowerCAmelCase_  :							Dict								=					None
        lowerCAmelCase_  :							int								=					None

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							List[Any]								=					value
        elif isinstance(SCREAMING_SNAKE_CASE_       ,     (str, pathlib.Path)			):
          lowerCAmelCase_  :							int								=					value
        elif isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							int								=					value
        else:
          raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_			)}"			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any       ,     SCREAMING_SNAKE_CASE_							: List[Any]=None       ,     SCREAMING_SNAKE_CASE_							: str=None			):
        from IPython.display import Image, display

        display(Image(self.to_string()			)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        if self._raw is not None:
          return self._raw

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





      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        if self._path is not None:
          return self._path

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

          return self._path

        if self._tensor is not None:
          lowerCAmelCase_  :							Optional[Any]								=					self._tensor.cpu().detach().numpy()

          # There is likely simpler than load into image into save
          lowerCAmelCase_  :							Optional[int]								=					Image.fromarray((array * 2_5_5).astype(np.uinta			)			)

          lowerCAmelCase_  :							Optional[int]								=					tempfile.mkdtemp()
          lowerCAmelCase_  :							int								=					os.path.join(SCREAMING_SNAKE_CASE_       ,     str(uuid.uuida()			) + '.png'			)

          img.save(self._path			)

          return self._path







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      def __init__( self							: int       ,     SCREAMING_SNAKE_CASE_							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=1_6_0_0_0			):
        super().__init__(SCREAMING_SNAKE_CASE_			)

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

        lowerCAmelCase_  :							Union[str, Any]								=					None
        lowerCAmelCase_  :							int								=					None

        lowerCAmelCase_  :							Optional[int]								=					samplerate

        if isinstance(SCREAMING_SNAKE_CASE_       ,     (str, pathlib.Path)			):
          lowerCAmelCase_  :							Union[str, Any]								=					value
        elif isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							Dict								=					value
        else:
          raise ValueError(F"Unsupported audio type: {type(SCREAMING_SNAKE_CASE_			)}"			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=None       ,     SCREAMING_SNAKE_CASE_							: Dict=None			):
        from IPython.display import Audio, display

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



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

        if self._path is not None:
          lowerCAmelCase_		,lowerCAmelCase_  :							List[str]								=					sf.read(self._path			)
          lowerCAmelCase_  :							Dict								=					torch.tensor(SCREAMING_SNAKE_CASE_			)
          return self._tensor





      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        if self._path is not None:
          return self._path

        if self._tensor is not None:
          lowerCAmelCase_  :							List[str]								=					tempfile.mkdtemp()
          lowerCAmelCase_  :							Tuple								=					os.path.join(SCREAMING_SNAKE_CASE_       ,     str(uuid.uuida()			) + '.wav'			)
          sf.write(self._path       ,     self._tensor       ,     samplerate=self.samplerate			)
          return self._path




lowercase__						:							Union[str, Any]        =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
lowercase__						:							List[Any]        =      {str: AgentText}

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

def 							UpperCamelCase_       (       *lowerCAmelCase__ :							Tuple       ,  **lowerCAmelCase__ :							List[Any]		)		->  List[Any]:

  """simple docstring"""




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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple=None		)		->  List[Any]:

  """simple docstring"""




  if isinstance(lowerCAmelCase__       ,  lowerCAmelCase__		):
    lowerCAmelCase_  :							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:
          lowerCAmelCase_  :							Dict								=					AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase__		)
        else:
          lowerCAmelCase_  :							List[Any]								=					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		):
            lowerCAmelCase_  :							Union[str, Any]								=					_v(lowerCAmelCase__		)
        if k not in decoded_outputs:
          lowerCAmelCase_  :							Optional[int]								=					AgentType[v]

  elif isinstance(lowerCAmelCase__       ,  (list, tuple)		):
    lowerCAmelCase_  :							str								=					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
        lowerCAmelCase_  :							Optional[Any]								=					False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowerCAmelCase__       ,  _k		):
            decoded_outputs.append(_v(lowerCAmelCase__		)		)
            lowerCAmelCase_  :							str								=					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
      lowerCAmelCase_  :							List[Any]								=					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










"""simple docstring"""







import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    'other, expected_equality'       ,  [
        (Version('1.0.0'		), True),
        ('1.0.0', True),
        (Version('2.0.0'		), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]       ,  )
def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[Any]		)		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							List[str]								=					Version('1.0.0'		)
  assert (version == other) is expected_equality
  assert (version != other) is not expected_equality
  assert (hash(lowerCAmelCase__		) == hash(lowerCAmelCase__		)) is expected_equality





