

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


UpperCAmelCase__	:       Any			      =      logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  UpperCAmelCase__	:       Any			      =      object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf







class     a__    :







  """simple docstring"""




  def __init__(     self      :	List[str]			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				value


  def __str__(     self      :	Optional[Any]						)   ->Tuple:
        """simple docstring"""







        return self.to_string()


  def 	_lowercase (     self      :	Tuple						)   ->Tuple:
        """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 	_lowercase (     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 str(self._value						)







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        return self._value




  def 	_lowercase (     self      :	Dict						)   ->str:
        """simple docstring"""







        return self._value







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







  """simple docstring"""




  def __init__(     self      :	int			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
        """simple docstring"""







        super().__init__(UpperCAmelCase__						)

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

        SCREAMING_SNAKE_CASE	: List[str]					       =				None
        SCREAMING_SNAKE_CASE	: List[str]					       =				None
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				None

        if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						):
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				value
        elif isinstance(UpperCAmelCase__			,			(str, pathlib.Path)						):
              SCREAMING_SNAKE_CASE	: List[str]					       =				value
        elif isinstance(UpperCAmelCase__			,			torch.Tensor						):
              SCREAMING_SNAKE_CASE	: Dict					       =				value
        else:
              raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(UpperCAmelCase__						)}"						)


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	List[str]=None			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        from IPython.display import Image, display

        display(Image(self.to_string()						)						)


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







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

        if self._path is not None:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				Image.open(self._path						)
              return self._raw




  def 	_lowercase (     self      :	Optional[int]						)   ->Union[str, Any]:
        """simple docstring"""







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

        if self._raw is not None:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				tempfile.mkdtemp()
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				os.path.join(UpperCAmelCase__			,			str(uuid.uuida()						) + """.png"""						)
              self._raw.save(self._path						)

              return self._path

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

              # There is likely simpler than load into image into save
              SCREAMING_SNAKE_CASE	: Tuple					       =				Image.fromarray((array * 2_5_5).astype(np.uinta						)						)

              SCREAMING_SNAKE_CASE	: str					       =				tempfile.mkdtemp()
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				os.path.join(UpperCAmelCase__			,			str(uuid.uuida()						) + """.png"""						)

              img.save(self._path						)

              return self._path







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Dict=1_6_0_0_0						)   ->str:
        """simple docstring"""







        super().__init__(UpperCAmelCase__						)

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

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				None
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				None

        SCREAMING_SNAKE_CASE	: List[Any]					       =				samplerate

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


  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	str=None			,			UpperCAmelCase__      :	Tuple=None						)   ->List[str]:
        """simple docstring"""







        from IPython.display import Audio, display

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


  def 	_lowercase (     self      :	Optional[Any]						)   ->str:
        """simple docstring"""







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

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




  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[Any]:
        """simple docstring"""







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

        if self._tensor is not None:
              SCREAMING_SNAKE_CASE	: Dict					       =				tempfile.mkdtemp()
              SCREAMING_SNAKE_CASE	: Tuple					       =				os.path.join(UpperCAmelCase__			,			str(uuid.uuida()						) + """.wav"""						)
              sf.write(self._path			,			self._tensor			,			samplerate=self.samplerate						)
              return self._path


UpperCAmelCase__	:       Optional[Any]			      =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
UpperCAmelCase__	:       Dict			      =      {str: AgentText}

if is_vision_available():
  UpperCAmelCase__	:       Optional[int]			      =      AgentImage






def    __lowercase (       *_A  ,      **_A     )       ->  int:
      SCREAMING_SNAKE_CASE	: List[str]					       =				[(arg.to_raw() if isinstance(_A  ,      _A     ) else arg) for arg in args]
      SCREAMING_SNAKE_CASE	: List[Any]					       =				{k: (v.to_raw() if isinstance(_A  ,      _A     ) else v) for k, v in kwargs.items()}
      return args, kwargs







def    __lowercase (       _A  ,      _A=None     )       ->  str:
      if isinstance(_A  ,      _A     ):
            SCREAMING_SNAKE_CASE	: 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:
                              SCREAMING_SNAKE_CASE	: Tuple					       =				AGENT_TYPE_MAPPING[output_types[i]](_A     )
                        else:
                              SCREAMING_SNAKE_CASE	: Optional[int]					       =				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	: Any					       =				_v(_A     )
                        if k not in decoded_outputs:
                              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				AgentType[v]

      elif isinstance(_A  ,      (list, tuple)     ):
            SCREAMING_SNAKE_CASE	: int					       =				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	: Dict					       =				False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_A  ,      _k     ):
                                    decoded_outputs.append(_v(_A     )     )
                                    SCREAMING_SNAKE_CASE	: Dict					       =				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	: Optional[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







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				inspect.getfile(accelerate.test_utils						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_sync.py"""]						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->str:
        """simple docstring"""







        debug_launcher(test_sync.main			,			num_processes=1						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        debug_launcher(test_sync.main						)


  @require_single_gpu
  def 	_lowercase (     self      :	str						)   ->Any:
        """simple docstring"""







        test_sync.main()




  @require_multi_gpu
  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(UpperCAmelCase__			,			env=os.environ.copy()						)




