

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__     :					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:
       UpperCamelCase__     :					List[str]								=			object

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf



class 					__snake_case     :

  def __init__(    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  value

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

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






class 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):

  def   lowerCAmelCase__   (    self):
      return self._value


  def   lowerCAmelCase__   (    self):
      return self._value






class 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):

  def __init__(    self  ,						_A):
      super().__init__(_A)

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

      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None

      if isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  value
      elif isinstance(_A  ,						(str, pathlib.Path)):
          SCREAMING_SNAKE_CASE_													=  value
      elif isinstance(_A  ,						torch.Tensor):
          SCREAMING_SNAKE_CASE_													=  value
      else:
          raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(_A)}""")

  def   lowerCAmelCase__   (    self  ,						_A=None  ,						_A=None):
      from IPython.display import Image, display

      display(Image(self.to_string()))

  def   lowerCAmelCase__   (    self):
      if self._raw is not None:
          return self._raw

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


  def   lowerCAmelCase__   (    self):
      if self._path is not None:
          return self._path

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

          return self._path

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

          # There is likely simpler than load into image into save
          SCREAMING_SNAKE_CASE_													=  Image.fromarray((array * 255).astype(np.uinta))

          SCREAMING_SNAKE_CASE_													=  tempfile.mkdtemp()
          SCREAMING_SNAKE_CASE_													=  os.path.join(_A  ,						str(uuid.uuida()) + '.png')

          img.save(self._path)

          return self._path






class 					__snake_case     (			lowerCAmelCase__				):

  def __init__(    self  ,						_A  ,						_A=16000):
      super().__init__(_A)

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

      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None

      SCREAMING_SNAKE_CASE_													=  samplerate

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

  def   lowerCAmelCase__   (    self  ,						_A=None  ,						_A=None):
      from IPython.display import Audio, display

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

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

      if self._path is not None:
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  sf.read(self._path)
          SCREAMING_SNAKE_CASE_													=  torch.tensor(_A)
          return self._tensor


  def   lowerCAmelCase__   (    self):
      if self._path is not None:
          return self._path

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






UpperCamelCase__     :					Union[str, Any]								=			{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCamelCase__     :					Optional[int]								=			{str: AgentText}

if is_vision_available():
       UpperCamelCase__     :					Dict								=			AgentImage


def     _UpperCAmelCase				(     *_SCREAMING_SNAKE_CASE    :						Any       ,      **_SCREAMING_SNAKE_CASE    :						Tuple				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  [(arg.to_raw() if isinstance(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				) else arg) for arg in args]
    SCREAMING_SNAKE_CASE_													=  {k: (v.to_raw() if isinstance(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				) else v) for k, v in kwargs.items()}
    return args, kwargs





def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Dict=None				):





    """simple docstring"""


    if isinstance(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				):
        SCREAMING_SNAKE_CASE_													=  {}
        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_													=  AGENT_TYPE_MAPPING[output_types[i]](_SCREAMING_SNAKE_CASE				)
                else:
                    SCREAMING_SNAKE_CASE_													=  AgentType(_SCREAMING_SNAKE_CASE				)

            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(_SCREAMING_SNAKE_CASE       ,      _k				):
                        SCREAMING_SNAKE_CASE_													=  _v(_SCREAMING_SNAKE_CASE				)
                if k not in decoded_outputs:
                    SCREAMING_SNAKE_CASE_													=  AgentType[v]

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

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

    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_													=  AGENT_TYPE_MAPPING[output_types[0]](_SCREAMING_SNAKE_CASE				)

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

    return decoded_outputs





from itertools import permutations


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  list(map(''.join       ,      permutations('0123456789'				)				)				)
    return result[999_999]


if __name__ == "__main__":
       print(solution())


