
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   						=     logging.get_logger(__name__)

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

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      value



       def __str__(							self	)				->				int:
          return self.to_string()



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          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:
          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__		(	__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          return self._value




       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          return self._value







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A	)				->				Tuple:
          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     _UpperCamelCase	(							self ,     _A=None ,     _A=None	)				->				Optional[int]:
          from IPython.display import Image, display

          display(Image(self.to_string()	)	)



       def     _UpperCamelCase	(							self	)				->				str:
          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     _UpperCamelCase	(							self	)				->				List[Any]:
          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 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=16000	)				->				List[Any]:
          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     _UpperCamelCase	(							self ,     _A=None ,     _A=None	)				->				Optional[int]:
          from IPython.display import Audio, display

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



       def     _UpperCamelCase	(							self	)				->				int:
          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     _UpperCamelCase	(							self	)				->				List[str]:
          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   						=     {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__UpperCAmelCase   						=     {str: AgentText}

if is_vision_available():
   __UpperCAmelCase   						=     AgentImage


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





def  A__ (       __lowerCamelCase,  __lowerCamelCase=None						):
   if isinstance(__lowerCamelCase,  __lowerCamelCase						):
      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]](__lowerCamelCase						)
            else:
               SCREAMING_SNAKE_CASE_	=      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						):
                  SCREAMING_SNAKE_CASE_	=      _v(__lowerCamelCase						)
            if k not in decoded_outputs:
               SCREAMING_SNAKE_CASE_	=      AgentType[v]

   elif isinstance(__lowerCamelCase,  (list, tuple)						):
      SCREAMING_SNAKE_CASE_	=      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
            SCREAMING_SNAKE_CASE_	=      False
            for _k, _v in INSTANCE_TYPE_MAPPING.items():
               if isinstance(__lowerCamelCase,  _k						):
                  decoded_outputs.append(_v(__lowerCamelCase						)						)
                  SCREAMING_SNAKE_CASE_	=      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
         SCREAMING_SNAKE_CASE_	=      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




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


