






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


a	: int      =				logging.get_logger(__name__)

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

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf
class 					a					:



      """simple docstring"""


      def __init__(   self      :    Tuple	,   __lowercase      :    List[str]      )							->   List[Any]:
        __UpperCAmelCase							:			Optional[int]     =						value
      def __str__(   self      :    List[Any]      )							->   List[Any]:
        return self.to_string()
      def 			UpperCAmelCase   (   self      :    Any      )							->   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      :    Any      )							->   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 					a					(				lowercase__							, lowercase__     ):



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Any:
        return self._value




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


class 					a					(				lowercase__							, lowercase__     ):



      """simple docstring"""


      def __init__(   self      :    Tuple	,   __lowercase      :    int      )							->   Optional[int]:
        super().__init__(__lowercase      )

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

        __UpperCAmelCase							:			Optional[int]     =						None
        __UpperCAmelCase							:			Optional[Any]     =						None
        __UpperCAmelCase							:			str     =						None

        if isinstance(__lowercase	,   __lowercase      ):
          __UpperCAmelCase							:			Tuple     =						value
        elif isinstance(__lowercase	,   (str, pathlib.Path)      ):
          __UpperCAmelCase							:			Optional[Any]     =						value
        elif isinstance(__lowercase	,   torch.Tensor      ):
          __UpperCAmelCase							:			List[Any]     =						value
        else:
          raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(__lowercase      )}"""      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    List[Any]=None      )							->   int:
        from IPython.display import Image, display

        display(Image(self.to_string()      )      )
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Optional[Any]:
        if self._raw is not None:
          return self._raw

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




      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   List[str]:
        if self._path is not None:
          return self._path

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

          return self._path

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

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

          __UpperCAmelCase							:			Optional[Any]     =						tempfile.mkdtemp()
          __UpperCAmelCase							:			int     =						os.path.join(__lowercase	,   str(uuid.uuida()      ) + """.png"""      )

          img.save(self._path      )

          return self._path


class 					a					(				lowercase__     ):



      """simple docstring"""


      def __init__(   self      :    Optional[int]	,   __lowercase      :    List[Any]	,   __lowercase      :    Optional[int]=16000      )							->   Union[str, Any]:
        super().__init__(__lowercase      )

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

        __UpperCAmelCase							:			List[Any]     =						None
        __UpperCAmelCase							:			Union[str, Any]     =						None

        __UpperCAmelCase							:			Union[str, Any]     =						samplerate

        if isinstance(__lowercase	,   (str, pathlib.Path)      ):
          __UpperCAmelCase							:			List[Any]     =						value
        elif isinstance(__lowercase	,   torch.Tensor      ):
          __UpperCAmelCase							:			str     =						value
        else:
          raise ValueError(f"""Unsupported audio type: {type(__lowercase      )}"""      )
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    Union[str, Any]=None	,   __lowercase      :    Tuple=None      )							->   int:
        from IPython.display import Audio, display

        display(Audio(self.to_string()	,   rate=self.samplerate      )      )
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   Optional[int]:
        if self._tensor is not None:
          return self._tensor

        if self._path is not None:
          __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						sf.read(self._path      )
          __UpperCAmelCase							:			Dict     =						torch.tensor(__lowercase      )
          return self._tensor




      def 			UpperCAmelCase   (   self      :    Any      )							->   Union[str, Any]:
        if self._path is not None:
          return self._path

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


a	: Dict      =				{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
a	: str      =				{str: AgentText}

if is_vision_available():
       a	: Any      =				AgentImage






def        lowerCamelCase__   (					*__lowerCamelCase    :  Optional[Any]						,	**__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			Tuple     =						[(arg.to_raw() if isinstance(__lowerCamelCase						,	__lowerCamelCase	) else arg) for arg in args]
  __UpperCAmelCase							:			Union[str, Any]     =						{k: (v.to_raw() if isinstance(__lowerCamelCase						,	__lowerCamelCase	) else v) for k, v in kwargs.items()}
  return args, kwargs







def        lowerCamelCase__   (					__lowerCamelCase    :  str						,	__lowerCamelCase    :  str=None	):
  if isinstance(__lowerCamelCase						,	__lowerCamelCase	):
    __UpperCAmelCase							:			Union[str, 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:
          __UpperCAmelCase							:			Optional[int]     =						AGENT_TYPE_MAPPING[output_types[i]](__lowerCamelCase	)
        else:
          __UpperCAmelCase							:			Optional[int]     =						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	):
            __UpperCAmelCase							:			Optional[int]     =						_v(__lowerCamelCase	)
        if k not in decoded_outputs:
          __UpperCAmelCase							:			int     =						AgentType[v]

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










import decimal






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						0
  __UpperCAmelCase							:			Tuple     =						decimal.Context(prec=105	)
  for i in range(2						,	100	):
    __UpperCAmelCase							:			Optional[int]     =						decimal.Decimal(__lowerCamelCase	)
    __UpperCAmelCase							:			Any     =						number.sqrt(__lowerCamelCase	)
    if len(str(__lowerCamelCase	)	) > 1:
      answer += int(str(__lowerCamelCase	)[0]	)
      __UpperCAmelCase							:			Optional[Any]     =						str(__lowerCamelCase	)[2:101]
      answer += sum(int(__lowerCamelCase	) for x in sqrt_number_str	)
  return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f"""{solution() = }""")


