






"""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


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  _SCREAMING_SNAKE_CASE :    Optional[int]    =						object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf






class 					a						:






    def __init__(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		int:
      lowerCamelCase_   					=						value






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






    def        UpperCamelCase	(	self					:							Dict )					->		Dict:
      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 )					->		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						( __snake_case  ,				__snake_case		):






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






    def        UpperCamelCase	(	self					:							str )					->		List[Any]:
      return self._value






class 					a						( __snake_case  ,				__snake_case		):






    def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		Dict:
      super().__init__(__SCREAMING_SNAKE_CASE )

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

      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						None

      if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        lowerCamelCase_   					=						value
      elif isinstance(__SCREAMING_SNAKE_CASE	,						(str, pathlib.Path) ):
        lowerCamelCase_   					=						value
      elif isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
        lowerCamelCase_   					=						value
      else:
        raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__SCREAMING_SNAKE_CASE )}''' )






    def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[str]=None	,						__SCREAMING_SNAKE_CASE					:							List[str]=None )					->		int:
      from IPython.display import Image, display

      display(Image(self.to_string() ) )






    def        UpperCamelCase	(	self					:							int )					->		Dict:
      if self._raw is not None:
        return self._raw

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






    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      if self._path is not None:
        return self._path

      if self._raw is not None:
        lowerCamelCase_   					=						tempfile.mkdtemp()
        lowerCamelCase_   					=						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_   					=						self._tensor.cpu().detach().numpy()

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

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

        img.save(self._path )

        return self._path






class 					a						( __snake_case		):






    def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							str=16000 )					->		List[Any]:
      super().__init__(__SCREAMING_SNAKE_CASE )

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

      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						None

      lowerCamelCase_   					=						samplerate

      if isinstance(__SCREAMING_SNAKE_CASE	,						(str, pathlib.Path) ):
        lowerCamelCase_   					=						value
      elif isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
        lowerCamelCase_   					=						value
      else:
        raise ValueError(F'''Unsupported audio type: {type(__SCREAMING_SNAKE_CASE )}''' )






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str]=None	,						__SCREAMING_SNAKE_CASE					:							Dict=None )					->		List[Any]:
      from IPython.display import Audio, display

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






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

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






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

      if self._tensor is not None:
        lowerCamelCase_   					=						tempfile.mkdtemp()
        lowerCamelCase_   					=						os.path.join(__SCREAMING_SNAKE_CASE	,						str(uuid.uuida() ) + '.wav' )
        sf.write(self._path	,						self._tensor	,						samplerate=self.samplerate )
        return self._path
_SCREAMING_SNAKE_CASE :    Optional[int]    =						{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_SCREAMING_SNAKE_CASE :    str    =						{str: AgentText}

if is_vision_available():
  _SCREAMING_SNAKE_CASE :    Any    =						AgentImage







def 					lowerCamelCase__	(							*_lowerCamelCase	:		Optional[Any]		,    **_lowerCamelCase	:		Any			)				->    List[str]:
  lowerCamelCase_   					=						[(arg.to_raw() if isinstance(_lowerCamelCase		,    _lowerCamelCase			) else arg) for arg in args]
  lowerCamelCase_   					=						{k: (v.to_raw() if isinstance(_lowerCamelCase		,    _lowerCamelCase			) else v) for k, v in kwargs.items()}
  return args, kwargs







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Union[str, Any]=None			)				->    List[str]:
  if isinstance(_lowerCamelCase		,    _lowerCamelCase			):
    lowerCamelCase_   					=						{}
    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_   					=						AGENT_TYPE_MAPPING[output_types[i]](_lowerCamelCase			)
        else:
          lowerCamelCase_   					=						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_   					=						_v(_lowerCamelCase			)
        if k not in decoded_outputs:
          lowerCamelCase_   					=						AgentType[v]

  elif isinstance(_lowerCamelCase		,    (list, tuple)			):
    lowerCamelCase_   					=						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_   					=						False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(_lowerCamelCase		,    _k			):
            decoded_outputs.append(_v(_lowerCamelCase			)			)
            lowerCamelCase_   					=						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_   					=						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"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




