'''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


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:
 snake_case__     :			Union[str, Any]      			=  object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf






class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      value


  def __str__( self						):




       '''simple docstring'''

       return self.to_string()


  def _UpperCamelCase     ( self						):




       '''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 _UpperCamelCase     ( self						):




       '''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      __SCREAMING_SNAKE_CASE						(       SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       return self._value






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       return self._value







class      __SCREAMING_SNAKE_CASE						(       SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       super().__init__(_SCREAMING_SNAKE_CASE						)

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

       UpperCAmelCase_							:   str   =      None
       UpperCAmelCase_							:   List[str]   =      None
       UpperCAmelCase_							:   Any   =      None

       if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						):
            UpperCAmelCase_							:   List[Any]   =      value
       elif isinstance(_SCREAMING_SNAKE_CASE      ,		(str, pathlib.Path)						):
            UpperCAmelCase_							:   List[str]   =      value
       elif isinstance(_SCREAMING_SNAKE_CASE      ,		torch.Tensor						):
            UpperCAmelCase_							:   Dict   =      value
       else:
            raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(_SCREAMING_SNAKE_CASE						)}'''						)


  def _UpperCamelCase     ( self      ,		snake_case_=None      ,		snake_case_=None						):




       '''simple docstring'''

       from IPython.display import Image, display

       display(Image(self.to_string()						)						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

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






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

       if self._raw is not None:
            UpperCAmelCase_							:   List[Any]   =      tempfile.mkdtemp()
            UpperCAmelCase_							:   Dict   =      os.path.join(_SCREAMING_SNAKE_CASE      ,		str(uuid.uuida()						) + '.png'						)
            self._raw.save(self._path						)

            return self._path

       if self._tensor is not None:
            UpperCAmelCase_							:   Optional[int]   =      self._tensor.cpu().detach().numpy()

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

            UpperCAmelCase_							:   Optional[Any]   =      tempfile.mkdtemp()
            UpperCAmelCase_							:   List[Any]   =      os.path.join(_SCREAMING_SNAKE_CASE      ,		str(uuid.uuida()						) + '.png'						)

            img.save(self._path						)

            return self._path







class      __SCREAMING_SNAKE_CASE						(       SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_=1_6_0_0_0						):




       '''simple docstring'''

       super().__init__(_SCREAMING_SNAKE_CASE						)

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

       UpperCAmelCase_							:   str   =      None
       UpperCAmelCase_							:   str   =      None

       UpperCAmelCase_							:   Union[str, Any]   =      samplerate

       if isinstance(_SCREAMING_SNAKE_CASE      ,		(str, pathlib.Path)						):
            UpperCAmelCase_							:   Union[str, Any]   =      value
       elif isinstance(_SCREAMING_SNAKE_CASE      ,		torch.Tensor						):
            UpperCAmelCase_							:   Optional[int]   =      value
       else:
            raise ValueError(F'''Unsupported audio type: {type(_SCREAMING_SNAKE_CASE						)}'''						)


  def _UpperCamelCase     ( self      ,		snake_case_=None      ,		snake_case_=None						):




       '''simple docstring'''

       from IPython.display import Audio, display

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

       if self._path is not None:
            UpperCAmelCase_							:   Optional[int]   =      sf.read(self._path						)
            UpperCAmelCase_							:   Optional[int]   =      torch.tensor(_SCREAMING_SNAKE_CASE						)
            return self._tensor






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

       if self._tensor is not None:
            UpperCAmelCase_							:   Optional[int]   =      tempfile.mkdtemp()
            UpperCAmelCase_							:   str   =      os.path.join(_SCREAMING_SNAKE_CASE      ,		str(uuid.uuida()						) + '.wav'						)
            sf.write(self._path      ,		self._tensor      ,		samplerate=self.samplerate						)
            return self._path







snake_case__     :			str      			=  {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case__     :			Tuple      			=  {str: AgentText}

if is_vision_available():
 snake_case__     :			Any      			=  AgentImage


def 					_lowerCamelCase    (      *lowerCamelCase_					:							List[Any]	,     **lowerCamelCase_					:							Dict   ):



     """simple docstring"""





     UpperCAmelCase_							:   Union[str, Any]   =      [(arg.to_raw() if isinstance(_a	,     _a   ) else arg) for arg in args]
     UpperCAmelCase_							:   int   =      {k: (v.to_raw() if isinstance(_a	,     _a   ) else v) for k, v in kwargs.items()}
     return args, kwargs





def 					_lowerCamelCase    (      lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Optional[int]=None   ):



     """simple docstring"""





     if isinstance(_a	,     _a   ):
          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_							:   int   =      AGENT_TYPE_MAPPING[output_types[i]](_a   )
                    else:
                         UpperCAmelCase_							:   Any   =      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   ):
                              UpperCAmelCase_							:   Optional[Any]   =      _v(_a   )
                    if k not in decoded_outputs:
                         UpperCAmelCase_							:   int   =      AgentType[v]

     elif isinstance(_a	,     (list, tuple)   ):
          UpperCAmelCase_							:   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
                    UpperCAmelCase_							:   Any   =      False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(_a	,     _k   ):
                              decoded_outputs.append(_v(_a   )   )
                              UpperCAmelCase_							:   Optional[Any]   =      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
               UpperCAmelCase_							:   int   =      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 math

def    lowerCAmelCase__		(			_a				:	int			):

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5     ,					int(math.sqrt(_a			) + 1			)     ,					6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True

def    lowerCAmelCase__		(			_a				:	int = 60_08_51_47_51_43			):

		try:
				snake_case_ :							int         =				int(_a			)
		except (TypeError, ValueError):
				raise TypeError("Parameter n must be int or castable to int."			)
		if n <= 0:
				raise ValueError("Parameter n must be greater than or equal to one."			)
		snake_case_ :							Dict         =				0
		if is_prime(_a			):
				return n
		while n % 2 == 0:
				n //= 2
		if is_prime(_a			):
				return n
		for i in range(3     ,					int(math.sqrt(_a			)			) + 1     ,					2			):
				if n % i == 0:
						if is_prime(n // i			):
								snake_case_ :							List[Any]         =				n // i
								break
						elif is_prime(_a			):
								snake_case_ :							str         =				i
		return max_number


if __name__ == "__main__":
							print(F"""{solution() = }""")
