'''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


_A		:      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:
 _A		:      str						   =object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase     ):
        lowercase				:							Dict									=   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 _lowerCAmelCase			(	snake_case							,   snake_case					):







      def      lowerCamelCase__      (  self     ):
        return self._value







      def      lowerCamelCase__      (  self     ):
        return self._value





class _lowerCAmelCase			(	snake_case							,   snake_case					):







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

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

        lowercase				:							Tuple									=   None
        lowercase				:							List[str]									=   None
        lowercase				:							Dict									=   None

        if isinstance(lowercase  ,    lowercase     ):
          lowercase				:							Any									=   value
        elif isinstance(lowercase  ,    (str, pathlib.Path)     ):
          lowercase				:							Optional[int]									=   value
        elif isinstance(lowercase  ,    torch.Tensor     ):
          lowercase				:							Optional[Any]									=   value
        else:
          raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(lowercase     )}"""     )







      def      lowerCamelCase__      (  self  ,    lowercase=None  ,    lowercase=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:
          lowercase				:							List[Any]									=   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:
          lowercase				:							Optional[int]									=   tempfile.mkdtemp()
          lowercase				:							Union[str, Any]									=   os.path.join(lowercase  ,    str(uuid.uuida()     ) + '''.png'''     )
          self._raw.save(self._path     )

          return self._path

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

          # There is likely simpler than load into image into save
          lowercase				:							Optional[Any]									=   Image.fromarray((array * 255).astype(np.uinta     )     )

          lowercase				:							Union[str, Any]									=   tempfile.mkdtemp()
          lowercase				:							Optional[int]									=   os.path.join(lowercase  ,    str(uuid.uuida()     ) + '''.png'''     )

          img.save(self._path     )

          return self._path





class _lowerCAmelCase			(	snake_case					):







      def __init__(  self  ,    lowercase  ,    lowercase=1_6000     ):
        super().__init__(lowercase     )

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

        lowercase				:							str									=   None
        lowercase				:							Optional[Any]									=   None

        lowercase				:							Optional[int]									=   samplerate

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







      def      lowerCamelCase__      (  self  ,    lowercase=None  ,    lowercase=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:
          lowercase				,      lowercase				:							int									=   sf.read(self._path     )
          lowercase				:							List[Any]									=   torch.tensor(lowercase     )
          return self._tensor







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

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





_A		:      str						   ={'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_A		:      Union[str, Any]						   ={str: AgentText}

if is_vision_available():
 _A		:      List[Any]						   =AgentImage







def 				__UpperCamelCase (					*__UpperCAmelCase  :					Dict ,   **__UpperCAmelCase  :					Optional[int]			)							->	int:
  lowercase				:							Dict									=   [(arg.to_raw() if isinstance(__UpperCAmelCase ,   __UpperCAmelCase			) else arg) for arg in args]
  lowercase				:							Optional[Any]									=   {k: (v.to_raw() if isinstance(__UpperCAmelCase ,   __UpperCAmelCase			) else v) for k, v in kwargs.items()}
  return args, kwargs







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					Any=None			)							->	int:
  if isinstance(__UpperCAmelCase ,   __UpperCAmelCase			):
    lowercase				:							Optional[int]									=   {}
    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:
          lowercase				:							Union[str, Any]									=   AGENT_TYPE_MAPPING[output_types[i]](__UpperCAmelCase			)
        else:
          lowercase				:							Dict									=   AgentType(__UpperCAmelCase			)

      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(__UpperCAmelCase ,   _k			):
            lowercase				:							Optional[Any]									=   _v(__UpperCAmelCase			)
        if k not in decoded_outputs:
          lowercase				:							Dict									=   AgentType[v]

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

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

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      lowercase				:							List[Any]									=   AGENT_TYPE_MAPPING[output_types[0]](__UpperCAmelCase			)

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

  return decoded_outputs
'''simple docstring'''
import math







def 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	Optional[int]:
  lowercase				:							Tuple									=   0
  if n % 2 == 0:
    count += 1
    while n % 2 == 0:
      lowercase				:							Optional[int]									=   int(n / 2			)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

  lowercase				:							Dict									=   3

  while i <= int(math.sqrt(__UpperCAmelCase			)			):
    if n % i == 0:
      count += 1
      while n % i == 0:
        lowercase				:							Dict									=   int(n / i			)
    lowercase				:							List[Any]									=   i + 2

  # this condition checks the prime
  # number n is greater than 2

  if n > 2:
    count += 1
  return count


if __name__ == "__main__":
 _A		:      List[Any]						   =5_1_2_4_2_1_8_3
 print(f"""The number of distinct prime factors is/are {exact_prime_factor_count(n)}""")
 print(f"""The value of log(log(n)) is {math.log(math.log(n)):.4f}""")