






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 	lowerCamelCase__				:






      def __init__(self					:    int		,		_snake_case					:    Tuple		)       ->   List[str]:


       """simple docstring"""






       lowerCamelCase_							:    str							=     value






      def __str__(self					:    int		)       ->   Union[str, Any]:


       """simple docstring"""






       return self.to_string()






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   int:


       """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					:    List[str]		)       ->   str:


       """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 	lowerCamelCase__				(							UpperCAmelCase,   UpperCAmelCase ):






      def      UpperCAmelCase_     (self					:    str		)       ->   int:


       """simple docstring"""






       return self._value






      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   Union[str, Any]:


       """simple docstring"""






       return self._value




class 	lowerCamelCase__				(							UpperCAmelCase,   UpperCAmelCase ):






      def __init__(self					:    List[Any]		,		_snake_case					:    Optional[int]		)       ->   Tuple:


       """simple docstring"""






       super().__init__(_snake_case		)

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

       lowerCamelCase_							:    Optional[Any]							=     None
       lowerCamelCase_							:    List[Any]							=     None
       lowerCamelCase_							:    Dict							=     None

       if isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    Optional[int]							=     value
       elif isinstance(_snake_case		,		(str, pathlib.Path)		):
        lowerCamelCase_							:    List[Any]							=     value
       elif isinstance(_snake_case		,		torch.Tensor		):
        lowerCamelCase_							:    int							=     value
       else:
        raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(_snake_case		)}'		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Union[str, Any]=None		,		_snake_case					:    Tuple=None		)       ->   Optional[Any]:


       """simple docstring"""






       from IPython.display import Image, display

       display(Image(self.to_string()		)		)






      def      UpperCAmelCase_     (self					:    Dict		)       ->   str:


       """simple docstring"""






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

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






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   List[Any]:


       """simple docstring"""






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

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

        return self._path

       if self._tensor is not None:
        lowerCamelCase_							:    int							=     self._tensor.cpu().detach().numpy()

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

        lowerCamelCase_							:    str							=     tempfile.mkdtemp()
        lowerCamelCase_							:    List[str]							=     os.path.join(_snake_case		,		str(uuid.uuida()		) + '.png'		)

        img.save(self._path		)

        return self._path




class 	lowerCamelCase__				(							UpperCAmelCase ):






      def __init__(self					:    List[Any]		,		_snake_case					:    List[str]		,		_snake_case					:    Optional[Any]=1_6000		)       ->   Any:


       """simple docstring"""






       super().__init__(_snake_case		)

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

       lowerCamelCase_							:    Optional[int]							=     None
       lowerCamelCase_							:    Any							=     None

       lowerCamelCase_							:    Dict							=     samplerate

       if isinstance(_snake_case		,		(str, pathlib.Path)		):
        lowerCamelCase_							:    Any							=     value
       elif isinstance(_snake_case		,		torch.Tensor		):
        lowerCamelCase_							:    Optional[Any]							=     value
       else:
        raise ValueError(f'Unsupported audio type: {type(_snake_case		)}'		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		,		_snake_case					:    Any=None		,		_snake_case					:    List[Any]=None		)       ->   List[Any]:


       """simple docstring"""






       from IPython.display import Audio, display

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






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Union[str, Any]:


       """simple docstring"""






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

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






      def      UpperCAmelCase_     (self					:    List[Any]		)       ->   List[str]:


       """simple docstring"""






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

       if self._tensor is not None:
        lowerCamelCase_							:    Optional[int]							=     tempfile.mkdtemp()
        lowerCamelCase_							:    Optional[Any]							=     os.path.join(_snake_case		,		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__				)  ->      int:
 lowerCamelCase_							:    Any							=     [(arg.to_raw() if isinstance(lowerCamelCase__ ,			lowerCamelCase__				) else arg) for arg in args]
 lowerCamelCase_							:    Tuple							=     {k: (v.to_raw() if isinstance(lowerCamelCase__ ,			lowerCamelCase__				) else v) for k, v in kwargs.items()}
 return args, kwargs





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

 elif isinstance(lowerCamelCase__ ,			(list, tuple)				):
  lowerCamelCase_							:    Optional[int]							=     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_							:    int							=     False
    for _k, _v in INSTANCE_TYPE_MAPPING.items():
     if isinstance(lowerCamelCase__ ,			_k				):
      decoded_outputs.append(_v(lowerCamelCase__				)				)
      lowerCamelCase_							:    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
   lowerCamelCase_							:    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








from __future__ import annotations




def       _a   (							lowerCamelCase__				)  ->      list[int]:
 lowerCamelCase_							:    Any							=     [0] * max_one

 for i in range(0 ,			lowerCamelCase__				):
  lowerCamelCase_							:    Union[str, Any]							=     i

 for i in range(2 ,			lowerCamelCase__				):
  if totients[i] == i:
   for j in range(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				):
    totients[j] -= totients[j] // i

 return totients




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      bool:
 return sorted(str(lowerCamelCase__				)				) == sorted(str(lowerCamelCase__				)				)




def       _a   (							lowerCamelCase__ = 10_00_00_00				)  ->      int:
 lowerCamelCase_							:    Tuple							=     1  # i
 lowerCamelCase_							:    Union[str, Any]							=     0  # φ(i)
 lowerCamelCase_							:    List[Any]							=     get_totients(max_n + 1				)

 for i in range(2 ,			max_n + 1				):
  lowerCamelCase_							:    Tuple							=     totients[i]

  if i * min_denominator < min_numerator * t and has_same_digits(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    str							=     i
   lowerCamelCase_							:    List[Any]							=     t

 return min_numerator


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