


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			:			Dict			      =					logging.get_logger(__name__)

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

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf



class  UpperCAmelCase_						:
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		value






       def __str__(  self    ):




              """simple docstring"""




              return self.to_string()






       def 			_lowercase (  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 			_lowercase (  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  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self._value






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self._value




class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_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     :						List[str] 						=		None
              UpperCamelCase     :						Any 						=		None
              UpperCamelCase     :						List[str] 						=		None

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              from IPython.display import Image, display

              display(Image(self.to_string()    )    )






       def 			_lowercase (  self    ):




              """simple docstring"""




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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




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

              if self._raw is not None:
                     UpperCamelCase     :						List[str] 						=		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     :						List[Any] 						=		Image.fromarray((array * 255).astype(np.uinta    )    )

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

                     img.save(self._path    )

                     return self._path




class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=16_000    ):




              """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     :						Optional[Any] 						=		None

              UpperCamelCase     :						str 						=		samplerate

              if isinstance(__SCREAMING_SNAKE_CASE			,       (str, pathlib.Path)    ):
                     UpperCamelCase     :						int 						=		value
              elif isinstance(__SCREAMING_SNAKE_CASE			,       torch.Tensor    ):
                     UpperCamelCase     :						str 						=		value
              else:
                     raise ValueError(f"""Unsupported audio type: {type(__SCREAMING_SNAKE_CASE    )}"""    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              from IPython.display import Audio, display

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






       def 			_lowercase (  self    ):




              """simple docstring"""




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

              if self._path is not None:
                     UpperCamelCase						,			UpperCamelCase     :						Dict 						=		sf.read(self._path    )
                     UpperCamelCase     :						List[Any] 						=		torch.tensor(__SCREAMING_SNAKE_CASE    )
                     return self._tensor






       def 			_lowercase (  self    ):




              """simple docstring"""




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

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




__UpperCAmelCase			:			int			      =					{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__UpperCAmelCase			:			Optional[Any]			      =					{str: AgentText}

if is_vision_available():
  __UpperCAmelCase			:			Any			      =					AgentImage


def   a						(				*SCREAMING_SNAKE_CASE_   :		int				,      **SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) else arg) for arg in args]
       UpperCamelCase     :						List[str] 						=		{k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) else v) for k, v in kwargs.items()}
       return args, kwargs


def   a						(				SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		int=None							):






       """simple docstring"""





       if isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							):
              UpperCamelCase     :						List[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     :						str 						=		AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE_							)
                            else:
                                   UpperCamelCase     :						Dict 						=		AgentType(SCREAMING_SNAKE_CASE_							)

                     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(SCREAMING_SNAKE_CASE_				,      _k							):
                                          UpperCamelCase     :						Dict 						=		_v(SCREAMING_SNAKE_CASE_							)
                            if k not in decoded_outputs:
                                   UpperCamelCase     :						Union[str, Any] 						=		AgentType[v]

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

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

       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     :						List[Any] 						=		AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE_							)

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

       return decoded_outputs





def   a						(				SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		[True for i in range(n + 1							)]
       UpperCamelCase     :						Tuple 						=		[i - 1 for i in range(n + 1							)]
       UpperCamelCase     :						Optional[Any] 						=		[]
       for i in range(2				,      n + 1							):
              if is_prime[i]:
                     primes.append(SCREAMING_SNAKE_CASE_							)
              for j in range(0				,      len(SCREAMING_SNAKE_CASE_							)							):
                     if i * primes[j] >= n:
                            break
                     UpperCamelCase     :						Any 						=		False

                     if i % primes[j] == 0:
                            UpperCamelCase     :						int 						=		totients[i] * primes[j]
                            break

                     UpperCamelCase     :						Dict 						=		totients[i] * (primes[j] - 1)

       return totients


if __name__ == "__main__":
  import doctest

  doctest.testmod()

