


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


__lowerCAmelCase									=				logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 __lowerCAmelCase									=				object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf





class     __a     :
 def __init__(  self							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  value
 def __str__(  self					)   ->							int:




        '''simple docstring'''
        return self.to_string()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							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     __a     (       __UpperCamelCase			,			__UpperCamelCase ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        return self._value
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        return self._value





class     __a     (       __UpperCamelCase			,			__UpperCamelCase ):
 def __init__(  self							,					lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__					)

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

        lowercase__:  Dict         =  None
        lowercase__:  List[str]         =  None
        lowercase__:  Dict         =  None

        if isinstance(lowerCAmelCase__							,					lowerCAmelCase__					):
               lowercase__:  Tuple         =  value
        elif isinstance(lowerCAmelCase__							,					(str, pathlib.Path)					):
               lowercase__:  str         =  value
        elif isinstance(lowerCAmelCase__							,					torch.Tensor					):
               lowercase__:  str         =  value
        else:
               raise ValueError(F'Unsupported type for {self.__class__.__name__}: {type(lowerCAmelCase__					)}'					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None					)   ->							int:




        '''simple docstring'''
        from IPython.display import Image, display

        display(Image(self.to_string()					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        if self._raw is not None:
               return self._raw

        if self._path is not None:
               lowercase__:  List[str]         =  Image.open(self._path					)
               return self._raw
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        if self._path is not None:
               return self._path

        if self._raw is not None:
               lowercase__:  Tuple         =  tempfile.mkdtemp()
               lowercase__:  Dict         =  os.path.join(lowerCAmelCase__							,					str(uuid.uuida()					) + '.png'					)
               self._raw.save(self._path					)

               return self._path

        if self._tensor is not None:
               lowercase__:  Union[str, Any]         =  self._tensor.cpu().detach().numpy()

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

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

               img.save(self._path					)

               return self._path





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=16_000					)   ->							Union[str, Any]:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__					)

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

        lowercase__:  int         =  None
        lowercase__:  str         =  None

        lowercase__:  Union[str, Any]         =  samplerate

        if isinstance(lowerCAmelCase__							,					(str, pathlib.Path)					):
               lowercase__:  str         =  value
        elif isinstance(lowerCAmelCase__							,					torch.Tensor					):
               lowercase__:  Optional[Any]         =  value
        else:
               raise ValueError(F'Unsupported audio type: {type(lowerCAmelCase__					)}'					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None					)   ->							List[str]:




        '''simple docstring'''
        from IPython.display import Audio, display

        display(Audio(self.to_string()							,					rate=self.samplerate					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        if self._tensor is not None:
               return self._tensor

        if self._path is not None:
               lowercase__     ,		lowercase__:  List[Any]         =  sf.read(self._path					)
               lowercase__:  str         =  torch.tensor(lowerCAmelCase__					)
               return self._tensor
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        if self._path is not None:
               return self._path

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





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

if is_vision_available():
 __lowerCAmelCase									=				AgentImage




def   snake_case_	(    *snake_case     ,				**snake_case	)    ->							Dict:
       lowercase__:  Union[str, Any]         =  [(arg.to_raw() if isinstance(snake_case     ,				snake_case	) else arg) for arg in args]
       lowercase__:  Tuple         =  {k: (v.to_raw() if isinstance(snake_case     ,				snake_case	) else v) for k, v in kwargs.items()}
       return args, kwargs




def   snake_case_	(    snake_case     ,				snake_case=None	)    ->							Union[str, Any]:
       if isinstance(snake_case     ,				snake_case	):
              lowercase__:  List[str]         =  {}
              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__:  Dict         =  AGENT_TYPE_MAPPING[output_types[i]](snake_case	)
                            else:
                                   lowercase__:  Optional[int]         =  AgentType(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(snake_case     ,				_k	):
                                          lowercase__:  Tuple         =  _v(snake_case	)
                            if k not in decoded_outputs:
                                   lowercase__:  Dict         =  AgentType[v]

       elif isinstance(snake_case     ,				(list, tuple)	):
              lowercase__:  List[str]         =  type(snake_case	)()
              for i, v in enumerate(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]](snake_case	)	)
                            else:
                                   decoded_outputs.append(AgentType(snake_case	)	)
                     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(snake_case     ,				_k	):
                                          decoded_outputs.append(_v(snake_case	)	)
                                          lowercase__:  str         =  True

                            if not found:
                                   decoded_outputs.append(AgentType(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
                     lowercase__:  str         =  AGENT_TYPE_MAPPING[output_types[0]](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(snake_case     ,				_k	):
                                   return _v(snake_case	)
                     return AgentType(snake_case	)

       return decoded_outputs






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


