



"""simple docstring"""




_UpperCamelCase:				Optional[Any]    							=  [0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
_UpperCamelCase:				Dict    							=  3
_UpperCamelCase:				Any    							=  5

# Buffer Space (with Parameters Space)
_UpperCamelCase:				list[float]    							=  []
_UpperCamelCase:				list[float]    							=  []

# Machine Time
_UpperCamelCase:				List[Any]    							=  0


def   lowercase__     (			_UpperCAmelCase				)     ->			int:






      '''simple docstring'''


      global buffer_space, params_space, machine_time, K, m, t

      # Choosing Dynamical Systems (All)
      for key, value in enumerate(_UpperCAmelCase				):
            # Evolution Parameter
            lowercase  :      int					=   float(seed / value				)

            # Control Theory: Orbit Change
            lowercase  :      Any					=   (buffer_space[(key + 1) % m] + e) % 1

            # Control Theory: Trajectory Change
            lowercase  :      int					=   (params_space[key] + e) % 1 + 3

            # Modification (Transition Function) - Jumps
            lowercase  :      Optional[Any]					=   round(float(r * value * (1 - value)				)	,      10				)
            lowercase  :      Union[str, Any]					=   r  # Saving to Parameters Space

      # Logistic Map
      assert max(_UpperCAmelCase				) < 1
      assert max(_UpperCAmelCase				) < 4

      # Machine Time
      machine_time += 1


def   lowercase__     (			)     ->			List[str]:






      '''simple docstring'''


      global buffer_space, params_space, machine_time, K, m, t

      # PRNG (Xorshift by George Marsaglia)
      def xorshift(_UpperCAmelCase	,      _UpperCAmelCase				):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x

      # Choosing Dynamical Systems (Increment)
      lowercase  :      Dict					=   machine_time % m

      # Evolution (Time Length)
      for _ in range(0	,      _UpperCAmelCase				):
            # Variables (Position + Parameters)
            lowercase  :      Dict					=   params_space[key]
            lowercase  :      List[str]					=   buffer_space[key]

            # Modification (Transition Function) - Flow
            lowercase  :      str					=   round(float(r * value * (1 - value)				)	,      10				)
            lowercase  :      Dict					=   (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

      # Choosing Chaotic Data
      lowercase  :      Any					=   int(buffer_space[(key + 2) % m] * (10**10)				)
      lowercase  :      int					=   int(buffer_space[(key - 2) % m] * (10**10)				)

      # Machine Time
      machine_time += 1

      return xorshift(_UpperCAmelCase	,      _UpperCAmelCase				) % 0XFF_FFF_FFF


def   lowercase__     (			)     ->			int:






      '''simple docstring'''


      global buffer_space, params_space, machine_time, K, m, t

      lowercase  :      Dict					=   K
      lowercase  :      Tuple					=   [0] * m
      lowercase  :      List[str]					=   0


if __name__ == "__main__":
     # Initialization
     reset()

     # Pushing Data (Input)
     import random

     _UpperCamelCase:				str    							=  random.sample(range(0XFFFF_FFFF), 1_0_0)
     for chunk in message:
          push(chunk)

     # for controlling
     _UpperCamelCase:				Tuple    							=  ''

     # Pulling Data (Output)
     while inp in ("e", "E"):
          print(f'''{format(pull(), '#04x')}''')
          print(buffer_space)
          print(params_space)
          _UpperCamelCase:				Tuple    							=  input('(e)exit? ').strip()





"""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


_UpperCamelCase:				List[str]    							=  logging.get_logger(__name__)

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

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf


class   a__		:


   def __init__(							self      :							Union[str, Any],     lowerCAmelCase      :							Optional[int]			)    ->       Any:
         lowercase  :      Dict					=   value


   def __str__(							self      :							List[str]			)    ->       Union[str, Any]:
         return self.to_string()


   def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:
         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      :							List[Any]			)    ->       str:
         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__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):


   def        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         return self._value






   def        lowercase				(							self      :							str			)    ->       Optional[int]:
         return self._value




class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							int,     lowerCAmelCase      :							Optional[Any]			)    ->       Any:
         super().__init__(lowerCAmelCase			)

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

         lowercase  :      Union[str, Any]					=   None
         lowercase  :      List[str]					=   None
         lowercase  :      Optional[Any]					=   None

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


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							Union[str, Any]=None,     lowerCAmelCase      :							Optional[Any]=None			)    ->       List[Any]:
         from IPython.display import Image, display

         display(Image(self.to_string()			)			)


   def        lowercase				(							self      :							int			)    ->       List[str]:
         if self._raw is not None:
               return self._raw

         if self._path is not None:
               lowercase  :      Any					=   Image.open(self._path			)
               return self._raw






   def        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         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  :      Optional[int]					=   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  :      Optional[int]					=   tempfile.mkdtemp()
               lowercase  :      Any					=   os.path.join(lowerCAmelCase,     str(uuid.uuida()			) + '.png'			)

               img.save(self._path			)

               return self._path




class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Dict,     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Any=16000			)    ->       Tuple:
         super().__init__(lowerCAmelCase			)

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

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

         lowercase  :      Optional[Any]					=   samplerate

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


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							int=None			)    ->       Optional[Any]:
         from IPython.display import Audio, display

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


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         if self._tensor is not None:
               return self._tensor

         if self._path is not None:
               lowercase	,    lowercase  :      List[Any]					=   sf.read(self._path			)
               lowercase  :      int					=   torch.tensor(lowerCAmelCase			)
               return self._tensor






   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         if self._path is not None:
               return self._path

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




_UpperCamelCase:				Optional[int]    							=  {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_UpperCamelCase:				Optional[int]    							=  {str: AgentText}

if is_vision_available():
     _UpperCamelCase:				str    							=  AgentImage


def   lowercase__     (			*_UpperCAmelCase	,      **_UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      lowercase  :      str					=   [(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   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=None				)     ->			Tuple:






      '''simple docstring'''


      if isinstance(_UpperCAmelCase	,      _UpperCAmelCase				):
            lowercase  :      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:
                              lowercase  :      int					=   AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase				)
                        else:
                              lowercase  :      Union[str, Any]					=   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[int]					=   _v(_UpperCAmelCase				)
                        if k not in decoded_outputs:
                              lowercase  :      str					=   AgentType[v]

      elif isinstance(_UpperCAmelCase	,      (list, tuple)				):
            lowercase  :      Union[str, Any]					=   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  :      Any					=   False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_UpperCAmelCase	,      _k				):
                                    decoded_outputs.append(_v(_UpperCAmelCase				)				)
                                    lowercase  :      Optional[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
