




from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase								=     TypeVar('T')





class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       List[Any]:
         lowerCAmelCase__		= data  # Assign data
         lowerCAmelCase__		= None  # Initialize next as null
         lowerCAmelCase__		= None  # Initialize prev as null






class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     Any   )      ->       None:
         lowerCAmelCase__		= None


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       None:
         if self.head is None:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
              lowerCAmelCase__		= new_node
              lowerCAmelCase__		= self.head
              lowerCAmelCase__		= None
              lowerCAmelCase__		= new_node


    def    a      (       self     :     int   )      ->       T | None:
         if self.head is None:
              return None
         else:
              assert self.head is not None
              lowerCAmelCase__		= self.head.data
              lowerCAmelCase__		= self.head.next
              if self.head is not None:
                   lowerCAmelCase__		= None
              return temp


    def    a      (       self     :     int   )      ->       T | None:
         return self.head.data if self.head is not None else None


    def __len__(       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= 0
         while temp is not None:
              count += 1
              lowerCAmelCase__		= temp.next
         return count


    def    a      (       self     :     Tuple   )      ->       bool:
         return self.head is None


    def    a      (       self     :     Dict   )      ->       None:
         print("stack elements are:"   )
         lowerCAmelCase__		= self.head
         while temp is not None:
              print(temp.data   ,	end="->"   )
              lowerCAmelCase__		= temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     UpperCamelCase								=     Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())






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       :




    """simple docstring"""

    def __init__(       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= value


    def __str__(       self     :     int   )      ->       List[Any]:
         return self.to_string()


    def    a      (       self     :     str   )      ->       Union[str, Any]:
         logger.error(
             "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"   )
         return self._value


    def    a      (       self     :     str   )      ->       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  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Optional[Any]   )      ->       int:
         return self._value


    def    a      (       self     :     Optional[Any]   )      ->       Any:
         return self._value






class  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

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

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

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	(str, pathlib.Path)   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= value
         else:
              raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE__   )}'   )


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

         display(Image(self.to_string()   )   )


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

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


    def    a      (       self     :     Optional[int]   )      ->       Union[str, Any]:
         if self._path is not None:
              return self._path

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

              return self._path

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

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

              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	str(uuid.uuida()   ) + ".png"   )

              img.save(self._path   )

              return self._path






class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     int=16_000   )      ->       Union[str, Any]:
         super().__init__(SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

         lowerCAmelCase__		= samplerate

         if isinstance(SCREAMING_SNAKE_CASE__   ,	(str, pathlib.Path)   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= value
         else:
              raise ValueError(f'Unsupported audio type: {type(SCREAMING_SNAKE_CASE__   )}'   )


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[str]=None   ,	SCREAMING_SNAKE_CASE__     :     List[str]=None   )      ->       Any:
         from IPython.display import Audio, display

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


    def    a      (       self     :     Optional[Any]   )      ->       List[Any]:
         if self._tensor is not None:
              return self._tensor

         if self._path is not None:
              lowerCAmelCase__	,		lowerCAmelCase__		= sf.read(self._path   )
              lowerCAmelCase__		= torch.tensor(SCREAMING_SNAKE_CASE__   )
              return self._tensor


    def    a      (       self     :     Dict   )      ->       List[str]:
         if self._path is not None:
              return self._path

         if self._tensor is not None:
              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_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_     :					List[str]   ,				**lowerCAmelCase_     :					Optional[int]			):
     """simple docstring"""

     lowerCAmelCase__		= [(arg.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else arg) for arg in args]
     lowerCAmelCase__		= {k: (v.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else v) for k, v in kwargs.items()}
     return args, kwargs






def 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					Optional[int]=None			):
     """simple docstring"""

     if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			):
          lowerCAmelCase__		= {}
          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__		= AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase_			)
                    else:
                         lowerCAmelCase__		= 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__		= _v(lowerCAmelCase_			)
                    if k not in decoded_outputs:
                         lowerCAmelCase__		= AgentType[v]

     elif isinstance(lowerCAmelCase_   ,				(list, tuple)			):
          lowerCAmelCase__		= 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__		= False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowerCAmelCase_   ,				_k			):
                              decoded_outputs.append(_v(lowerCAmelCase_			)			)
                              lowerCAmelCase__		= 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__		= 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
