code: 
  
  
  
  
  """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
  
  
  __A :		Union[str, Any]      							=    logging.get_logger(__name__)
  
  if is_vision_available():
         import PIL.Image
         from PIL import Image
         from PIL.Image import Image as ImageType
  else:
         __A :		Any      							=    object
  
  if is_torch_available():
         import torch
  
  if is_soundfile_availble():
         import soundfile as sf
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							value
  
  
  
  
  
  
  
     def __str__(							self ):
         return self.to_string()
  
  
  
  
  
  
  
     def       a_							(							self ):
         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 ):
         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       a_							(							self ):
         return self._value
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self._value
  
  
  
  
  
  
  
  class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         super().__init__(SCREAMING_SNAKE_CASE_ )
  
         if not is_vision_available():
             raise ImportError("""PIL must be installed in order to handle images.""" )
  
         UpperCamelCase				:   Optional[Any]          =							None
         UpperCamelCase				:   Dict          =							None
         UpperCamelCase				:   str          =							None
  
         if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Union[str, Any]          =							value
         elif isinstance(SCREAMING_SNAKE_CASE_    ,      (str, pathlib.Path) ):
             UpperCamelCase				:   Union[str, Any]          =							value
         elif isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   Any          =							value
         else:
             raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_ )}' )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None ):
         from IPython.display import Image, display
  
         display(Image(self.to_string() ) )
  
  
  
  
  
  
  
     def       a_							(							self ):
         if self._raw is not None:
             return self._raw
  
         if self._path is not None:
             UpperCamelCase				:   Any          =							Image.open(self._path )
             return self._raw
  
  
  
  
  
  
  
     def       a_							(							self ):
         if self._path is not None:
             return self._path
  
         if self._raw is not None:
             UpperCamelCase				:   Union[str, Any]          =							tempfile.mkdtemp()
             UpperCamelCase				:   Optional[int]          =							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[str]          =							Image.fromarray((array * 255).astype(np.uinta ) )
  
             UpperCamelCase				:   Optional[int]          =							tempfile.mkdtemp()
             UpperCamelCase				:   Optional[int]          =							os.path.join(SCREAMING_SNAKE_CASE_    ,      str(uuid.uuida() ) + """.png""" )
  
             img.save(self._path )
  
             return self._path
  
  
  
  
  
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=1_6000 ):
         super().__init__(SCREAMING_SNAKE_CASE_ )
  
         if not is_soundfile_availble():
             raise ImportError("""soundfile must be installed in order to handle audio.""" )
  
         UpperCamelCase				:   Tuple          =							None
         UpperCamelCase				:   Union[str, Any]          =							None
  
         UpperCamelCase				:   Union[str, Any]          =							samplerate
  
         if isinstance(SCREAMING_SNAKE_CASE_    ,      (str, pathlib.Path) ):
             UpperCamelCase				:   Any          =							value
         elif isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   Optional[int]          =							value
         else:
             raise ValueError(f'Unsupported audio type: {type(SCREAMING_SNAKE_CASE_ )}' )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None ):
         from IPython.display import Audio, display
  
         display(Audio(self.to_string()    ,      rate=self.samplerate ) )
  
  
  
  
  
  
  
     def       a_							(							self ):
         if self._tensor is not None:
             return self._tensor
  
         if self._path is not None:
             UpperCamelCase				:   str          =							sf.read(self._path )
             UpperCamelCase				:   Optional[Any]          =							torch.tensor(SCREAMING_SNAKE_CASE_ )
             return self._tensor
  
  
  
  
  
  
  
     def       a_							(							self ):
         if self._path is not None:
             return self._path
  
         if self._tensor is not None:
             UpperCamelCase				:   List[str]          =							tempfile.mkdtemp()
             UpperCamelCase				:   Optional[int]          =							os.path.join(SCREAMING_SNAKE_CASE_    ,      str(uuid.uuida() ) + """.wav""" )
             sf.write(self._path    ,      self._tensor    ,      samplerate=self.samplerate )
             return self._path
  
  
  
  
  
  
  
  __A :		int      							=    {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
  __A :		List[Any]      							=    {str: AgentText}
  
  if is_vision_available():
         __A :		Optional[Any]      							=    AgentImage
  def 					A_			(		*snake_case_     :       List[str]						,**snake_case_     :       Any    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Tuple          =							[(arg.to_raw() if isinstance(snake_case_						,snake_case_    ) else arg) for arg in args]
      UpperCamelCase				:   Union[str, Any]          =							{k: (v.to_raw() if isinstance(snake_case_						,snake_case_    ) else v) for k, v in kwargs.items()}
      return args, kwargs
  
  
  def 					A_			(		snake_case_     :       Optional[int]						,snake_case_     :       Tuple=None    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,snake_case_    ):
          UpperCamelCase				:   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:
                      UpperCamelCase				:   int          =							AGENT_TYPE_MAPPING[output_types[i]](snake_case_    )
                  else:
                      UpperCamelCase				:   str          =							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    ):
                          UpperCamelCase				:   Union[str, Any]          =							_v(snake_case_    )
                  if k not in decoded_outputs:
                      UpperCamelCase				:   Optional[int]          =							AgentType[v]
  
      elif isinstance(snake_case_						,(list, tuple)    ):
          UpperCamelCase				:   Tuple          =							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
                  UpperCamelCase				:   List[str]          =							False
                  for _k, _v in INSTANCE_TYPE_MAPPING.items():
                      if isinstance(snake_case_						,_k    ):
                          decoded_outputs.append(_v(snake_case_    )    )
                          UpperCamelCase				:   List[Any]          =							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
              UpperCamelCase				:   Any          =							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
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  