code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import Optional, Union
  
  from ...file_utils import TensorType
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  int      =				'MarkupLMFeatureExtractor'
     lowercase       :  Union[str, Any]      =				('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowercase       :  Tuple      =				True
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 0    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # first, create nodes and xpaths
         if self.parse_html:
             if html_strings is None:
                 raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`""" )
  
             if nodes is not None or xpaths is not None or node_labels is not None:
                 raise ValueError(
                     """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`""" )
  
             UpperCamelCase				:   Any          =							self.feature_extractor(A_ )
             UpperCamelCase				:   Optional[Any]          =							features["""nodes"""]
             UpperCamelCase				:   Any          =							features["""xpaths"""]
         else:
             if html_strings is not None:
                 raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`.""" )
             if nodes is None or xpaths is None:
                 raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`""" )
  
          # # second, apply the tokenizer
         if questions is not None and self.parse_html:
             if isinstance(A_    ,      A_ ):
                 UpperCamelCase				:   List[Any]          =							[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
         UpperCamelCase				:   Union[str, Any]          =							self.tokenizer(
             text=questions if questions is not None else nodes    ,      text_pair=nodes if questions is not None else None    ,      xpaths=A_    ,      node_labels=A_    ,      add_special_tokens=A_    ,      padding=A_    ,      truncation=A_    ,      max_length=A_    ,      stride=A_    ,      pad_to_multiple_of=A_    ,      return_token_type_ids=A_    ,      return_attention_mask=A_    ,      return_overflowing_tokens=A_    ,      return_special_tokens_mask=A_    ,      return_offsets_mapping=A_    ,      return_length=A_    ,      verbose=A_    ,      return_tensors=A_    ,      **A_    ,      )
  
         return encoded_inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.tokenizer.batch_decode(*A_    ,      **A_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.tokenizer.decode(*A_    ,      **A_ )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   Union[str, Any]          =							self.tokenizer.model_input_names
         return tokenizer_input_names
  
  
  
  
  
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)}'''
    )
  
  
  
  
  