code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  __A :		List[str]      							=    logging.get_logger(__name__)
  
  __A :		str      							=    {
      '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
      '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
      '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
      '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
      '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
  }
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  str      =				'gpt2'
     lowercase       :  str      =				['past_key_values']
     lowercase       :  Any      =				{
         'hidden_size': 'n_embd',
         'max_position_embeddings': 'n_positions',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=5_0257    ,      SCREAMING_SNAKE_CASE_=1024    ,      SCREAMING_SNAKE_CASE_=768    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="gelu_new"    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=1e-5    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_="cls_index"    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=5_0256    ,      SCREAMING_SNAKE_CASE_=5_0256    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   int          =							vocab_size
         UpperCamelCase				:   List[str]          =							n_positions
         UpperCamelCase				:   Union[str, Any]          =							n_embd
         UpperCamelCase				:   List[str]          =							n_layer
         UpperCamelCase				:   List[str]          =							n_head
         UpperCamelCase				:   Dict          =							n_inner
         UpperCamelCase				:   str          =							activation_function
         UpperCamelCase				:   Optional[Any]          =							resid_pdrop
         UpperCamelCase				:   Dict          =							embd_pdrop
         UpperCamelCase				:   List[str]          =							attn_pdrop
         UpperCamelCase				:   List[Any]          =							layer_norm_epsilon
         UpperCamelCase				:   int          =							initializer_range
         UpperCamelCase				:   Tuple          =							summary_type
         UpperCamelCase				:   int          =							summary_use_proj
         UpperCamelCase				:   Optional[Any]          =							summary_activation
         UpperCamelCase				:   List[str]          =							summary_first_dropout
         UpperCamelCase				:   Optional[Any]          =							summary_proj_to_labels
         UpperCamelCase				:   str          =							scale_attn_weights
         UpperCamelCase				:   Any          =							use_cache
         UpperCamelCase				:   Tuple          =							scale_attn_by_inverse_layer_idx
         UpperCamelCase				:   int          =							reorder_and_upcast_attn
  
         UpperCamelCase				:   Dict          =							bos_token_id
         UpperCamelCase				:   int          =							eos_token_id
  
         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_    ,      eos_token_id=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "default"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False    ,      ):
         super().__init__(SCREAMING_SNAKE_CASE_    ,      task=SCREAMING_SNAKE_CASE_    ,      patching_specs=SCREAMING_SNAKE_CASE_    ,      use_past=SCREAMING_SNAKE_CASE_ )
         if not getattr(self._config    ,      """pad_token_id"""    ,      SCREAMING_SNAKE_CASE_ ):
             # TODO: how to do that better?
             UpperCamelCase				:   Tuple          =							0
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
         if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_    ,      direction="""inputs""" )
             UpperCamelCase				:   Dict          =							{0: """batch""", 1: """past_sequence + sequence"""}
         else:
             UpperCamelCase				:   str          =							{0: """batch""", 1: """sequence"""}
  
         return common_inputs
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._config.n_layer
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._config.n_head
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         UpperCamelCase				:   Any          =							super(SCREAMING_SNAKE_CASE_    ,      self ).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE_    ,      batch_size=SCREAMING_SNAKE_CASE_    ,      seq_length=SCREAMING_SNAKE_CASE_    ,      is_pair=SCREAMING_SNAKE_CASE_    ,      framework=SCREAMING_SNAKE_CASE_ )
  
         # We need to order the input in the way they appears in the forward()
         UpperCamelCase				:   Optional[Any]          =							OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
  
         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
             else:
                 import torch
  
                 UpperCamelCase       ,							UpperCamelCase				:   Dict          =							common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 UpperCamelCase				:   str          =							seqlen + 2
                 UpperCamelCase				:   Tuple          =							(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 UpperCamelCase				:   Union[str, Any]          =							[
                     (torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
                 ]
  
         UpperCamelCase				:   Optional[Any]          =							common_inputs["""attention_mask"""]
         if self.use_past:
             UpperCamelCase				:   Any          =							ordered_inputs["""attention_mask"""].dtype
             UpperCamelCase				:   int          =							torch.cat(
                 [ordered_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )]    ,      dim=1 )
  
         return ordered_inputs
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return 13
  
  
  
  
  
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)}'''
    )
  
  
  
  
  