code: 
  
  
  
  
  
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  def        A	(							__snake_case:   List[Any]   ,    __snake_case:   str   ,    __snake_case:   Union[str, Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						json.load(open(__snake_case   ,    'r'     )     )
         __magic_name__										=						GPTNeoConfig(
             hidden_size=config_json['n_embd']   ,    num_layers=config_json['n_layer']   ,    num_heads=config_json['n_head']   ,    attention_types=config_json['attention_types']   ,    max_position_embeddings=config_json['n_positions']   ,    resid_dropout=config_json['res_dropout']   ,    embed_dropout=config_json['embed_dropout']   ,    attention_dropout=config_json['attn_dropout']   ,    )
         print(F"""Building PyTorch model from configuration: {config}"""     )
         __magic_name__										=						GPTNeoForCausalLM(__snake_case     )
  
         # Load weights from tf checkpoint
         load_tf_weights_in_gpt_neo(__snake_case   ,    __snake_case   ,    __snake_case     )
  
         # Save pytorch-model
         print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
         model.save_pretrained(__snake_case     )
  
  
  if __name__ == "__main__":
   snake_case   :   Optional[Any]    		=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained mesh-tf model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   snake_case   :   List[str]    		=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1     )
  
         # First, let's deal with the case in which oracle is balanced
         if case == "balanced":
                # First generate a random number that tells us which CNOTs to
                # wrap in X-gates:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__snake_case   ,    F"""0{num_qubits}b"""     )
                # Next, we place the first X-gates. Each digit in our binary string
                # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
                # we apply an X-gate to that qubit:
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
  
      # Case in which oracle is constant
         if case == "constant":
                # First decide what the fixed output of the oracle will be
                # (either always 0 or always 1)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__snake_case     )
  
  
  if __name__ == "__main__":
   print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
   print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")