






'''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


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= "gptj"
       lowerCamelCase							:				int 				= {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }



       def __init__(							self			,						a__=5_04_00			,						a__=20_48			,						a__=40_96			,						a__=28			,						a__=16			,						a__=64			,						a__=None			,						a__="gelu_new"			,						a__=0.0			,						a__=0.0			,						a__=0.0			,						a__=1e-5			,						a__=0.02			,						a__=True			,						a__=5_02_56			,						a__=5_02_56			,						a__=False			,						**a__			,						) ->      Any:






            '''simple docstring'''





            __snake_case			:Dict          =						vocab_size
            __snake_case			:Any          =						n_positions
            __snake_case			:Any          =						n_embd
            __snake_case			:List[str]          =						n_layer
            __snake_case			:List[Any]          =						n_head
            __snake_case			:Dict          =						n_inner
            __snake_case			:str          =						rotary_dim
            __snake_case			:Optional[Any]          =						activation_function
            __snake_case			:Optional[int]          =						resid_pdrop
            __snake_case			:List[Any]          =						embd_pdrop
            __snake_case			:Tuple          =						attn_pdrop
            __snake_case			:Dict          =						layer_norm_epsilon
            __snake_case			:Union[str, Any]          =						initializer_range
            __snake_case			:Any          =						use_cache

            __snake_case			:Optional[Any]          =						bos_token_id
            __snake_case			:str          =						eos_token_id

            super().__init__(
                bos_token_id=a__			,						eos_token_id=a__			,						tie_word_embeddings=a__			,						**a__    )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__ = "default"			,						a__ = None			,						a__ = False			,						) ->      List[str]:






            '''simple docstring'''





            super().__init__(a__			,						task=a__			,						patching_specs=a__			,						use_past=a__    )
            if not getattr(self._config			,						"""pad_token_id"""			,						a__    ):
                 # TODO: how to do that better?
                 __snake_case			:Any          =						0



       @property
       def 					__lowercase						(							self    ) ->      Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            __snake_case			:str          =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
            if self.use_past:
                 self.fill_with_past_key_values_(a__			,						direction="""inputs"""    )
                 __snake_case			:int          =						{0: """batch""", 1: """past_sequence + sequence"""}
            else:
                 __snake_case			:Dict          =						{0: """batch""", 1: """sequence"""}

            return common_inputs



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self._config.n_layer



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self._config.n_head



       def 					__lowercase						(							self			,						a__			,						a__ = -1			,						a__ = -1			,						a__ = False			,						a__ = None			,						) ->      Mapping[str, Any]:






            '''simple docstring'''





            __snake_case			:Any          =						super(a__			,						self    ).generate_dummy_inputs(
                a__			,						batch_size=a__			,						seq_length=a__			,						is_pair=a__			,						framework=a__    )

            # We need to order the input in the way they appears in the forward()
            __snake_case			:List[str]          =						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

                      __snake_case			:Union[str, Any]          =						common_inputs["""input_ids"""].shape
                      # Not using the same length for past_key_values
                      __snake_case			:List[Any]          =						seqlen + 2
                      __snake_case			:Any          =						(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __snake_case			:Optional[Any]          =						[
                          (torch.zeros(a__    ), torch.zeros(a__    )) for _ in range(self.num_layers    )
                      ]

            __snake_case			:int          =						common_inputs["""attention_mask"""]
            if self.use_past:
                 __snake_case			:int          =						ordered_inputs["""attention_mask"""].dtype
                 __snake_case			:Optional[Any]          =						torch.cat(
                     [ordered_inputs["""attention_mask"""], torch.ones(a__			,						a__			,						dtype=a__    )]			,						dim=1    )

            return ordered_inputs



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return 13










import numpy as np
import qiskit






def   UpperCamelCase  (       snake_case__     :   int = 8	,snake_case__     :   int | None = None  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						np.random.default_rng(seed=snake_case__  )

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     __snake_case			:Union[str, Any]          =						6 * key_len
     # Measurement basis for Alice's qubits.
     __snake_case			:int          =						rng.integers(2	,size=snake_case__  )
     # The set of states Alice will prepare.
     __snake_case			:Dict          =						rng.integers(2	,size=snake_case__  )
     # Measurement basis for Bob's qubits.
     __snake_case			:str          =						rng.integers(2	,size=snake_case__  )

     # Quantum Circuit to simulate BB84
     __snake_case			:Optional[int]          =						qiskit.QuantumCircuit(snake_case__	,name="""BB84"""  )

     # Alice prepares her qubits according to rules above.
     for index, _ in enumerate(snake_case__  ):
          if alice_state[index] == 1:
               bbaa_circ.x(snake_case__  )
          if alice_basis[index] == 1:
               bbaa_circ.h(snake_case__  )
     bbaa_circ.barrier()

     # Bob measures the received qubits according to rules above.
     for index, _ in enumerate(snake_case__  ):
          if bob_basis[index] == 1:
               bbaa_circ.h(snake_case__  )

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     __snake_case			:Optional[int]          =						qiskit.Aer.get_backend("""aer_simulator"""  )
     # We only need to run one shot because the key is unique.
     # Multiple shots will produce the same key.
     __snake_case			:List[Any]          =						qiskit.execute(snake_case__	,snake_case__	,shots=1	,seed_simulator=snake_case__  )
     # Returns the result of measurement.
     __snake_case			:Tuple          =						job.result().get_counts(snake_case__  ).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     __snake_case			:Any          =						"""""".join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 snake_case__	,snake_case__	,snake_case__  )
             if alice_basis_bit == bob_basis_bit
         ]  )

     # Get final key. Pad with 0 if too short, otherwise truncate.
     __snake_case			:Dict          =						gen_key[:key_len] if len(snake_case__  ) >= key_len else gen_key.ljust(snake_case__	,"""0"""  )
     return key


if __name__ == "__main__":
     print(f'''The generated key is : {bbaa(8, seed=0)}''')
     from doctest import testmod

     testmod()


