






'''simple docstring'''





from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Dict 				= [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]



       @register_to_config
       def __init__(							self			,						a__			,						a__			,						a__ = None			,						a__ = 5_02_57			,						a__ = 10_24			,						a__ = 7_68			,						a__ = 12			,						a__ = 12			,						a__ = None			,						a__ = "gelu_new"			,						a__ = 0.1			,						a__ = 0.1			,						a__ = 0.1			,						a__ = 1e-5			,						a__ = 0.02			,						a__ = True			,						a__ = True			,						a__ = False			,						a__ = False			,						) ->      str:






            '''simple docstring'''





            super().__init__()

            __snake_case			:Any          =						prefix_length

            if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
                 raise ValueError(
                     F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                     F''' `n_embd`: {n_embd} are not equal.'''    )

            __snake_case			:int          =						prefix_inner_dim
            __snake_case			:Any          =						prefix_hidden_dim

            __snake_case			:List[str]          =						(
                nn.Linear(self.prefix_inner_dim			,						self.prefix_hidden_dim    )
                if self.prefix_hidden_dim is not None
                else nn.Identity()
            )
            __snake_case			:List[Any]          =						(
                nn.Linear(self.prefix_hidden_dim			,						a__    ) if self.prefix_hidden_dim is not None else nn.Identity()
            )

            __snake_case			:Union[str, Any]          =						GPTaConfig(
                vocab_size=a__			,						n_positions=a__			,						n_embd=a__			,						n_layer=a__			,						n_head=a__			,						n_inner=a__			,						activation_function=a__			,						resid_pdrop=a__			,						embd_pdrop=a__			,						attn_pdrop=a__			,						layer_norm_epsilon=a__			,						initializer_range=a__			,						scale_attn_weights=a__			,						use_cache=a__			,						scale_attn_by_inverse_layer_idx=a__			,						reorder_and_upcast_attn=a__			,						)
            __snake_case			:Optional[Any]          =						GPTaLMHeadModel(a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = None			,						a__ = None			,						) ->      Dict:






            '''simple docstring'''





            __snake_case			:Any          =						self.transformer.transformer.wte(a__    )
            __snake_case			:Tuple          =						self.encode_prefix(a__    )
            __snake_case			:Dict          =						self.decode_prefix(a__    )
            __snake_case			:Dict          =						torch.cat((prefix_embeds, embedding_text)			,						dim=1    )

            if labels is not None:
                 __snake_case			:Optional[Any]          =						self.get_dummy_token(input_ids.shape[0]			,						input_ids.device    )
                 __snake_case			:int          =						torch.cat((dummy_token, input_ids)			,						dim=1    )
            __snake_case			:int          =						self.transformer(inputs_embeds=a__			,						labels=a__			,						attention_mask=a__    )
            if self.prefix_hidden_dim is not None:
                 return out, hidden
            else:
                 return out



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      torch.Tensor:






            '''simple docstring'''





            return torch.zeros(a__			,						self.prefix_length			,						dtype=torch.intaa			,						device=a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            return self.encode_prefix(a__    )



       @torch.no_grad()
       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						torch.split(a__			,						1			,						dim=0    )
            __snake_case			:Optional[int]          =						[]
            __snake_case			:List[Any]          =						[]
            for feature in features:
                 __snake_case			:Optional[Any]          =						self.decode_prefix(feature.to(a__    )    )  # back to the clip feature
                 # Only support beam search for now
                 __snake_case			:Optional[int]          =						self.generate_beam(
                     input_embeds=a__			,						device=a__			,						eos_token_id=a__    )
                 generated_tokens.append(output_tokens[0]    )
                 generated_seq_lengths.append(seq_lengths[0]    )
            __snake_case			:List[Any]          =						torch.stack(a__    )
            __snake_case			:Dict          =						torch.stack(a__    )
            return generated_tokens, generated_seq_lengths



       @torch.no_grad()
       def 					__lowercase						(							self			,						a__=None			,						a__=None			,						a__=None			,						a__ = 5			,						a__ = 67			,						a__ = 1.0			,						a__ = None			,						) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						eos_token_id
            __snake_case			:Any          =						None
            __snake_case			:List[Any]          =						None
            __snake_case			:Optional[int]          =						torch.ones(a__			,						device=a__			,						dtype=torch.int    )
            __snake_case			:int          =						torch.zeros(a__			,						device=a__			,						dtype=torch.bool    )

            if input_embeds is not None:
                 __snake_case			:int          =						input_embeds
            else:
                 __snake_case			:int          =						self.transformer.transformer.wte(a__    )

            for i in range(a__    ):
                 __snake_case			:List[str]          =						self.transformer(inputs_embeds=a__    )
                 __snake_case			:List[Any]          =						outputs.logits
                 __snake_case			:Tuple          =						logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
                 __snake_case			:Dict          =						logits.softmax(-1    ).log()

                 if scores is None:
                      __snake_case			:Tuple          =						logits.topk(a__			,						-1    )
                      __snake_case			:Union[str, Any]          =						generated.expand(a__			,						*generated.shape[1:]    )
                      __snake_case			:Dict          =						next_tokens.permute(1			,						0    ), scores.squeeze(0    )
                      if tokens is None:
                           __snake_case			:Optional[Any]          =						next_tokens
                      else:
                           __snake_case			:Union[str, Any]          =						tokens.expand(a__			,						*tokens.shape[1:]    )
                           __snake_case			:Dict          =						torch.cat((tokens, next_tokens)			,						dim=1    )
                 else:
                      __snake_case			:int          =						-float(np.inf    )
                      __snake_case			:str          =						0
                      __snake_case			:Optional[int]          =						scores[:, None] + logits
                      seq_lengths[~is_stopped] += 1
                      __snake_case			:List[Any]          =						scores_sum / seq_lengths[:, None]
                      __snake_case			:Any          =						scores_sum_average.view(-1    ).topk(a__			,						-1    )
                      __snake_case			:Union[str, Any]          =						next_tokens // scores_sum.shape[1]
                      __snake_case			:Dict          =						seq_lengths[next_tokens_source]
                      __snake_case			:List[Any]          =						next_tokens % scores_sum.shape[1]
                      __snake_case			:Union[str, Any]          =						next_tokens.unsqueeze(1    )
                      __snake_case			:Any          =						tokens[next_tokens_source]
                      __snake_case			:Any          =						torch.cat((tokens, next_tokens)			,						dim=1    )
                      __snake_case			:Union[str, Any]          =						generated[next_tokens_source]
                      __snake_case			:List[str]          =						scores_sum_average * seq_lengths
                      __snake_case			:Tuple          =						is_stopped[next_tokens_source]

                 __snake_case			:Dict          =						self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]			,						1			,						-1    )
                 __snake_case			:Dict          =						torch.cat((generated, next_token_embed)			,						dim=1    )
                 __snake_case			:List[Any]          =						is_stopped + next_tokens.eq(a__    ).squeeze()
                 if is_stopped.all():
                      break

            __snake_case			:List[str]          =						scores / seq_lengths
            __snake_case			:Optional[Any]          =						scores.argsort(descending=a__    )
            # tokens tensors are already padded to max_seq_length
            __snake_case			:List[Any]          =						[tokens[i] for i in order]
            __snake_case			:Tuple          =						torch.stack(a__			,						dim=0    )
            __snake_case			:List[Any]          =						torch.tensor([seq_lengths[i] for i in order]			,						dtype=seq_lengths.dtype    )
            return output_texts, seq_lengths










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()


