


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Optional[int]  					=       {
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[int]        =						'''realm'''
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Any=3_0_5_2_2     ,							SCREAMING_SNAKE_CASE__				:       int=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=1_2     ,							SCREAMING_SNAKE_CASE__				:       int=8     ,							SCREAMING_SNAKE_CASE__				:       int=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]="gelu_new"     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.02     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1E-12     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2_5_6     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_0     ,							SCREAMING_SNAKE_CASE__				:       int=1E-3     ,							SCREAMING_SNAKE_CASE__				:       str=5     ,							SCREAMING_SNAKE_CASE__				:       Any=3_2_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=1_3_3_5_3_7_1_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=5_0_0_0     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2     ,							**SCREAMING_SNAKE_CASE__				:       Dict     ,							)		->			Optional[Any]:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             # Common config
             a_					:		Optional[int]							=  vocab_size
             a_					:		List[Any]							=  max_position_embeddings
             a_					:		Dict							=  hidden_size
             a_					:		List[str]							=  retriever_proj_size
             a_					:		Optional[int]							=  num_hidden_layers
             a_					:		Optional[Any]							=  num_attention_heads
             a_					:		Dict							=  num_candidates
             a_					:		Any							=  intermediate_size
             a_					:		Optional[int]							=  hidden_act
             a_					:		Optional[Any]							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		List[Any]							=  initializer_range
             a_					:		str							=  type_vocab_size
             a_					:		Any							=  layer_norm_eps

             # Reader config
             a_					:		str							=  span_hidden_size
             a_					:		List[Any]							=  max_span_width
             a_					:		Tuple							=  reader_layer_norm_eps
             a_					:		int							=  reader_beam_size
             a_					:		List[Any]							=  reader_seq_len

             # Retrieval config
             a_					:		int							=  num_block_records
             a_					:		int							=  searcher_beam_size



'''simple docstring'''

import numpy as np
import qiskit







def      UpperCamelCase_						(					snake_case_       :       int = 8						,		snake_case_       :       int | None = None      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						np.random.default_rng(seed=snake_case_      )

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __lowerCAmelCase											=						6 * key_len
 # Measurement basis for Alice's qubits.
 __lowerCAmelCase											=						rng.integers(2						,		size=snake_case_      )
 # The set of states Alice will prepare.
 __lowerCAmelCase											=						rng.integers(2						,		size=snake_case_      )
 # Measurement basis for Bob's qubits.
 __lowerCAmelCase											=						rng.integers(2						,		size=snake_case_      )

 # Quantum Circuit to simulate BB84
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						qiskit.execute(snake_case_						,		snake_case_						,		shots=1						,		seed_simulator=snake_case_      )
 # Returns the result of measurement.
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						"""""".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.
 __lowerCAmelCase											=						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()






