

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										="data2vec-text"







  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Union[str, Any]=3_05_22	,							lowerCAmelCase			:   List[Any]=7_68	,							lowerCAmelCase			:   str=12	,							lowerCAmelCase			:   Optional[int]=12	,							lowerCAmelCase			:   Dict=30_72	,							lowerCAmelCase			:   List[str]="gelu"	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=5_12	,							lowerCAmelCase			:   Optional[int]=2	,							lowerCAmelCase			:   Tuple=0.02	,							lowerCAmelCase			:   int=1e-12	,							lowerCAmelCase			:   List[str]=1	,							lowerCAmelCase			:   int=0	,							lowerCAmelCase			:   Optional[int]=2	,							lowerCAmelCase			:   List[str]="absolute"	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   List[str]=None	,							**lowerCAmelCase			:   Union[str, Any]	,							)							->     Any:






     """simple docstring"""







     super().__init__(pad_token_id=lowerCAmelCase	,							bos_token_id=lowerCAmelCase	,							eos_token_id=lowerCAmelCase	,							**lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      List[Any]    =						num_hidden_layers
     __lowerCAmelCase  :      str    =						num_attention_heads
     __lowerCAmelCase  :      List[str]    =						hidden_act
     __lowerCAmelCase  :      Tuple    =						intermediate_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[str]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						max_position_embeddings
     __lowerCAmelCase  :      Tuple    =						type_vocab_size
     __lowerCAmelCase  :      List[str]    =						initializer_range
     __lowerCAmelCase  :      Dict    =						layer_norm_eps
     __lowerCAmelCase  :      Dict    =						position_embedding_type
     __lowerCAmelCase  :      Union[str, Any]    =						use_cache
     __lowerCAmelCase  :      List[str]    =						classifier_dropout






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






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






     """simple docstring"""







     if self.task == "multiple-choice":
        __lowerCAmelCase  :      int    =						{0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        __lowerCAmelCase  :      Tuple    =						{0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ("""input_ids""", dynamic_axis),
             ("""attention_mask""", dynamic_axis),
         ]   )





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

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

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

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

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

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[Any]    =						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  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

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

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


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

   testmod()


