

from typing import List, Optional, Union

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


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



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






  lowerCamelCase   :      Dict										="informer"
  lowerCamelCase   :      int										={
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
      "num_hidden_layers": "encoder_layers",
  }







  def __init__(      self			:   str	,							lowerCAmelCase			:   Union[str, Any] = None	,							lowerCAmelCase			:   Any = None	,							lowerCAmelCase			:   Union[str, Any] = "student_t"	,							lowerCAmelCase			:   Dict = "nll"	,							lowerCAmelCase			:   List[str] = 1	,							lowerCAmelCase			:   Union[str, Any] = None	,							lowerCAmelCase			:   List[Any] = "mean"	,							lowerCAmelCase			:   str = 0	,							lowerCAmelCase			:   Optional[Any] = 0	,							lowerCAmelCase			:   List[Any] = 0	,							lowerCAmelCase			:   Any = 0	,							lowerCAmelCase			:   str = None	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   str = 64	,							lowerCAmelCase			:   Dict = 32	,							lowerCAmelCase			:   List[str] = 32	,							lowerCAmelCase			:   str = 2	,							lowerCAmelCase			:   Tuple = 2	,							lowerCAmelCase			:   Any = 2	,							lowerCAmelCase			:   int = 2	,							lowerCAmelCase			:   int = True	,							lowerCAmelCase			:   Any = "gelu"	,							lowerCAmelCase			:   List[Any] = 0.05	,							lowerCAmelCase			:   Any = 0.1	,							lowerCAmelCase			:   List[Any] = 0.1	,							lowerCAmelCase			:   Optional[int] = 0.1	,							lowerCAmelCase			:   Any = 0.1	,							lowerCAmelCase			:   List[Any] = 1_00	,							lowerCAmelCase			:   Tuple = 0.02	,							lowerCAmelCase			:   Dict=True	,							lowerCAmelCase			:   Optional[Any] = "prob"	,							lowerCAmelCase			:   int = 5	,							lowerCAmelCase			:   List[Any] = True	,							**lowerCAmelCase			:   Dict	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						prediction_length
     __lowerCAmelCase  :      str    =						context_length or prediction_length
     __lowerCAmelCase  :      Any    =						distribution_output
     __lowerCAmelCase  :      List[Any]    =						loss
     __lowerCAmelCase  :      str    =						input_size
     __lowerCAmelCase  :      Optional[Any]    =						num_time_features
     __lowerCAmelCase  :      List[str]    =						lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
     __lowerCAmelCase  :      List[str]    =						scaling
     __lowerCAmelCase  :      Optional[Any]    =						num_dynamic_real_features
     __lowerCAmelCase  :      Any    =						num_static_real_features
     __lowerCAmelCase  :      Tuple    =						num_static_categorical_features

     # set cardinality
     if cardinality and num_static_categorical_features > 0:
        if len(__a   ) != num_static_categorical_features:
           raise ValueError(
               """The cardinality should be a list of the same length as `num_static_categorical_features`"""   )
        __lowerCAmelCase  :      Optional[int]    =						cardinality
     else:
        __lowerCAmelCase  :      Dict    =						[0]

     # set embedding_dimension
     if embedding_dimension and num_static_categorical_features > 0:
        if len(__a   ) != num_static_categorical_features:
           raise ValueError(
               """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""   )
        __lowerCAmelCase  :      Tuple    =						embedding_dimension
     else:
        __lowerCAmelCase  :      Union[str, Any]    =						[min(50	,							(cat + 1) // 2   ) for cat in self.cardinality]

     __lowerCAmelCase  :      Any    =						num_parallel_samples

     # Transformer architecture configuration
     __lowerCAmelCase  :      Any    =						input_size * len(self.lags_sequence   ) + self._number_of_features
     __lowerCAmelCase  :      List[str]    =						d_model
     __lowerCAmelCase  :      Dict    =						encoder_attention_heads
     __lowerCAmelCase  :      List[Any]    =						decoder_attention_heads
     __lowerCAmelCase  :      int    =						encoder_ffn_dim
     __lowerCAmelCase  :      Tuple    =						decoder_ffn_dim
     __lowerCAmelCase  :      Dict    =						encoder_layers
     __lowerCAmelCase  :      int    =						decoder_layers

     __lowerCAmelCase  :      Optional[Any]    =						dropout
     __lowerCAmelCase  :      Union[str, Any]    =						attention_dropout
     __lowerCAmelCase  :      int    =						activation_dropout
     __lowerCAmelCase  :      Tuple    =						encoder_layerdrop
     __lowerCAmelCase  :      int    =						decoder_layerdrop

     __lowerCAmelCase  :      Any    =						activation_function
     __lowerCAmelCase  :      Union[str, Any]    =						init_std

     __lowerCAmelCase  :      str    =						use_cache

     # Informer
     __lowerCAmelCase  :      List[str]    =						attention_type
     __lowerCAmelCase  :      Optional[int]    =						sampling_factor
     __lowerCAmelCase  :      Optional[Any]    =						distil

     super().__init__(is_encoder_decoder=__a	,							**__a   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     return (
         sum(self.embedding_dimension   )
         + self.num_dynamic_real_features
         + self.num_time_features
         + self.num_static_real_features
         + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
     )





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()


