

import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      float										=0.0
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      bool										=True
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      jnp.dtype										=jnp.floataa







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






     """simple docstring"""







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

     for i in range(self.num_layers   ):
        __lowerCAmelCase  :      Any    =						self.in_channels if i == 0 else self.out_channels

        __lowerCAmelCase  :      Tuple    =						FlaxResnetBlockaD(
            in_channels=lowerCAmelCase	,							out_channels=self.out_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
        resnets.append(lowerCAmelCase   )

        __lowerCAmelCase  :      List[Any]    =						FlaxTransformeraDModel(
            in_channels=self.out_channels	,							n_heads=self.num_attention_heads	,							d_head=self.out_channels // self.num_attention_heads	,							depth=1	,							use_linear_projection=self.use_linear_projection	,							only_cross_attention=self.only_cross_attention	,							use_memory_efficient_attention=self.use_memory_efficient_attention	,							dtype=self.dtype	,							)
        attentions.append(lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						resnets
     __lowerCAmelCase  :      Optional[int]    =						attentions

     if self.add_downsample:
        __lowerCAmelCase  :      Any    =						FlaxDownsampleaD(self.out_channels	,							dtype=self.dtype   )







  def __call__(      self			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Any=True   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						()

     for resnet, attn in zip(self.resnets	,							self.attentions   ):
        __lowerCAmelCase  :      str    =						resnet(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )
        __lowerCAmelCase  :      Union[str, Any]    =						attn(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )
        output_states += (hidden_states,)

     if self.add_downsample:
        __lowerCAmelCase  :      int    =						self.downsamplers_a(lowerCAmelCase   )
        output_states += (hidden_states,)

     return hidden_states, output_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      float										=0.0
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      bool										=True
  lowerCamelCase   :      jnp.dtype										=jnp.floataa







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[]

     for i in range(self.num_layers   ):
        __lowerCAmelCase  :      Union[str, Any]    =						self.in_channels if i == 0 else self.out_channels

        __lowerCAmelCase  :      Optional[Any]    =						FlaxResnetBlockaD(
            in_channels=lowerCAmelCase	,							out_channels=self.out_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
        resnets.append(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						resnets

     if self.add_downsample:
        __lowerCAmelCase  :      Any    =						FlaxDownsampleaD(self.out_channels	,							dtype=self.dtype   )







  def __call__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Any=True   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						()

     for resnet in self.resnets:
        __lowerCAmelCase  :      List[Any]    =						resnet(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )
        output_states += (hidden_states,)

     if self.add_downsample:
        __lowerCAmelCase  :      Any    =						self.downsamplers_a(lowerCAmelCase   )
        output_states += (hidden_states,)

     return hidden_states, output_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      float										=0.0
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      bool										=True
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      jnp.dtype										=jnp.floataa







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Union[str, Any]:






     """simple docstring"""







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

     for i in range(self.num_layers   ):
        __lowerCAmelCase  :      Any    =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
        __lowerCAmelCase  :      str    =						self.prev_output_channel if i == 0 else self.out_channels

        __lowerCAmelCase  :      Any    =						FlaxResnetBlockaD(
            in_channels=resnet_in_channels + res_skip_channels	,							out_channels=self.out_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
        resnets.append(lowerCAmelCase   )

        __lowerCAmelCase  :      Optional[Any]    =						FlaxTransformeraDModel(
            in_channels=self.out_channels	,							n_heads=self.num_attention_heads	,							d_head=self.out_channels // self.num_attention_heads	,							depth=1	,							use_linear_projection=self.use_linear_projection	,							only_cross_attention=self.only_cross_attention	,							use_memory_efficient_attention=self.use_memory_efficient_attention	,							dtype=self.dtype	,							)
        attentions.append(lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						resnets
     __lowerCAmelCase  :      Tuple    =						attentions

     if self.add_upsample:
        __lowerCAmelCase  :      str    =						FlaxUpsampleaD(self.out_channels	,							dtype=self.dtype   )







  def __call__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   int=True   )							->     Union[str, Any]:






     """simple docstring"""







     for resnet, attn in zip(self.resnets	,							self.attentions   ):
        # pop res hidden states
        __lowerCAmelCase  :      Optional[int]    =						res_hidden_states_tuple[-1]
        __lowerCAmelCase  :      int    =						res_hidden_states_tuple[:-1]
        __lowerCAmelCase  :      Dict    =						jnp.concatenate((hidden_states, res_hidden_states)	,							axis=-1   )

        __lowerCAmelCase  :      List[Any]    =						resnet(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )
        __lowerCAmelCase  :      Union[str, Any]    =						attn(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )

     if self.add_upsample:
        __lowerCAmelCase  :      Dict    =						self.upsamplers_a(lowerCAmelCase   )

     return hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      int
  lowerCamelCase   :      float										=0.0
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      bool										=True
  lowerCamelCase   :      jnp.dtype										=jnp.floataa







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						[]

     for i in range(self.num_layers   ):
        __lowerCAmelCase  :      Any    =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
        __lowerCAmelCase  :      Optional[int]    =						self.prev_output_channel if i == 0 else self.out_channels

        __lowerCAmelCase  :      List[Any]    =						FlaxResnetBlockaD(
            in_channels=resnet_in_channels + res_skip_channels	,							out_channels=self.out_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
        resnets.append(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						resnets

     if self.add_upsample:
        __lowerCAmelCase  :      Optional[int]    =						FlaxUpsampleaD(self.out_channels	,							dtype=self.dtype   )







  def __call__(      self			:   List[str]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int=True   )							->     Tuple:






     """simple docstring"""







     for resnet in self.resnets:
        # pop res hidden states
        __lowerCAmelCase  :      Dict    =						res_hidden_states_tuple[-1]
        __lowerCAmelCase  :      List[Any]    =						res_hidden_states_tuple[:-1]
        __lowerCAmelCase  :      List[Any]    =						jnp.concatenate((hidden_states, res_hidden_states)	,							axis=-1   )

        __lowerCAmelCase  :      List[Any]    =						resnet(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )

     if self.add_upsample:
        __lowerCAmelCase  :      Optional[Any]    =						self.upsamplers_a(lowerCAmelCase   )

     return hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  lowerCamelCase   :      int
  lowerCamelCase   :      float										=0.0
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      int										=1
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      bool										=False
  lowerCamelCase   :      jnp.dtype										=jnp.floataa







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						[
         FlaxResnetBlockaD(
             in_channels=self.in_channels	,							out_channels=self.in_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
     ]

     __lowerCAmelCase  :      str    =						[]

     for _ in range(self.num_layers   ):
        __lowerCAmelCase  :      Optional[Any]    =						FlaxTransformeraDModel(
            in_channels=self.in_channels	,							n_heads=self.num_attention_heads	,							d_head=self.in_channels // self.num_attention_heads	,							depth=1	,							use_linear_projection=self.use_linear_projection	,							use_memory_efficient_attention=self.use_memory_efficient_attention	,							dtype=self.dtype	,							)
        attentions.append(lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						FlaxResnetBlockaD(
            in_channels=self.in_channels	,							out_channels=self.in_channels	,							dropout_prob=self.dropout	,							dtype=self.dtype	,							)
        resnets.append(lowerCAmelCase   )

     __lowerCAmelCase  :      Tuple    =						resnets
     __lowerCAmelCase  :      List[Any]    =						attentions







  def __call__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]=True   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.resnets[0](lowerCAmelCase	,							lowerCAmelCase   )
     for attn, resnet in zip(self.attentions	,							self.resnets[1:]   ):
        __lowerCAmelCase  :      List[str]    =						attn(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						resnet(lowerCAmelCase	,							lowerCAmelCase	,							deterministic=lowerCAmelCase   )

     return hidden_states





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()


