





import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__lowerCamelCase			:	Dict					    =	logging.getLogger(__name__)



class    A__  (			__snake_case     ):





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_=None			,				A_=None  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  self.layer[current_layer](A_			,				A_			,				head_mask[current_layer]  )

       UpperCamelCase      :   str			      =  layer_outputs[0]

       return hidden_states



@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'			,  __snake_case			,  )
class    A__  (			__snake_case     ):





      def __init__(					self			,				A_  ):
       '''simple docstring'''




       super().__init__(A_  )

       UpperCamelCase      :   Tuple			      =  BertEncoderWithPabee(A_  )

       self.init_weights()
       UpperCamelCase      :   List[str]			      =  0
       UpperCamelCase      :   Optional[Any]			      =  0
       UpperCamelCase      :   List[str]			      =  0

       UpperCamelCase      :   List[Any]			      =  0





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  threshold





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  patience





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  0
       UpperCamelCase      :   List[Any]			      =  0





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  self.inference_layers_num / self.inference_instances_num
       UpperCamelCase      :   List[Any]			      =  (
           F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
           F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
       )
       print(A_  )





      @add_start_docstrings_to_model_forward(A_  )
      def 			__UpperCamelCase(					self			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=False			,				):
       '''simple docstring'''




       if input_ids is not None and inputs_embeds is not None:
        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"  )
       elif input_ids is not None:
        UpperCamelCase      :   Optional[Any]			      =  input_ids.size()
       elif inputs_embeds is not None:
        UpperCamelCase      :   str			      =  inputs_embeds.size()[:-1]
       else:
        raise ValueError("You have to specify either input_ids or inputs_embeds"  )

       UpperCamelCase      :   Tuple			      =  input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
        UpperCamelCase      :   Optional[int]			      =  torch.ones(A_			,				device=A_  )
       if token_type_ids is None:
        UpperCamelCase      :   int			      =  torch.zeros(A_			,				dtype=torch.long			,				device=A_  )

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       UpperCamelCase      :   torch.Tensor			      =  self.get_extended_attention_mask(A_			,				A_			,				A_  )

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if self.config.is_decoder and encoder_hidden_states is not None:
        UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Any			      =  encoder_hidden_states.size()
        UpperCamelCase      :   Any			      =  (encoder_batch_size, encoder_sequence_length)
        if encoder_attention_mask is None:
         UpperCamelCase      :   List[Any]			      =  torch.ones(A_			,				device=A_  )
        UpperCamelCase      :   Optional[int]			      =  self.invert_attention_mask(A_  )
       else:
        UpperCamelCase      :   Union[str, Any]			      =  None

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       UpperCamelCase      :   Optional[int]			      =  self.get_head_mask(A_			,				self.config.num_hidden_layers  )

       UpperCamelCase      :   Union[str, Any]			      =  self.embeddings(
           input_ids=A_			,				position_ids=A_			,				token_type_ids=A_			,				inputs_embeds=A_  )
       UpperCamelCase      :   List[str]			      =  embedding_output

       if self.training:
        UpperCamelCase      :   Dict			      =  []
        for i in range(self.config.num_hidden_layers  ):
         UpperCamelCase      :   Any			      =  self.encoder.adaptive_forward(
             A_			,				current_layer=A_			,				attention_mask=A_			,				head_mask=A_  )

         UpperCamelCase      :   List[str]			      =  self.pooler(A_  )
         UpperCamelCase      :   Union[str, Any]			      =  output_layers[i](output_dropout(A_  )  )
         res.append(A_  )
       elif self.patience == 0:  # Use all layers for inference
        UpperCamelCase      :   Any			      =  self.encoder(
            A_			,				attention_mask=A_			,				head_mask=A_			,				encoder_hidden_states=A_			,				encoder_attention_mask=A_			,				)
        UpperCamelCase      :   Tuple			      =  self.pooler(encoder_outputs[0]  )
        UpperCamelCase      :   List[str]			      =  [output_layers[self.config.num_hidden_layers - 1](A_  )]
       else:
        UpperCamelCase      :   Any			      =  0
        UpperCamelCase      :   Any			      =  None
        UpperCamelCase      :   List[str]			      =  0
        for i in range(self.config.num_hidden_layers  ):
         calculated_layer_num += 1
         UpperCamelCase      :   Dict			      =  self.encoder.adaptive_forward(
             A_			,				current_layer=A_			,				attention_mask=A_			,				head_mask=A_  )

         UpperCamelCase      :   Dict			      =  self.pooler(A_  )
         UpperCamelCase      :   Dict			      =  output_layers[i](A_  )
         if regression:
          UpperCamelCase      :   str			      =  logits.detach()
          if patient_result is not None:
           UpperCamelCase      :   List[str]			      =  patient_result.detach()
          if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
           patient_counter += 1
          else:
           UpperCamelCase      :   Dict			      =  0
         else:
          UpperCamelCase      :   Dict			      =  logits.detach().argmax(dim=1  )
          if patient_result is not None:
           UpperCamelCase      :   int			      =  patient_result.detach().argmax(dim=1  )
          if (patient_result is not None) and torch.all(labels.eq(A_  )  ):
           patient_counter += 1
          else:
           UpperCamelCase      :   List[Any]			      =  0

         UpperCamelCase      :   Optional[Any]			      =  logits
         if patient_counter == self.patience:
          break
        UpperCamelCase      :   Union[str, Any]			      =  [patient_result]
        self.inference_layers_num += calculated_layer_num
        self.inference_instances_num += 1

       return res



@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '			,  __snake_case			,  )
class    A__  (			__snake_case     ):





      def __init__(					self			,				A_  ):
       '''simple docstring'''




       super().__init__(A_  )
       UpperCamelCase      :   Dict			      =  config.num_labels

       UpperCamelCase      :   int			      =  BertModelWithPabee(A_  )
       UpperCamelCase      :   Optional[Any]			      =  nn.Dropout(config.hidden_dropout_prob  )
       UpperCamelCase      :   Union[str, Any]			      =  nn.ModuleList(
           [nn.Linear(config.hidden_size			,				self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

       self.init_weights()





      @add_start_docstrings_to_model_forward(A_  )
      def 			__UpperCamelCase(					self			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				A_=None			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  self.bert(
           input_ids=A_			,				attention_mask=A_			,				token_type_ids=A_			,				position_ids=A_			,				head_mask=A_			,				inputs_embeds=A_			,				output_dropout=self.dropout			,				output_layers=self.classifiers			,				regression=self.num_labels == 1			,				)

       UpperCamelCase      :   Any			      =  (logits[-1],)

       if labels is not None:
        UpperCamelCase      :   Any			      =  None
        UpperCamelCase      :   Optional[int]			      =  0
        for ix, logits_item in enumerate(A_  ):
         if self.num_labels == 1:
          #  We are doing regression
          UpperCamelCase      :   Tuple			      =  MSELoss()
          UpperCamelCase      :   Any			      =  loss_fct(logits_item.view(-1  )			,				labels.view(-1  )  )
         else:
          UpperCamelCase      :   int			      =  CrossEntropyLoss()
          UpperCamelCase      :   List[str]			      =  loss_fct(logits_item.view(-1			,				self.num_labels  )			,				labels.view(-1  )  )
         if total_loss is None:
          UpperCamelCase      :   Optional[int]			      =  loss
         else:
          total_loss += loss * (ix + 1)
         total_weights += ix + 1
        UpperCamelCase      :   Union[str, Any]			      =  (total_loss / total_weights,) + outputs

       return outputs







import numpy as np
import qiskit



def 						A_							(	_lowerCAmelCase = 8				,  _lowerCAmelCase = None	)		->					str:
 UpperCamelCase      :   Tuple			      =  np.random.default_rng(seed=_lowerCAmelCase	)

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

 # Quantum Circuit to simulate BB84
 UpperCamelCase      :   List[Any]			      =  qiskit.QuantumCircuit(_lowerCAmelCase				,  name="BB84"	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 UpperCamelCase      :   Union[str, 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.
 UpperCamelCase      :   Tuple			      =  qiskit.execute(_lowerCAmelCase				,  _lowerCAmelCase				,  shots=1				,  seed_simulator=_lowerCAmelCase	)
 # Returns the result of measurement.
 UpperCamelCase      :   Optional[Any]			      =  job.result().get_counts(_lowerCAmelCase	).most_frequent()

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

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


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

    testmod()
