code: 
  
  
  
  
  
  
  """simple docstring"""
  import copy
  import logging
  import random
  
  import joblib
  import numpy as np
  import torch
  import torch.nn as nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  
  from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup
  
  
  snake_case   :   Any    		=		logging.getLogger(__name__)
  
  
  def        A	(							__snake_case:   Any     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         random.seed(__snake_case     )
         np.random.seed(__snake_case     )
         torch.manual_seed(__snake_case     )
         torch.cuda.manual_seed_all(__snake_case     )
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         model.eval()
         __magic_name__										=						next(model.parameters()     ).device
         __magic_name__										=						1
         __magic_name__										=						torch.zeros((eval_batch_size, context_len)   ,    dtype=torch.long   ,    device=__snake_case     )
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
         __magic_name__										=						torch.zeros(1   ,    device=__snake_case     )
         __magic_name__										=						0
         for batch in eval_dataloader:
                batch.to(__snake_case     )
                # pad
                context.zero_()
                for i in range(__snake_case     ):
                       __magic_name__										=						batch[i]
                __magic_name__										=						model(__snake_case   ,    labels=__snake_case     )
                eval_loss += outputs[0].sum().item()
                nb_eval_examples += batch.size(0     )
         __magic_name__										=						eval_loss / nb_eval_examples
         __magic_name__										=						torch.exp(__snake_case     )
         model.train()
         return perplexity
  
  
  def        A	(							__snake_case:   Optional[int]="gpt2"     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						GPTaLMHeadModel.from_pretrained(__snake_case   ,    output_hidden_states=__snake_case     )
         torch.save(model.state_dict()   ,    model_name + 'local.pt'     )
         return model
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   List[Any]     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						copy.deepcopy(__snake_case     )
         model.to(__snake_case     )
  
         __magic_name__										=						['bias', 'LayerNorm.weight']
         __magic_name__										=						[
             {
                 'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
                 'weight_decay': 0.0,
             },
             {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )], 'weight_decay': 0.0},
         ]
         __magic_name__										=						AdamW(__snake_case   ,    lr=5E-5   ,    eps=1E-8     )
         __magic_name__										=						get_linear_schedule_with_warmup(__snake_case   ,    0   ,    __snake_case     )
         torch.cuda.empty_cache()
         return model, lm_optimizer, lm_scheduler
  
  
  def        A	(							__snake_case:   Optional[Any]   ,    __snake_case:   Any   ,    __snake_case:   Optional[int]   ,    __snake_case:   Any     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						np.array(__snake_case     ).mean()
         __magic_name__										=						np.array(__snake_case     ).std()
         __magic_name__										=						(real_perps - avg) / std
         __magic_name__										=						list(zip(__snake_case   ,    __snake_case   ,    __snake_case     )     )
         joblib.dump(__snake_case   ,    __snake_case     )
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Optional[int]   ,    __snake_case:   int   ,    __snake_case:   str   ,    __snake_case:   List[str]   ,    __snake_case:   List[Any]   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Optional[int]="dev.jbl"   ,    __snake_case:   List[str]=recopy_gpta   ,    )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						[]
         __magic_name__										=						[]
         __magic_name__										=						[]
  
         # Initialize the transformer model
         __magic_name__										=						copy.deepcopy(__snake_case     )
         orig_model.to(device='cpu'     )
         torch.cuda.empty_cache()
  
         # Compute perplexity of initial transformer model for comparison
         model.train()
         __magic_name__							,     __magic_name__							,     __magic_name__										=						recopy_model(__snake_case   ,    __snake_case   ,    __snake_case     )
  
         for step in tqdm(range(__snake_case     )     ):
                __magic_name__										=						torch.zeros((1, context_len)   ,    dtype=torch.long   ,    device=__snake_case     )
                __magic_name__										=						random.choice(__snake_case     )
                __magic_name__										=						random.randint(0   ,    len(story[0]     ) - context_len - 1     )
                __magic_name__										=						story[0][start : start + context_len]
                lm_optimizer.zero_grad()
                __magic_name__										=						model(__snake_case   ,    labels=__snake_case     )
                __magic_name__										=						outputs[0]
                __magic_name__										=						compute_perplexity(__snake_case   ,    __snake_case   ,    __snake_case     )
                model.train()
                lm_loss.backward()
                # Do LM backprop
                torch.nn.utils.clip_grad_norm_(model.parameters()   ,    3.0     )
                lm_optimizer.step()
                lm_scheduler.step()  # Update learning rate schedule
  
                # Compute perplexity after back-propagating on the selected context
                __magic_name__										=						compute_perplexity(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                # Periodically save the stored (X, IG(X)) pairs
                if step % 1_0_0_0 == 0 and step > 1:
                       intermittent_save(__snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case     )
  
                # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
                __magic_name__							,     __magic_name__							,     __magic_name__										=						recopy_model(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                past_perps.append(past_perp.item()     )
                real_perps.append(orig_perp - real_perp.item()     )
                contexts.append(np.array(context.cpu()     )     )
  
         intermittent_save(__snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case     )
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   int="data/tokenized_stories_train_wikitext103.jbl"   ,    __snake_case:   int=1_0_0   ,    __snake_case:   Optional[int]=1_0_2_6   ,    __snake_case:   Dict=True     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						joblib.load(__snake_case     )
         print('data loaded'     )
         __magic_name__										=						[]
         if trim:
                for i, example in enumerate(__snake_case     ):
                       if len(example[0]     ) > min_len:
                              __magic_name__										=						random.randint(0   ,    len(example[0]     ) - context_len - 1     )
                              objective_set.append(example[0, start : start + context_len]     )
                       if len(__snake_case     ) >= number:
                              break
                __magic_name__										=						[]
                for j in range(i + 1   ,    len(__snake_case     )     ):
                       if len(data[j][0]     ) > min_len:
                              train_data.append(data[j]     )
         else:
                __magic_name__										=						data[0:number]
                __magic_name__										=						data[number:]
  
         joblib.dump(__snake_case   ,    'objective_set.jbl'     )
         print('objective set saved'     )
         return train_data, objective_set
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Optional[int]   ,    __snake_case:   Dict   ,    __snake_case:   List[str]=5_0   ,    __snake_case:   str="secondary_learner.pt"     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
         # We will use the first 512 pairs from our dataset as a test set for
         # our secondary learner and the rest to train
         __magic_name__										=						train_dataset[:5_1_2]
         __magic_name__										=						train_dataset[5_1_2:]
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
  
         # secondary learner model set up
         __magic_name__										=						nn.MSELoss()
         __magic_name__										=						nn.MSELoss(reduction='sum'     )
         secondary_learner.to(__snake_case     )
         __magic_name__										=						torch.optim.Adam(secondary_learner.parameters()   ,    lr=0.00001     )
         secondary_learner.train()
  
         # TODO in original code this is written as number of actual batches seen
         # not number of items seen but other places it is number of items instead.
         # improve consistency! changed this to epochs for clarity
         __magic_name__										=						float('inf'     )
         # Iterate through batches until we've used max_steps batches
         for epoch in range(int(__snake_case     )     ):
                __magic_name__										=						0.0
                secondary_learner.train()
                for step, batch in enumerate(__snake_case     ):
                       __magic_name__										=						batch[0].to(__snake_case     )
                       __magic_name__										=						batch[1].to(__snake_case     )
                       __magic_name__										=						secondary_learner(__snake_case     )
                       q_optimizer.zero_grad()
                       __magic_name__										=						loss(__snake_case   ,    real_q.float()     )
                       q_loss.backward()
                       q_optimizer.step()
                       tr_q_loss += q_loss.item()
  
                       # model trains fairly quickly so we won't wait for a full epoch
                       # eval is triggered at eval_freq and end of epochs
                       if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__snake_case     )):
                              __magic_name__										=						tr_q_loss / (step + 1)
  
                              secondary_learner.eval()
                              __magic_name__										=						0.0
                              __magic_name__										=						0.0
                              __magic_name__										=						[]
                              __magic_name__										=						[]
                              # Compute performance of the secondary learner after this batch
                              for stepa, batcha in enumerate(__snake_case     ):
                                     __magic_name__										=						batcha[0].to(__snake_case     )
                                     __magic_name__										=						batcha[1].to(__snake_case     )
                                     __magic_name__										=						secondary_learner(__snake_case     )
                                     q_lossa += test_loss(__snake_case   ,    __snake_case     ).item()
                                     sum_qa += torch.sum(__snake_case     ).item()
                                     for ei, i in enumerate(predicted_qa.cpu().detach().numpy()     ):
                                            predicted.append(i.item()     )
                                     for ei, i in enumerate(real_qa.cpu().detach().numpy()     ):
                                            actual.append(i.item()     )
  
                              q_lossa /= len(__snake_case     )
                              print(
                                  'Epoch: '   ,    __snake_case   ,    'step: '   ,    __snake_case   ,    'Avg. q:'   ,    sum_qa / len(__snake_case     )   ,    'Train Loss: '   ,    __snake_case   ,    'Test Loss: '   ,    __snake_case   ,    )
                              if q_lossa < best_test_loss:
                                     joblib.dump((predicted, actual)   ,    'pred_vs_actual.jbl'     )
                                     torch.save(secondary_learner.state_dict()   ,    __snake_case     )
                                     __magic_name__										=						q_lossa
  
                       secondary_learner.train()
         return secondary_learner
  
  
  
  
  
  
  class 					UpperCamelCase__   (							nn.Module):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  List[Any]							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             super(UpperCamelCase_							,						self   ).__init__()
             __magic_name__										=						model.transformer.wte
             __magic_name__										=						copy.deepcopy(model.transformer.wte.weight   )
             __magic_name__										=						nn.Convad(self.embeddings.weight.size(1   )							,						2_5_6							,						3							,						padding=1   )
             __magic_name__										=						nn.Sequential(nn.Linear(2_5_6							,						3_2   )							,						nn.Dropout(p=0.1   )							,						nn.Linear(3_2							,						3_2   )							,						nn.Linear(3_2							,						1   )   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						torch.max(self.conv(self.embeddings(UpperCamelCase_   ).squeeze(1   ).transpose(1							,						2   )   )							,						2   )[0]
             __magic_name__										=						self.fc(UpperCamelCase_   )
             return qs.squeeze(1   )
  
  
  
  
  
  
  
      @classmethod
      def 							a__ (		cls   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						cls(UpperCamelCase_   )  # this calls __init__
             __magic_name__										=						torch.load(UpperCamelCase_   )
             secondary_learner.load_state_dict(UpperCamelCase_   )
             __magic_name__										=						model.transformer.wte
             __magic_name__										=						copy.deepcopy(model.transformer.wte.weight   )
             return secondary_learner
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1     )
  
         # First, let's deal with the case in which oracle is balanced
         if case == "balanced":
                # First generate a random number that tells us which CNOTs to
                # wrap in X-gates:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__snake_case   ,    F"""0{num_qubits}b"""     )
                # Next, we place the first X-gates. Each digit in our binary string
                # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
                # we apply an X-gate to that qubit:
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
  
      # Case in which oracle is constant
         if case == "constant":
                # First decide what the fixed output of the oracle will be
                # (either always 0 or always 1)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__snake_case     )
  
  
  if __name__ == "__main__":
   print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
   print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")