






"""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







"""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)}""")