code: import torch
  import time
  import sys
  import os
  import math
  import random
  from termcolor import colored
  
  from .distributions import Empirical
  from . import util, state, TraceMode, PriorInflation, InferenceEngine, InferenceNetwork, ImportanceWeighting, Optimizer, LearningRateScheduler, AddressDictionary
  from .nn import InferenceNetwork as InferenceNetworkBase
  from .nn import OnlineDataset, OfflineDataset, InferenceNetworkFeedForward, InferenceNetworkLSTM
  from .remote import ModelServer
  
  
  class Model():
      def __init__(self, name='Unnamed pyprob model', address_dict_file_name=None):
          super().__init__()
          self.name = name
          self._inference_network = None
          if address_dict_file_name is None:
              self._address_dictionary = None
          else:
              self._address_dictionary = AddressDictionary(address_dict_file_name)
  
      def forward(self):
          raise NotImplementedError()
  
      def _trace_generator(self, trace_mode=TraceMode.PRIOR, prior_inflation=PriorInflation.DISABLED, inference_engine=InferenceEngine.IMPORTANCE_SAMPLING, inference_network=None, observe=None, metropolis_hastings_trace=None, likelihood_importance=1., importance_weighting=ImportanceWeighting.IW0, *args, **kwargs):
          state._init_traces(func=self.forward, trace_mode=trace_mode, prior_inflation=prior_inflation, inference_engine=inference_engine, inference_network=inference_network, observe=observe, metropolis_hastings_trace=metropolis_hastings_trace, address_dictionary=self._address_dictionary, likelihood_importance=likelihood_importance, importance_weighting=importance_weighting)
          while True:
              state._begin_trace()
              result = self.forward(*args, **kwargs)
              trace = state._end_trace(result)
              yield trace
  
      def _traces(self, num_traces=10, trace_mode=TraceMode.PRIOR, prior_inflation=PriorInflation.DISABLED, inference_engine=InferenceEngine.IMPORTANCE_SAMPLING, inference_network=None, map_func=None, silent=False, observe=None, file_name=None, likelihood_importance=1., *args, **kwargs):
          generator = self._trace_generator(trace_mode=trace_mode, prior_inflation=prior_inflation, inference_engine=inference_engine, inference_network=inference_network, observe=observe, likelihood_importance=likelihood_importance, *args, **kwargs)
          traces = Empirical(file_name=file_name)
          if map_func is None:
              map_func = lambda trace: trace
          time_start = time.time()
          if (util._verbosity > 1) and not silent:
              len_str_num_traces = len(str(num_traces))
              print('Time spent  | Time remain.| Progress             | {} | Traces/sec'.format('Trace'.ljust(len_str_num_traces * 2 + 1)))
              prev_duration = 0
          for i in range(num_traces):
              if (util._verbosity > 1) and not silent:
                  duration = time.time() - time_start
                  if (duration - prev_duration > util._print_refresh_rate) or (i == num_traces - 1):
                      prev_duration = duration
                      traces_per_second = (i + 1) / duration
                      print('{} | {} | {} | {}/{} | {:,.2f}       '.format(util.days_hours_mins_secs_str(duration), util.days_hours_mins_secs_str((num_traces - i) / traces_per_second), util.progress_bar(i+1, num_traces), str(i+1).rjust(len_str_num_traces), num_traces, traces_per_second), end='\r')
                      sys.stdout.flush()
              trace = next(generator)
              if trace_mode == TraceMode.PRIOR:
                  log_weight = 1.
              else:
                  log_weight = trace.log_importance_weight
              traces.add(map_func(trace), log_weight)
          if (util._verbosity > 1) and not silent:
              print()
          traces.finalize()
          return traces
  
      def get_trace(self, *args, **kwargs):
          return next(self._trace_generator(*args, **kwargs))
  
      def prior(self, num_traces=10, prior_inflation=PriorInflation.DISABLED, map_func=None, file_name=None, likelihood_importance=1., *args, **kwargs):
          prior = self._traces(num_traces=num_traces, trace_mode=TraceMode.PRIOR, prior_inflation=prior_inflation, map_func=map_func, file_name=file_name, likelihood_importance=likelihood_importance, *args, **kwargs)
          prior.rename('Prior, traces: {:,}'.format(prior.length))
          prior.add_metadata(op='prior', num_traces=num_traces, prior_inflation=str(prior_inflation), likelihood_importance=likelihood_importance)
          return prior
  
      def prior_results(self, num_traces=10, prior_inflation=PriorInflation.DISABLED, map_func=lambda trace: trace.result, file_name=None, likelihood_importance=1., *args, **kwargs):
          return self.prior(num_traces=num_traces, prior_inflation=prior_inflation, map_func=map_func, file_name=file_name, likelihood_importance=likelihood_importance, *args, **kwargs)
  
      def posterior(self, num_traces=10, inference_engine=InferenceEngine.IMPORTANCE_SAMPLING, initial_trace=None, map_func=None, observe=None, file_name=None, thinning_steps=None, likelihood_importance=1., *args, **kwargs):
          if inference_engine == InferenceEngine.IMPORTANCE_SAMPLING:
              posterior = self._traces(num_traces=num_traces, trace_mode=TraceMode.POSTERIOR, inference_engine=inference_engine, inference_network=None, map_func=map_func, observe=observe, file_name=file_name, likelihood_importance=likelihood_importance, *args, **kwargs)
              posterior.rename('Posterior, IS, traces: {:,}, ESS: {:,.2f}'.format(posterior.length, posterior.effective_sample_size))
              posterior.add_metadata(op='posterior', num_traces=num_traces, inference_engine=str(inference_engine), effective_sample_size=posterior.effective_sample_size, likelihood_importance=likelihood_importance)
          elif inference_engine == InferenceEngine.IMPORTANCE_SAMPLING_WITH_INFERENCE_NETWORK:
              if self._inference_network is None:
                  raise RuntimeError('Cannot run inference engine IMPORTANCE_SAMPLING_WITH_INFERENCE_NETWORK because no inference network for this model is available. Use learn_inference_network or load_inference_network first.')
              with torch.no_grad():
                  posterior = self._traces(num_traces=num_traces, trace_mode=TraceMode.POSTERIOR, inference_engine=inference_engine, inference_network=self._inference_network, map_func=map_func, observe=observe, file_name=file_name, likelihood_importance=likelihood_importance, *args, **kwargs)
              posterior.rename('Posterior, IC, traces: {:,}, train. traces: {:,}, ESS: {:,.2f}'.format(posterior.length, self._inference_network._total_train_traces, posterior.effective_sample_size))
              posterior.add_metadata(op='posterior', num_traces=num_traces, inference_engine=str(inference_engine), effective_sample_size=posterior.effective_sample_size, likelihood_importance=likelihood_importance, train_traces=self._inference_network._total_train_traces)
          else:  # inference_engine == InferenceEngine.LIGHTWEIGHT_METROPOLIS_HASTINGS or inference_engine == InferenceEngine.RANDOM_WALK_METROPOLIS_HASTINGS
              posterior = Empirical(file_name=file_name)
              if map_func is None:
                  map_func = lambda trace: trace
              if initial_trace is None:
                  current_trace = next(self._trace_generator(trace_mode=TraceMode.POSTERIOR, inference_engine=inference_engine, observe=observe, *args, **kwargs))
              else:
                  current_trace = initial_trace
  
              time_start = time.time()
              traces_accepted = 0
              samples_reused = 0
              samples_all = 0
              if thinning_steps is None:
                  thinning_steps = 1
  
              if util._verbosity > 1:
                  len_str_num_traces = len(str(num_traces))
                  print('Time spent  | Time remain.| Progress             | {} | Accepted|Smp reuse| Traces/sec'.format('Trace'.ljust(len_str_num_traces * 2 + 1)))
                  prev_duration = 0
              for i in range(num_traces):
                  if util._verbosity > 1:
                      duration = time.time() - time_start
                      if (duration - prev_duration > util._print_refresh_rate) or (i == num_traces - 1):
                          prev_duration = duration
                          traces_per_second = (i + 1) / duration
                          print('{} | {} | {} | {}/{} | {} | {} | {:,.2f}       '.format(util.days_hours_mins_secs_str(duration), util.days_hours_mins_secs_str((num_traces - i) / traces_per_second), util.progress_bar(i+1, num_traces), str(i+1).rjust(len_str_num_traces), num_traces, '{:,.2f}%'.format(100 * (traces_accepted / (i + 1))).rjust(7), '{:,.2f}%'.format(100 * samples_reused / max(1, samples_all)).rjust(7), traces_per_second), end='\r')
                          sys.stdout.flush()
                  candidate_trace = next(self._trace_generator(trace_mode=TraceMode.POSTERIOR, inference_engine=inference_engine, metropolis_hastings_trace=current_trace, observe=observe, *args, **kwargs))
                  log_acceptance_ratio = math.log(current_trace.length_controlled) - math.log(candidate_trace.length_controlled) + candidate_trace.log_prob_observed - current_trace.log_prob_observed
                  for variable in candidate_trace.variables_controlled:
                      if variable.reused:
                          log_acceptance_ratio += torch.sum(variable.log_prob)
                          log_acceptance_ratio -= torch.sum(current_trace.variables_dict_address[variable.address].log_prob)
                          samples_reused += 1
                  samples_all += candidate_trace.length_controlled
  
                  if state._metropolis_hastings_site_transition_log_prob is None:
                      print(colored('Warning: trace did not hit the Metropolis Hastings site, ensure that the model is deterministic except pyprob.sample calls', 'red', attrs=['bold']))
                  else:
                      log_acceptance_ratio += torch.sum(state._metropolis_hastings_site_transition_log_prob)
  
                  # print(log_acceptance_ratio)
                  if math.log(random.random()) < float(log_acceptance_ratio):
                      traces_accepted += 1
                      current_trace = candidate_trace
                  # do thinning
                  if i % thinning_steps == 0:
                      posterior.add(map_func(current_trace))
  
              if util._verbosity > 1:
                  print()
  
              posterior.finalize()
              posterior.rename('Posterior, {}, traces: {:,}{}, accepted: {:,.2f}%, sample reuse: {:,.2f}%'.format('LMH' if inference_engine == InferenceEngine.LIGHTWEIGHT_METROPOLIS_HASTINGS else 'RMH', posterior.length, '' if thinning_steps == 1 else ' (thinning steps: {:,})'.format(thinning_steps), 100 * (traces_accepted / num_traces), 100 * samples_reused / samples_all))
              posterior.add_metadata(op='posterior', num_traces=num_traces, inference_engine=str(inference_engine), likelihood_importance=likelihood_importance, thinning_steps=thinning_steps, num_traces_accepted=traces_accepted, num_samples_reuised=samples_reused, num_samples=samples_all)
          return posterior
  
      def posterior_results(self, num_traces=10, inference_engine=InferenceEngine.IMPORTANCE_SAMPLING, initial_trace=None, map_func=lambda trace: trace.result, observe=None, file_name=None, thinning_steps=None, *args, **kwargs):
          return self.posterior(num_traces=num_traces, inference_engine=inference_engine, initial_trace=initial_trace, map_func=map_func, observe=observe, file_name=file_name, thinning_steps=thinning_steps, *args, **kwargs)
  
      def reset_inference_network(self):
          self._inference_network = None
  
      def learn_inference_network(self, num_traces, num_traces_end=1e9, inference_network=InferenceNetwork.FEEDFORWARD, prior_inflation=PriorInflation.DISABLED, dataset_dir=None, dataset_valid_dir=None, observe_embeddings={}, batch_size=64, valid_size=None, valid_every=None, optimizer_type=Optimizer.ADAM, learning_rate_init=0.001, learning_rate_end=1e-6, learning_rate_scheduler_type=LearningRateScheduler.NONE, momentum=0.9, weight_decay=0., save_file_name_prefix=None, save_every_sec=600, pre_generate_layers=True, distributed_backend=None, distributed_params_sync_every_iter=10000, distributed_num_buckets=None, dataloader_offline_num_workers=0, stop_with_bad_loss=True, log_file_name=None, lstm_dim=512, lstm_depth=1, proposal_mixture_components=10):
          if dataset_dir is None:
              dataset = OnlineDataset(model=self, prior_inflation=prior_inflation)
          else:
              dataset = OfflineDataset(dataset_dir=dataset_dir)
  
          if dataset_valid_dir is None:
              dataset_valid = None
          else:
              dataset_valid = OfflineDataset(dataset_dir=dataset_valid_dir)
  
          if self._inference_network is None:
              print('Creating new inference network...')
              if inference_network == InferenceNetwork.FEEDFORWARD:
                  self._inference_network = InferenceNetworkFeedForward(model=self, observe_embeddings=observe_embeddings, proposal_mixture_components=proposal_mixture_components)
              elif inference_network == InferenceNetwork.LSTM:
                  self._inference_network = InferenceNetworkLSTM(model=self, observe_embeddings=observe_embeddings, lstm_dim=lstm_dim, lstm_depth=lstm_depth, proposal_mixture_components=proposal_mixture_components)
              else:
                  raise ValueError('Unknown inference_network: {}'.format(inference_network))
              if pre_generate_layers:
                  if dataset_valid_dir is not None:
                      self._inference_network._pre_generate_layers(dataset_valid, save_file_name_prefix=save_file_name_prefix)
                  if dataset_dir is not None:
                      self._inference_network._pre_generate_layers(dataset, save_file_name_prefix=save_file_name_prefix)
          else:
              print('Continuing to train existing inference network...')
              print('Total number of parameters: {:,}'.format(self._inference_network._history_num_params[-1]))
  
          self._inference_network.to(device=util._device)
          self._inference_network.optimize(num_traces=num_traces, dataset=dataset, dataset_valid=dataset_valid, num_traces_end=num_traces_end, batch_size=batch_size, valid_every=valid_every, optimizer_type=optimizer_type, learning_rate_init=learning_rate_init, learning_rate_end=learning_rate_end, learning_rate_scheduler_type=learning_rate_scheduler_type, momentum=momentum, weight_decay=weight_decay, save_file_name_prefix=save_file_name_prefix, save_every_sec=save_every_sec, distributed_backend=distributed_backend, distributed_params_sync_every_iter=distributed_params_sync_every_iter, distributed_num_buckets=distributed_num_buckets, dataloader_offline_num_workers=dataloader_offline_num_workers, stop_with_bad_loss=stop_with_bad_loss, log_file_name=log_file_name)
  
      def save_inference_network(self, file_name):
          if self._inference_network is None:
              raise RuntimeError('The model has no trained inference network.')
          self._inference_network._save(file_name)
  
      def load_inference_network(self, file_name):
          self._inference_network = InferenceNetworkBase._load(file_name)
          # The following is due to a temporary hack related with https://github.com/pytorch/pytorch/issues/9981 and can be deprecated by using dill as pickler with torch > 0.4.1
          self._inference_network._model = self
  
      def save_dataset(self, dataset_dir, num_traces, num_traces_per_file, prior_inflation=PriorInflation.DISABLED, *args, **kwargs):
          if not os.path.exists(dataset_dir):
              print('Directory does not exist, creating: {}'.format(dataset_dir))
              os.makedirs(dataset_dir)
          dataset = OnlineDataset(self, None, prior_inflation=prior_inflation)
          dataset.save_dataset(dataset_dir=dataset_dir, num_traces=num_traces, num_traces_per_file=num_traces_per_file, *args, **kwargs)
  
  
  class RemoteModel(Model):
      def __init__(self, server_address='tcp://127.0.0.1:5555', before_forward_func=None, after_forward_func=None, *args, **kwargs):
          self._server_address = server_address
          self._model_server = None
          self._before_forward_func = before_forward_func  # Optional mthod to run before each forward call of the remote model (simulator)
          self._after_forward_func = after_forward_func  # Optional method to run after each forward call of the remote model (simulator)
          super().__init__(*args, **kwargs)
  
      def close(self):
          if self._model_server is not None:
              self._model_server.close()
          super().close()
  
      def forward(self):
          if self._model_server is None:
              self._model_server = ModelServer(self._server_address)
              self.name = '{} running on {}'.format(self._model_server.model_name, self._model_server.system_name)
  
          if self._before_forward_func is not None:
              self._before_forward_func()
          ret = self._model_server.forward()  # Calls the forward run of the remove model (simulator)
          if self._after_forward_func is not None:
              self._after_forward_func()
          return ret
  
apis: torch.sum torch.no_grad