text: """Compressed sensing main script"""
  # pylint: disable=C0301,C0103,C0111
  
  from __future__ import division
  import os
  from argparse import ArgumentParser
  import numpy as np
  import utils
  import yaml
  
  
  def main(hparams):
  
      # Set up some stuff accoring to hparams
      hparams.n_input = np.prod(hparams.image_shape)
      utils.print_hparams(hparams)
  
      # get inputs
      xs_dict = utils.model_input(hparams)
  
      # get estimator
      estimator = utils.get_estimator(hparams, hparams.model_type)
  
      # set up folders, etc for checkpointing 
      utils.setup_checkpointing(hparams)
  
      # get saved results
      measurement_losses, l2_losses, z_hats, likelihoods = utils.load_checkpoints(hparams)
  
      x_batch_dict = {}
      x_hats_dict = {}
  
      A = utils.get_A(hparams)
  
  
  
      for key, x in xs_dict.items():
          if not hparams.not_lazy:
              # If lazy, first check if the image has already been
              # saved before . If yes, then skip this image.
              save_path = utils.get_save_path(hparams, key)
              is_saved = os.path.isfile(save_path) 
              if is_saved:
                  continue
  
          x_batch_dict[key] = x
          if len(x_batch_dict) < hparams.batch_size:
              continue
  
          # Reshape input
          x_batch_list = [x.reshape(1, hparams.n_input) for _, x in x_batch_dict.items()]
          x_batch = np.concatenate(x_batch_list)
  
          # Construct noise and measurements
          noise_batch = utils.get_noise(hparams)
          y_batch = utils.get_measurements(x_batch, A, noise_batch, hparams)
  
          # Construct estimates 
          x_hat_batch, z_hat_batch, likelihood_batch = estimator(A, y_batch, hparams)
  
          for i, key in enumerate(x_batch_dict.keys()):
              x = xs_dict[key]
              y_train = y_batch[i]
              x_hat = x_hat_batch[i]
  
              # Save the estimate
              x_hats_dict[key] = x_hat
  
              # Compute and store measurement and l2 loss
              measurement_losses[key] = utils.get_measurement_loss(x_hat, A, y_train, hparams)
              l2_losses[key] = utils.get_l2_loss(x_hat, x)
              z_hats[key] = z_hat_batch[i]
              likelihoods[key] = likelihood_batch[i]
  
          print('Processed upto image {0} / {1}'.format(key+1, len(xs_dict)))
  
          # Checkpointing
          if (not hparams.debug) and ((key+1) % hparams.checkpoint_iter == 0):
              utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, z_hats, likelihoods, hparams)
              x_hats_dict = {} 
              print('\nProcessed and saved first ', key+1, 'images\n')
  
          x_batch_dict = {}
  
      # Final checkpoint
      if not hparams.debug:
          utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, z_hats, likelihoods, hparams)
          print('\nProcessed and saved all {0} image(s)\n'.format(len(xs_dict)))
  
      if hparams.print_stats:
          print(hparams.model_type)
          measurement_loss_list = list(measurement_losses.values())
          l2_loss_list = list(l2_losses.values())
          mean_m_loss = np.mean(measurement_loss_list)
          mean_l2_loss = np.mean(l2_loss_list)
          print('mean measurement loss = {0}'.format(mean_m_loss))
          print('mean l2 loss = {0}'.format(mean_l2_loss))
  
      if hparams.image_matrix > 0:
          utils.image_matrix(xs_dict, x_hats_dict, hparams)
  
      # Warn the user that some things were not processsed
      if len(x_batch_dict) > 0:
          print('\nDid NOT process last {} images because they did not fill up the last batch.'.format(len(x_batch_dict)))
          print('Consider rerunning lazily with a smaller batch size.')
  
  if __name__ == '__main__':
      PARSER = ArgumentParser()
  
      # Pretrained model
      PARSER.add_argument('--checkpoint-path', type=str, default='models/ncsnv2_ffhq/checkpoint_80000.pth', help='Path to pretrained model')
      PARSER.add_argument('--net', type=str, default='ncsnv2', help='Name of model. options = [glow, stylegan2, ncsnv2, dd]')
  
      # Input
      PARSER.add_argument('--dataset', type=str, default='celebA', help='Dataset to use')
      PARSER.add_argument('--image-size', type=int, default=256, help='size of image')
      PARSER.add_argument('--input-type', type=str, default='full-input', help='Where to take input from')
      PARSER.add_argument('--num-input-images', type=int, default=2, help='number of input images')
      PARSER.add_argument('--batch-size', type=int, default=2, help='How many examples are processed together')
      PARSER.add_argument('--cache-dir', type=str, default='cache', help='cache directory for model weights')
      PARSER.add_argument('--ncsnv2-configs-file', type=str, default='./ncsnv2/configs/ffhq.yml', help='location of ncsnv2 config file')
  
  
      # Problem definition
      PARSER.add_argument('--measurement-type', type=str, default='gaussian', help='measurement type')
      PARSER.add_argument('--noise-std', type=float, default=1, help='expected norm of noise')
      PARSER.add_argument('--measurement-noise-type', type=str, default='gaussian', help='type of noise')
  
      # Measurement type specific hparams
      PARSER.add_argument('--num-measurements', type=int, default=200, help='number of gaussian measurements')
      PARSER.add_argument('--downsample', type=int, default=None, help='downsampling factor')
  
      # Model
      PARSER.add_argument('--model-type', type=str, default=None, required=True, help='model used for estimation. options=[map, langevin, pulse, dd]')
      PARSER.add_argument('--mloss-weight', type=float, default=-1, help='L2 measurement loss weight')
      PARSER.add_argument('--zprior-weight', type=float, default=-1, help='weight on z prior')
      PARSER.add_argument('--zprior-sdev', type=float, default=1.0, help='standard deviation for target distributon of  z')
      PARSER.add_argument('--zprior-init-sdev', type=float, default=1.0, help='standard deviation to initialize z')
      PARSER.add_argument('--T', type=float, default=-1, help='number of iterations for each level of noise in Langevin annealing')
      PARSER.add_argument('--L', type=float, default=-1, help='number of noise levels for annealing Langevin')
      PARSER.add_argument('--sigma-init', type=float, default=-1, help='initial noise level for annealing langevin')
      PARSER.add_argument('--sigma-final', type=float, default=-1, help='final noise level for annealing Langevin')
      PARSER.add_argument('--error-threshold', type=float, default=0., help='threshold for measurement error before restart')
      PARSER.add_argument('--num-noise-variables', type=int, default=5, help='STYLEGAN2 : number of noise variables in  to optimize')
  
      # NN specfic hparams
      PARSER.add_argument('--optimizer-type', type=str, default='sgd', help='Optimizer type')
      PARSER.add_argument('--learning-rate', type=float, default=0.4, help='learning rate')
      PARSER.add_argument('--momentum', type=float, default=0., help='momentum value')
      PARSER.add_argument('--max-update-iter', type=int, default=1000, help='maximum updates to z')
      PARSER.add_argument('--num-random-restarts', type=int, default=1, help='number of random restarts')
      PARSER.add_argument('--decay-lr', action='store_true', help='whether to decay learning rate')
  
      #PULSE arguments
      PARSER.add_argument('--seed', type=int, help='manual seed to use')
      PARSER.add_argument('--loss-str', type=str, default="100*L2+0.05*GEOCROSS", help='Loss function to use')
      PARSER.add_argument('--pulse-eps', type=float, default=2e-3, help='Target for downscaling loss (L2)')
      PARSER.add_argument('--noise-type', type=str, default='trainable', help='zero, fixed, or trainable')
      PARSER.add_argument('--tile-latent', action='store_true', help='Whether to forcibly tile the same latent 18 times')
      PARSER.add_argument('--lr-schedule', type=str, default='linear1cycledrop', help='fixed, linear1cycledrop, linear1cycle')
  
      # Output
      PARSER.add_argument('--not-lazy', action='store_true', help='whether the evaluation is lazy')
      PARSER.add_argument('--debug', action='store_true', help='debug mode does not save images or stats')
      PARSER.add_argument('--print-stats', action='store_true', help='whether to print statistics')
      PARSER.add_argument('--checkpoint-iter', type=int, default=50, help='checkpoint every x batches')
      PARSER.add_argument('--image-matrix', type=int, default=0,
                          help='''
                                  0 = 00 =      no       image matrix,
                                  1 = 01 =          show image matrix
                                  2 = 10 = save          image matrix
                                  3 = 11 = save and show image matrix
                               '''
                         )
      PARSER.add_argument('--gif', action='store_true', help='whether to create a gif')
      PARSER.add_argument('--gif-iter', type=int, default=1, help='save gif frame every x iter')
      PARSER.add_argument('--gif-dir', type=str, default='', help='where to store gif frames')
  
      PARSER.add_argument('--cuda', dest='cuda', action='store_true')
      PARSER.add_argument('--no-cuda', dest='cuda', action='store_false')
      PARSER.set_defaults(cuda=True)
  
      PARSER.add_argument('--project', dest='project', action='store_true')
      PARSER.add_argument('--no-project', dest='project', action='store_false')
      PARSER.set_defaults(project=True)
  
      PARSER.add_argument('--annealed', dest='annealed', action='store_true')
      PARSER.add_argument('--no-annealed', dest='annealed', action='store_false')
      PARSER.set_defaults(annealed=False)
  
      HPARAMS = PARSER.parse_args()
      HPARAMS.input_path = f'./test_images/{HPARAMS.dataset}'
      if HPARAMS.cuda:
          HPARAMS.device='cuda:0'
      else:
          HPARAMS.device = 'cpu:0'
  
  
      if HPARAMS.net == 'ncsnv2':
          with open(HPARAMS.ncsnv2_configs_file, 'r') as f:
              HPARAMS.ncsnv2_configs = yaml.load(f)
          HPARAMS.ncsnv2_configs['sampling']['step_lr'] = HPARAMS.learning_rate
          HPARAMS.ncsnv2_configs['sampling']['n_steps_each'] = int(HPARAMS.T)
          HPARAMS.ncsnv2_configs['model']['sigma_begin'] = int(HPARAMS.sigma_init)
          HPARAMS.ncsnv2_configs['model']['sigma_end'] = HPARAMS.sigma_final
  
      HPARAMS.image_shape = (3, HPARAMS.image_size, HPARAMS.image_size)
      HPARAMS.n_input = np.prod(HPARAMS.image_shape)
  
      if HPARAMS.measurement_type == 'circulant':
          HPARAMS.train_indices = np.random.randint(0, HPARAMS.n_input, HPARAMS.num_measurements )
          HPARAMS.sign_pattern = np.float32((np.random.rand(1,HPARAMS.n_input) < 0.5)*2 - 1.)
      elif HPARAMS.measurement_type == 'superres':
          HPARAMS.y_shape = (HPARAMS.batch_size, HPARAMS.image_shape[0], HPARAMS.image_size//HPARAMS.downsample,HPARAMS.image_size//HPARAMS.downsample)
          HPARAMS.num_measurements = np.prod(HPARAMS.y_shape[1:])
      elif HPARAMS.measurement_type == 'project':
          HPARAMS.y_shape = (HPARAMS.batch_size, HPARAMS.image_shape[0], HPARAMS.image_size, HPARAMS.image_size)
          HPARAMS.num_measurements = np.prod(HPARAMS.y_shape[1:])
  
  
      from utils import view_image 
  
      if HPARAMS.mloss_weight < 0:
          HPARAMS.mloss_weight = None
      if HPARAMS.zprior_weight < 0:
          HPARAMS.zprior_weight = None
      if HPARAMS.annealed:
          if HPARAMS.T < 0:
              HPARAMS.T = 200
          if HPARAMS.L < 0:
              HPARAMS.L = 10
          if HPARAMS.sigma_final < 0:
              HPARAMS.sigma_final = HPARAMS.noise_std
          if HPARAMS.sigma_init < 0:
              HPARAMS.sigma_init =  100 * HPARAMS.sigma_final
          HPARAMS.max_update_iter = int(HPARAMS.T * HPARAMS.L)
  
      main(HPARAMS)
  
  
  