text: import argparse
  from difflib import SequenceMatcher
  import os
  
  import numpy as np
  import tensorflow as tf
  import torch
  
  from TTS.utils.io import load_config
  from TTS.vocoder.tf.utils.convert_torch_to_tf_utils import (
      compare_torch_tf, convert_tf_name, transfer_weights_torch_to_tf)
  from TTS.vocoder.tf.utils.generic_utils import \
      setup_generator as setup_tf_generator
  from TTS.vocoder.tf.utils.io import save_checkpoint
  from TTS.vocoder.utils.generic_utils import setup_generator
  
  # prevent GPU use
  os.environ['CUDA_VISIBLE_DEVICES'] = ''
  
  # define args
  parser = argparse.ArgumentParser()
  parser.add_argument('--torch_model_path',
                      type=str,
                      help='Path to target torch model to be converted to TF.')
  parser.add_argument('--config_path',
                      type=str,
                      help='Path to config file of torch model.')
  parser.add_argument(
      '--output_path',
      type=str,
      help='path to output file including file name to save TF model.')
  args = parser.parse_args()
  
  # load model config
  config_path = args.config_path
  c = load_config(config_path)
  num_speakers = 0
  
  # init torch model
  model = setup_generator(c)
  checkpoint = torch.load(args.torch_model_path,
                          map_location=torch.device('cpu'))
  state_dict = checkpoint['model']
  model.load_state_dict(state_dict)
  model.remove_weight_norm()
  state_dict = model.state_dict()
  
  # init tf model
  model_tf = setup_tf_generator(c)
  
  common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
  # get tf_model graph by passing an input
  # B x D x T
  dummy_input = tf.random.uniform((7, 80, 64), dtype=tf.float32)
  mel_pred = model_tf(dummy_input, training=False)
  
  # get tf variables
  tf_vars = model_tf.weights
  
  # match variable names with fuzzy logic
  torch_var_names = list(state_dict.keys())
  tf_var_names = [we.name for we in model_tf.weights]
  var_map = []
  for tf_name in tf_var_names:
      # skip re-mapped layer names
      if tf_name in [name[0] for name in var_map]:
          continue
      tf_name_edited = convert_tf_name(tf_name)
      ratios = [
          SequenceMatcher(None, torch_name, tf_name_edited).ratio()
          for torch_name in torch_var_names
      ]
      max_idx = np.argmax(ratios)
      matching_name = torch_var_names[max_idx]
      del torch_var_names[max_idx]
      var_map.append((tf_name, matching_name))
  
  # pass weights
  tf_vars = transfer_weights_torch_to_tf(tf_vars, dict(var_map), state_dict)
  
  # Compare TF and TORCH models
  # check embedding outputs
  model.eval()
  dummy_input_torch = torch.ones((1, 80, 10))
  dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
  dummy_input_tf = tf.transpose(dummy_input_tf, perm=[0, 2, 1])
  dummy_input_tf = tf.expand_dims(dummy_input_tf, 2)
  
  out_torch = model.layers[0](dummy_input_torch)
  out_tf = model_tf.model_layers[0](dummy_input_tf)
  out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
  
  assert compare_torch_tf(out_torch, out_tf_) < 1e-5
  
  for i in range(1, len(model.layers)):
      print(f"{i} -> {model.layers[i]} vs {model_tf.model_layers[i]}")
      out_torch = model.layers[i](out_torch)
      out_tf = model_tf.model_layers[i](out_tf)
      out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
      diff = compare_torch_tf(out_torch, out_tf_)
      assert diff < 1e-5, diff
  
  torch.manual_seed(0)
  dummy_input_torch = torch.rand((1, 80, 100))
  dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
  model.inference_padding = 0
  model_tf.inference_padding = 0
  output_torch = model.inference(dummy_input_torch)
  output_tf = model_tf(dummy_input_tf, training=False)
  assert compare_torch_tf(output_torch, output_tf) < 1e-5, compare_torch_tf(
      output_torch, output_tf)
  
  # save tf model
  save_checkpoint(model_tf, checkpoint['step'], checkpoint['epoch'],
                  args.output_path)
  print(' > Model conversion is successfully completed :).')
  