






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__UpperCAmelCase      (					a_							,					a_=1):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.')[n_shave_prefix_segments:])
							else:
														return ".".join(path.split('.')[:n_shave_prefix_segments])





def 						__UpperCAmelCase      (					a_							,					a_=0):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item
														snake_case_           =							new_item.replace('block.'							,					'resnets.')
														snake_case_           =							new_item.replace('conv_shorcut'							,					'conv1')
														snake_case_           =							new_item.replace('in_shortcut'							,					'conv_shortcut')
														snake_case_           =							new_item.replace('temb_proj'							,					'time_emb_proj')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)

														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_=0							,					a_=False):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					snake_case_           =							new_item.replace('attn'							,					'attentions')
														snake_case_           =							new_item.replace('.k.'							,					'.key.')
														snake_case_           =							new_item.replace('.v.'							,					'.value.')
														snake_case_           =							new_item.replace('.q.'							,					'.query.')

														snake_case_           =							new_item.replace('proj_out'							,					'proj_attn')
														snake_case_           =							new_item.replace('norm'							,					'group_norm')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)
														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=None							,					a_=None							,					a_=None):
							assert isinstance(a_							,					a_), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.')

														for path, path_map in attention_paths_to_split.items():
																					snake_case_           =							old_checkpoint[path]
																					snake_case_           =							old_tensor.shape[0] // 3

																					snake_case_           =							(-1, channels) if len(old_tensor.shape) == 3 else (-1)

																					snake_case_           =							old_tensor.shape[0] // config.get('num_head_channels'							,					1) // 3

																					snake_case_           =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
																					snake_case_      ,		snake_case_      ,		snake_case_           =							old_tensor.split(channels // num_heads							,					dim=1)

																					snake_case_           =							query.reshape(a_).squeeze()
																					snake_case_           =							key.reshape(a_).squeeze()
																					snake_case_           =							value.reshape(a_).squeeze()

							for path in paths:
														snake_case_           =							path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														snake_case_           =							new_path.replace('down.'							,					'down_blocks.')
														snake_case_           =							new_path.replace('up.'							,					'up_blocks.')

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												snake_case_           =							new_path.replace(replacement['old']							,					replacement['new'])

														if "attentions" in new_path:
																					snake_case_           =							old_checkpoint[path['old']].squeeze()
														else:
																					snake_case_           =							old_checkpoint[path['old']]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['temb.dense.0.weight']
							snake_case_           =							checkpoint['temb.dense.0.bias']
							snake_case_           =							checkpoint['temb.dense.1.weight']
							snake_case_           =							checkpoint['temb.dense.1.bias']

							snake_case_           =							checkpoint['norm_out.weight']
							snake_case_           =							checkpoint['norm_out.bias']

							snake_case_           =							checkpoint['conv_in.weight']
							snake_case_           =							checkpoint['conv_in.bias']
							snake_case_           =							checkpoint['conv_out.weight']
							snake_case_           =							checkpoint['conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''down.{i}.downsample.op.weight'''
																					]
																					snake_case_           =							checkpoint[f'''down.{i}.downsample.op.bias''']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[f'''up.{i}.upsample.conv.bias''']

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['encoder.norm_out.weight']
							snake_case_           =							checkpoint['encoder.norm_out.bias']

							snake_case_           =							checkpoint['encoder.conv_in.weight']
							snake_case_           =							checkpoint['encoder.conv_in.bias']
							snake_case_           =							checkpoint['encoder.conv_out.weight']
							snake_case_           =							checkpoint['encoder.conv_out.bias']

							snake_case_           =							checkpoint['decoder.norm_out.weight']
							snake_case_           =							checkpoint['decoder.norm_out.bias']

							snake_case_           =							checkpoint['decoder.conv_in.weight']
							snake_case_           =							checkpoint['decoder.conv_in.bias']
							snake_case_           =							checkpoint['decoder.conv_out.weight']
							snake_case_           =							checkpoint['decoder.conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.bias'''
																					]

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.bias'''
																					]

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							snake_case_           =							checkpoint['quant_conv.weight']
							snake_case_           =							checkpoint['quant_conv.bias']
							if "quantize.embedding.weight" in checkpoint:
														snake_case_           =							checkpoint['quantize.embedding.weight']
							snake_case_           =							checkpoint['post_quant_conv.weight']
							snake_case_           =							checkpoint['post_quant_conv.bias']

							return new_checkpoint


if __name__ == "__main__":
	lowercase  							= argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)

	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The config json file corresponding to the architecture.",
	)

	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

	lowercase  							= parser.parse_args()
	lowercase  							= torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		lowercase  							= json.loads(f.read())

	# unet case
	lowercase  							= {key.split(".")[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		lowercase  							= convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		lowercase  							= convert_ddpm_checkpoint(checkpoint, config)

	if "ddpm" in config:
		del config["ddpm"]

	if config["_class_name"] == "VQModel":
		lowercase  							= VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowercase  							= AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowercase  							= UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		lowercase  							= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

		lowercase  							= DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)













from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							seed(1)
							return [gauss(a_							,					a_) for _ in range(a_)]





def 						__UpperCAmelCase      (					a_							,					a_):
							return [k for k in range(a_) for _ in range(instance_count[k])]





def 						__UpperCAmelCase      (					a_							,					a_):
							return sum(a_) / instance_count





def 						__UpperCAmelCase      (					a_							,					a_):
							return instance_count / total_count





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							[]  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(a_)):
														# for loop iterates over number of elements in inner layer of items
														for j in range(len(items[i])):
																					# appending squared differences to 'squared_diff' list
																					squared_diff.append((items[i][j] - means[i]) ** 2)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
							snake_case_           =							len(a_)  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(a_)





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_):
							snake_case_           =							[]
							# for loop iterates over number of elements in list
							for i in range(len(a_)):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i])):
																					snake_case_           =							[]  # to store all discriminant values of each item as a list
																					# for loop iterates over number of classes we have in our dataset
																					for k in range(len(a_)):
																												# appending values of discriminants for each class to 'temp' list
																												temp.append(
																												    x_items[i][j] * (means[k] / variance)
																												    - (means[k] ** 2 / (2 * variance))
																												    + log(probabilities[k]))
																					# appending discriminant values of each item to 'results' list
																					results.append(a_)

							return [result.index(max(a_)) for result in results]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							sum(1 for i, j in zip(a_							,					a_) if i == j)
							# percentage of accuracy equals to number of correct predictions divided by number
							# of all data and multiplied by 100
							return (correct / len(a_)) * 1_00


lowercase  							= TypeVar("num")





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_ = lambda a_: True							,					a_ = None							,					):
							while True:
														try:
																					snake_case_           =							input_type(input(a_).strip() or default)
																					if condition(a_):
																												return user_input
																					else:
																												print(f'''{user_input}: {err_msg}''')
																												continue
														except ValueError:
																					print(
																					    f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}''')





def 						__UpperCAmelCase      (					):
							while True:
														print(' Linear Discriminant Analysis '.center(50							,					'*'))
														print('*' * 50							,					'\n')
														print('First of all we should specify the number of classes that')
														print('we want to generate as training dataset')
														# Trying to get number of classes
														snake_case_           =							valid_input(
														    input_type=a_							,					condition=lambda a_: x > 0							,					input_msg='Enter the number of classes (Data Groupings): '							,					err_msg='Number of classes should be positive!'							,					)

														print('-' * 1_00)

														# Trying to get the value of standard deviation
														snake_case_           =							valid_input(
														    input_type=a_							,					condition=lambda a_: x >= 0							,					input_msg=(
														        'Enter the value of standard deviation'
														        '(Default value is 1.0 for all classes): '
														    )							,					err_msg='Standard deviation should not be negative!'							,					default='1.0'							,					)

														print('-' * 1_00)

														# Trying to get number of instances in classes and theirs means to generate
														# dataset
														snake_case_           =							[]  # An empty list to store instance counts of classes in dataset
														for i in range(a_):
																					snake_case_           =							valid_input(
																					    input_type=a_							,					condition=lambda a_: x > 0							,					input_msg=(f'''Enter The number of instances for class_{i+1}: ''')							,					err_msg='Number of instances should be positive!'							,					)
																					counts.append(a_)
														print('-' * 1_00)

														# An empty list to store values of user-entered means of classes
														snake_case_           =							[]
														for a in range(a_):
																					snake_case_           =							valid_input(
																					    input_type=a_							,					input_msg=(f'''Enter the value of mean for class_{a+1}: ''')							,					err_msg='This is an invalid value.'							,					)
																					user_means.append(a_)
														print('-' * 1_00)

														print('Standard deviation: '							,					a_)
														# print out the number of instances in classes in separated line
														for i, count in enumerate(a_							,					1):
																					print(f'''Number of instances in class_{i} is: {count}''')
														print('-' * 1_00)

														# print out mean values of classes separated line
														for i, user_mean in enumerate(a_							,					1):
																					print(f'''Mean of class_{i} is: {user_mean}''')
														print('-' * 1_00)

														# Generating training dataset drawn from gaussian distribution
														snake_case_           =							[
														    gaussian_distribution(user_means[j]							,					a_							,					counts[j])
														    for j in range(a_)
														]
														print('Generated Normal Distribution: \n'							,					a_)
														print('-' * 1_00)

														# Generating Ys to detecting corresponding classes
														snake_case_           =							y_generator(a_							,					a_)
														print('Generated Corresponding Ys: \n'							,					a_)
														print('-' * 1_00)

														# Calculating the value of actual mean for each class
														snake_case_           =							[calculate_mean(counts[k]							,					x[k]) for k in range(a_)]
														# for loop iterates over number of elements in 'actual_means' list and print
														# out them in separated line
														for i, actual_mean in enumerate(a_							,					1):
																					print(f'''Actual(Real) mean of class_{i} is: {actual_mean}''')
														print('-' * 1_00)

														# Calculating the value of probabilities for each class
														snake_case_           =							[
														    calculate_probabilities(counts[i]							,					sum(a_)) for i in range(a_)
														]

														# for loop iterates over number of elements in 'probabilities' list and print
														# out them in separated line
														for i, probability in enumerate(a_							,					1):
																					print(f'''Probability of class_{i} is: {probability}''')
														print('-' * 1_00)

														# Calculating the values of variance for each class
														snake_case_           =							calculate_variance(a_							,					a_							,					sum(a_))
														print('Variance: '							,					a_)
														print('-' * 1_00)

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														snake_case_           =							predict_y_values(a_							,					a_							,					a_							,					a_)
														print('-' * 1_00)

														# Calculating Accuracy of the model
														print(f'''Accuracy: {accuracy(a_							,					a_)}''')
														print('-' * 1_00)
														print(' DONE '.center(1_00							,					'+'))

														if input('Press any key to restart or \'q\' for quit: ').strip().lower() == "q":
																					print('\n' + 'GoodBye!'.center(1_00							,					'-') + '\n')
																					break
														system('cls' if name == 'nt' else 'clear')  # noqa: S605


if __name__ == "__main__":
	main()





