import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : List[Any]      ,							__a    : int      ,							__a    : Optional[int]      ,							__a    : int = 4_096		)      ->       Optional[Any]:
	_lowercase   ,  _lowercase   ,  _lowercase   =key.shape[-3:]
	_lowercase   =value.shape[-1]
	_lowercase   =min(__a      ,							__a		)
	_lowercase   =query / jnp.sqrt(__a		)

	@functools.partial(jax.checkpoint      ,							prevent_cse=__a		)
	def summarize_chunk(__a    : Dict      ,							__a    : Optional[Any]      ,							__a    : Optional[int]		):
		_lowercase   =jnp.einsum("...qhd,...khd->...qhk"      ,							__a      ,							__a      ,							precision=__a		)

		_lowercase   =jnp.max(__a      ,							axis=-1      ,							keepdims=__a		)
		_lowercase   =jax.lax.stop_gradient(__a		)
		_lowercase   =jnp.exp(attn_weights - max_score		)

		_lowercase   =jnp.einsum("...vhf,...qhv->...qhf"      ,							__a      ,							__a      ,							precision=__a		)
		_lowercase   =jnp.einsum("...qhk->...qh"      ,							__a		)

		return (exp_values, exp_weights.sum(axis=-1		), max_score)

	def chunk_scanner(__a    : str		):
		# julienne key array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]      ,							slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features]      ,							)

		# julienne value array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]      ,							slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features]      ,							)

		return summarize_chunk(__a      ,							__a      ,							__a		)

	_lowercase   ,  _lowercase   ,  _lowercase   =jax.lax.map(f=__a      ,							xs=jnp.arange(0      ,							__a      ,							__a		)		)

	_lowercase   =jnp.max(__a      ,							axis=0      ,							keepdims=__a		)
	_lowercase   =jnp.exp(chunk_max - global_max		)

	chunk_values *= jnp.expand_dims(__a      ,							axis=-1		)
	chunk_weights *= max_diffs

	_lowercase   =chunk_values.sum(axis=0		)
	_lowercase   =jnp.expand_dims(__a      ,							-1		).sum(axis=0		)

	return all_values / all_weights

def 	__lowerCamelCase							(				__a    : Union[str, Any]      ,							__a    : Dict      ,							__a    : int      ,							__a    : Any=jax.lax.Precision.HIGHEST      ,							__a    : int = 1_024      ,							__a    : int = 4_096		)      ->       Any:
	_lowercase   ,  _lowercase   ,  _lowercase   =query.shape[-3:]

	def chunk_scanner(__a    : List[Any]      ,							__a    : Union[str, Any]		):
		# julienne query array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]      ,							slice_sizes=list(query.shape[:-3]		) + [min(__a      ,							__a		), num_heads, q_features]      ,							)

		return (
		    chunk_idx + query_chunk_size,  # unused ignore it
		    _query_chunk_attention(
		        query=__a      ,							key=__a      ,							value=__a      ,							precision=__a      ,							key_chunk_size=__a		),
		)

	_lowercase   ,  _lowercase   =jax.lax.scan(
	    f=__a      ,							init=0      ,							xs=__a      ,							length=math.ceil(num_q / query_chunk_size		)		)

	return jnp.concatenate(__a      ,							axis=-3		)  # fuse the chunked result back
class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					8
	__SCREAMING_SNAKE_CASE              =					64
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dim_head * self.heads
		_lowercase   =self.dim_head**-0.5

		# Weights were exported with old names {to_q, to_k, to_v, to_out}
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_q"       )
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_k"       )
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_v"       )

		_lowercase   =nn.Dense(self.query_dim       ,  dtype=self.dtype       ,  name="to_out_0"       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =tensor.shape
		_lowercase   =self.heads
		_lowercase   =tensor.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  dim // head_size       )
		_lowercase   =jnp.transpose(lowerCAmelCase_       ,  (0, 2, 1, 3)       )
		_lowercase   =tensor.reshape(batch_size * head_size       ,  lowerCAmelCase_       ,  dim // head_size       )
		return tensor


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =tensor.shape
		_lowercase   =self.heads
		_lowercase   =tensor.reshape(batch_size // head_size       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =jnp.transpose(lowerCAmelCase_       ,  (0, 2, 1, 3)       )
		_lowercase   =tensor.reshape(batch_size // head_size       ,  lowerCAmelCase_       ,  dim * head_size       )
		return tensor


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ):
		_lowercase   =hidden_states if context is None else context

		_lowercase   =self.query(lowerCAmelCase_       )
		_lowercase   =self.key(lowerCAmelCase_       )
		_lowercase   =self.value(lowerCAmelCase_       )

		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )
		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )
		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )

		if self.use_memory_efficient_attention:
			_lowercase   =query_states.transpose(1       ,  0       ,  2       )
			_lowercase   =key_states.transpose(1       ,  0       ,  2       )
			_lowercase   =value_states.transpose(1       ,  0       ,  2       )

			# this if statement create a chunk size for each layer of the unet
			# the chunk size is equal to the query_length dimension of the deepest layer of the unet

			_lowercase   =query_states.shape[-3]
			if flatten_latent_dim % 64 == 0:
				_lowercase   =int(flatten_latent_dim / 64       )
			elif flatten_latent_dim % 16 == 0:
				_lowercase   =int(flatten_latent_dim / 16       )
			elif flatten_latent_dim % 4 == 0:
				_lowercase   =int(flatten_latent_dim / 4       )
			else:
				_lowercase   =int(lowerCAmelCase_       )

			_lowercase   =jax_memory_efficient_attention(
			    lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  query_chunk_size=lowerCAmelCase_       ,  key_chunk_size=4096 * 4       )

			_lowercase   =hidden_states.transpose(1       ,  0       ,  2       )
		else:
			# compute attentions
			_lowercase   =jnp.einsum("b i d, b j d->b i j"       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =attention_scores * self.scale
			_lowercase   =nn.softmax(lowerCAmelCase_       ,  axis=2       )

			# attend to values
			_lowercase   =jnp.einsum("b i j, b j d -> b i d"       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =self.reshape_batch_dim_to_heads(lowerCAmelCase_       )
		_lowercase   =self.proj_attn(lowerCAmelCase_       )
		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		# self attention (or cross_attention if only_cross_attention is True)
		_lowercase   =FlaxAttention(
		    self.dim       ,  self.n_heads       ,  self.d_head       ,  self.dropout       ,  self.use_memory_efficient_attention       ,  dtype=self.dtype       )
		# cross attention
		_lowercase   =FlaxAttention(
		    self.dim       ,  self.n_heads       ,  self.d_head       ,  self.dropout       ,  self.use_memory_efficient_attention       ,  dtype=self.dtype       )
		_lowercase   =FlaxFeedForward(dim=self.dim       ,  dropout=self.dropout       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		# self attention
		_lowercase   =hidden_states
		if self.only_cross_attention:
			_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		else:
			_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		# cross attention
		_lowercase   =hidden_states
		_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		# feed forward
		_lowercase   =hidden_states
		_lowercase   =self.ff(self.norma(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					1
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =nn.GroupNorm(num_groups=32       ,  epsilon=1e-5       )

		_lowercase   =self.n_heads * self.d_head
		if self.use_linear_projection:
			_lowercase   =nn.Dense(lowerCAmelCase_       ,  dtype=self.dtype       )
		else:
			_lowercase   =nn.Conv(
			    lowerCAmelCase_       ,  kernel_size=(1, 1)       ,  strides=(1, 1)       ,  padding="VALID"       ,  dtype=self.dtype       ,  )

		_lowercase   =[
		    FlaxBasicTransformerBlock(
		        lowerCAmelCase_       ,  self.n_heads       ,  self.d_head       ,  dropout=self.dropout       ,  only_cross_attention=self.only_cross_attention       ,  dtype=self.dtype       ,  use_memory_efficient_attention=self.use_memory_efficient_attention       ,  )
		    for _ in range(self.depth       )
		]

		if self.use_linear_projection:
			_lowercase   =nn.Dense(lowerCAmelCase_       ,  dtype=self.dtype       )
		else:
			_lowercase   =nn.Conv(
			    lowerCAmelCase_       ,  kernel_size=(1, 1)       ,  strides=(1, 1)       ,  padding="VALID"       ,  dtype=self.dtype       ,  )

		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =hidden_states.shape
		_lowercase   =hidden_states
		_lowercase   =self.norm(lowerCAmelCase_       )
		if self.use_linear_projection:
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  height * width       ,  lowerCAmelCase_       )
			_lowercase   =self.proj_in(lowerCAmelCase_       )
		else:
			_lowercase   =self.proj_in(lowerCAmelCase_       )
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  height * width       ,  lowerCAmelCase_       )

		for transformer_block in self.transformer_blocks:
			_lowercase   =transformer_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

		if self.use_linear_projection:
			_lowercase   =self.proj_out(lowerCAmelCase_       )
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =self.proj_out(lowerCAmelCase_       )

		_lowercase   =hidden_states + residual
		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		# The second linear layer needs to be called
		# net_2 for now to match the index of the Sequential layer
		_lowercase   =FlaxGEGLU(self.dim       ,  self.dropout       ,  self.dtype       )
		_lowercase   =nn.Dense(self.dim       ,  dtype=self.dtype       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   =self.net_a(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =self.net_a(lowerCAmelCase_       )
		return hidden_states

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dim * 4
		_lowercase   =nn.Dense(inner_dim * 2       ,  dtype=self.dtype       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   =self.proj(lowerCAmelCase_       )
		_lowercase   ,  _lowercase   =jnp.split(lowerCAmelCase_       ,  2       ,  axis=2       )
		return self.dropout_layer(hidden_linear * nn.gelu(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )












'''simple docstring'''

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		:						float    ,      a		:						float    ,      a		:						int				)      ->      list:





						"""simple docstring"""
						seed(1				)
						return [gauss(a    ,      a				) for _ in range(a				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						return [k for k in range(a				) for _ in range(instance_count[k]				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						return sum(a				) / instance_count






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						return instance_count / total_count






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						lowercase_   : str   	=      []  # 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
						lowercase_   : Tuple   	=      len(a				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(a				)






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						float    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						lowercase_   : str   	=      []
						# 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]				)				):
																		lowercase_   : Any   	=      []  # 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		:						list    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						lowercase_   : Optional[Any]   	=      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_0_0


A:   Union[str, Any]						=					TypeVar("num")






def 						_UpperCAmelCase     (	a		:						Callable[[object], num]    ,      a		:						str    ,      a		:						str    ,      a		:						Callable[[num], bool] = lambda a				: True    ,      a		:						str | None = None    ,      )      ->      num:





						"""simple docstring"""
						while True:
												try:
																		lowercase_   : int   	=      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     (	)      ->      List[str]:





						"""simple docstring"""
						while True:
												print(' Linear Discriminant Analysis '.center(5_0    ,      '*'				)				)
												print('*' * 5_0    ,      '\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
												lowercase_   : Dict   	=      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_0_0				)

												# Trying to get the value of standard deviation
												lowercase_   : List[Any]   	=      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_0_0				)

												# Trying to get number of instances in classes and theirs means to generate
												# dataset
												lowercase_   : Any   	=      []  # An empty list to store instance counts of classes in dataset
												for i in range(a				):
																		lowercase_   : Union[str, Any]   	=      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_0_0				)

												# An empty list to store values of user-entered means of classes
												lowercase_   : int   	=      []
												for a in range(a				):
																		lowercase_   : Union[str, Any]   	=      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_0_0				)

												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_0_0				)

												# 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_0_0				)

												# Generating training dataset drawn from gaussian distribution
												lowercase_   : Optional[int]   	=      [
												    gaussian_distribution(user_means[j]    ,      a    ,      counts[j]				)
												    for j in range(a				)
												]
												print('Generated Normal Distribution: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Generating Ys to detecting corresponding classes
												lowercase_   : Optional[int]   	=      y_generator(a    ,      a				)
												print('Generated Corresponding Ys: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Calculating the value of actual mean for each class
												lowercase_   : str   	=      [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_0_0				)

												# Calculating the value of probabilities for each class
												lowercase_   : Union[str, Any]   	=      [
												    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_0_0				)

												# Calculating the values of variance for each class
												lowercase_   : List[str]   	=      calculate_variance(a    ,      a    ,      sum(a				)				)
												print('Variance: '    ,      a				)
												print('-' * 1_0_0				)

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												lowercase_   : Tuple   	=      predict_y_values(a    ,      a    ,      a    ,      a				)
												print('-' * 1_0_0				)

												# Calculating Accuracy of the model
												print(f"Accuracy: {accuracy(a    ,      a				)}"				)
												print('-' * 1_0_0				)
												print(' DONE '.center(1_0_0    ,      '+'				)				)

												if input('Press any key to restart or \'q\' for quit: '				).strip().lower() == "q":
																		print('\n' + 'GoodBye!'.center(1_0_0    ,      '-'				) + '\n'				)
																		break
												system('cls' if name == 'nt' else 'clear'				)  # noqa: S605


if __name__ == "__main__":
						main()
