



'''simple docstring'''
import numpy as np






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCamelCase      :	Optional[int]				       =     np.floor(UpperCAmelCase_							)
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCamelCase      :	Optional[int]				       =     int(n_draws - floored.sum()							)
						if need_to_add > 0:
												_UpperCamelCase      :	Dict				       =     continuous - floored
												_UpperCamelCase      :	Tuple				       =     np.sort(np.unique(UpperCAmelCase_							)							)[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		(_UpperCamelCase    )      :	Union[str, Any]				       =     np.where(remainder == value							)
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCamelCase      :	List[Any]				       =     min(len(UpperCAmelCase_							)						,						UpperCAmelCase_							)
																		_UpperCamelCase      :	Tuple				       =     rng.choice(UpperCAmelCase_						,						size=UpperCAmelCase_						,						replace=UpperCAmelCase_							)
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa							)






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=1_0							):
						_UpperCamelCase      :	List[Any]				       =     np.unique(UpperCAmelCase_						,						return_inverse=UpperCAmelCase_							)
						_UpperCamelCase      :	List[str]				       =     classes.shape[0]
						_UpperCamelCase      :	Union[str, Any]				       =     np.bincount(UpperCAmelCase_							)
						if np.min(UpperCAmelCase_							) < 2:
												raise ValueError('Minimum class count error'							)
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)							)
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)							)
						_UpperCamelCase      :	Optional[int]				       =     np.split(np.argsort(UpperCAmelCase_						,						kind='mergesort'							)						,						np.cumsum(UpperCAmelCase_							)[:-1]							)
						for _ in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Optional[Any]				       =     approximate_mode(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)
												_UpperCamelCase      :	Dict				       =     class_counts - n_i
												_UpperCamelCase      :	Tuple				       =     approximate_mode(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)

												_UpperCamelCase      :	Tuple				       =     []
												_UpperCamelCase      :	str				       =     []

												for i in range(UpperCAmelCase_							):
																		_UpperCamelCase      :	List[Any]				       =     rng.permutation(class_counts[i]							)
																		_UpperCamelCase      :	Optional[int]				       =     class_indices[i].take(UpperCAmelCase_						,						mode='clip'							)
																		train.extend(perm_indices_class_i[: n_i[i]]							)
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
												_UpperCamelCase      :	str				       =     rng.permutation(UpperCAmelCase_							)
												_UpperCamelCase      :	Union[str, Any]				       =     rng.permutation(UpperCAmelCase_							)

												yield train, test







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.


