"""simple docstring"""
import numpy as np





def  lowerCAmelCase__     (      _UpperCamelCase							:					Tuple			,      _UpperCamelCase							:					int			,      _UpperCamelCase							:					Any       )       ->					str:





						"""simple docstring"""






						snake_case             =      n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						snake_case             =      np.floor(_UpperCamelCase       )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						snake_case             =      int(n_draws - floored.sum()       )
						if need_to_add > 0:
												snake_case             =      continuous - floored
												snake_case             =      np.sort(np.unique(_UpperCamelCase       )       )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((snake_case)       ,)             =      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
																		snake_case             =      min(len(_UpperCamelCase       )			,      _UpperCamelCase       )
																		snake_case             =      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  lowerCAmelCase__     (      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					Dict			,      _UpperCamelCase							:					Tuple=1_0       )       ->					Dict:





						"""simple docstring"""






						snake_case       ,snake_case             =      np.unique(_UpperCamelCase			,      return_inverse=_UpperCamelCase       )
						snake_case             =      classes.shape[0]
						snake_case             =      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)       )
						snake_case             =      np.split(np.argsort(_UpperCamelCase			,      kind='mergesort'       )			,      np.cumsum(_UpperCamelCase       )[:-1]       )
						for _ in range(_UpperCamelCase       ):
												snake_case             =      approximate_mode(_UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase       )
												snake_case             =      class_counts - n_i
												snake_case             =      approximate_mode(_UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase       )

												snake_case             =      []
												snake_case             =      []

												for i in range(_UpperCamelCase       ):
																		snake_case             =      rng.permutation(class_counts[i]       )
																		snake_case             =      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]]       )
												snake_case             =      rng.permutation(_UpperCamelCase       )
												snake_case             =      rng.permutation(_UpperCamelCase       )

												yield train, test



"""simple docstring"""
import os
from string import ascii_letters

SCREAMING_SNAKE_CASE__       =	ascii_letters + " \t\n"





def  lowerCAmelCase__     (      )       ->					dict[str, None]:





						"""simple docstring"""






						snake_case             =      os.path.split(os.path.realpath(_UpperCamelCase       )       )
						snake_case             =      {}
						with open(path[0] + '/dictionary.txt'       ) as dictionary_file:
												for word in dictionary_file.read().split('\n'       ):
																		snake_case             =      None
						return english_words


SCREAMING_SNAKE_CASE__       =	load_dictionary()





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					float:





						"""simple docstring"""






						snake_case             =      message.upper()
						snake_case             =      remove_non_letters(_UpperCamelCase       )
						snake_case             =      message.split()
						snake_case             =      len([word for word in possible_words if word in ENGLISH_WORDS]       )
						return float(_UpperCamelCase       ) / len(_UpperCamelCase       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					str:





						"""simple docstring"""






						return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int = 2_0			,      _UpperCamelCase							:					int = 8_5       )       ->					bool:





						"""simple docstring"""






						snake_case             =      get_english_count(_UpperCamelCase       ) * 1_0_0 >= word_percentage
						snake_case             =      len(remove_non_letters(_UpperCamelCase       )       )
						snake_case             =      (float(_UpperCamelCase       ) / len(_UpperCamelCase       )) * 1_0_0
						snake_case             =      message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()


