


"""simple docstring"""







import numpy as np






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__												=     np.floor(_UpperCamelCase			)
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__												=     int(n_draws - floored.sum()			)
						if need_to_add > 0:
												UpperCAmelCase__												=     continuous - floored
												UpperCAmelCase__												=     np.sort(np.unique(_UpperCamelCase			)			)[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,					)												=     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__												=     min(len(_UpperCamelCase			)						,      _UpperCamelCase			)
																		UpperCAmelCase__												=     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=10			):





						"""simple docstring"""

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

												UpperCAmelCase__												=     []
												UpperCAmelCase__												=     []

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

												yield train, test



"""simple docstring"""







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   SpeechaTextaTokenizer
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Dict					):
													super().setUp()

													UpperCAmelCase__												=     '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''					)
													UpperCAmelCase__												=     ['''he re</w> 123''', '''here a 1456''']
													UpperCAmelCase__												=     dict(zip(lowerCamelCase_		,			range(len(lowerCamelCase_					)					)					)					)

													UpperCAmelCase__												=     {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

													UpperCAmelCase__												=     tempfile.mkdtemp()
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''vocab_file''']					)
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''merges_file''']					)

													with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''					) as fp:
																			fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)

													with open(self.merges_file		,			'''w'''					) as fp:
																			fp.write('''\n'''.join(lowerCamelCase_					)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]		,			'''<s>'''					)
													self.assertEqual(vocab_keys[1]		,			'''<pad>'''					)
													self.assertEqual(vocab_keys[-1]		,			'''vocab'''					)
													self.assertEqual(len(lowerCamelCase_					)		,			14					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													self.assertEqual(self.get_tokenizer().vocab_size		,			14					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													# make sure @@ is correctly concatenated
													UpperCAmelCase__												=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
													UpperCAmelCase__												=     tokenizer.decode(lowerCamelCase_					)

													self.assertTrue(output_string == '''herecouple words ofthe'''					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			os.remove(os.path.join(lowerCamelCase_		,			'''merges.txt'''					)					)

																			# load tokenizer without merges file should not throw an error
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			# save tokenizer and load again
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													self.assertIsNotNone(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													if not self.test_slow_tokenizer:
																			return

													UpperCAmelCase__												=     inspect.signature(self.tokenizer_class.__init__					)
													UpperCAmelCase__												=     self.get_tokenizer()

													for parameter_name, parameter in signature.parameters.items():
																			if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																									self.assertIn(lowerCamelCase_		,			tokenizer.init_kwargs					)