






import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# 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(__lowerCAmelCase      )      )[::-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(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 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(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						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(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test








import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class __lowerCamelCase					( snake_case__	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= GPTNeoXJapaneseTokenizer
	UpperCamelCase__								= False
	UpperCamelCase__								= {"do_clean_text": False, "add_prefix_space": False}

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase							   =     [
							    'こん',
							    'こんに',
							    'にちは',
							    'ばんは',
							    '世界,㔺界',
							    '、',
							    '。',
							    '<BR>',
							    '<SP>',
							    '<TAB>',
							    '<URL>',
							    '<EMAIL>',
							    '<TEL>',
							    '<DATE>',
							    '<PRICE>',
							    '<BLOCK>',
							    '<KIGOU>',
							    '<U2000U2BFF>',
							    '<|emoji1|>',
							    '<unk>',
							    '<|startoftext|>',
							    '<|endoftext|>',
							]
							_UpperCAmelCase							   =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							_UpperCAmelCase							   =     {'unk_token': '<unk>'}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['vocab_file']       )
							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['emoji_file']       )
							with open(self.vocab_file			,       'w'			,       encoding='utf-8'       ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
							with open(self.emoji_file			,       'w'       ) as emoji_writer:
													emoji_writer.write(json.dumps(UpperCAmelCase       )       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							kwargs.update(self.special_tokens_map       )
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_input_output_texts(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode(UpperCAmelCase			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.decode(UpperCAmelCase			,       clean_up_tokenization_spaces=UpperCAmelCase       )
							return text, ids

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()

							# Testing tokenization
							_UpperCAmelCase							   =     'こんにちは、世界。　こんばんは、㔺界。'
							_UpperCAmelCase							   =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							_UpperCAmelCase							   =     tokenizer.tokenize(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids without special tokens
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids with special tokens
							_UpperCAmelCase							   =     tokens + [tokenizer.unk_token]
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

							_UpperCAmelCase							   =     tokenizer.encode('ありがとう。'			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode('どういたしまして。'			,       add_special_tokens=UpperCAmelCase       )

							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,       UpperCAmelCase       )

							assert encoded_sentence == ids_a
							assert encoded_pair == ids_a + ids_a

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass
