"""simple docstring"""







import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       _lowercase		( __lowerCamelCase   ,   unittest.TestCase	):
				lowerCamelCase__							=							BertweetTokenizer
				lowerCamelCase__							=							False

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										UpperCAmelCase__       						=		["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
										UpperCAmelCase__       						=		dict(zip(_lowercase							,     range(len(_lowercase  )  )  )  )
										UpperCAmelCase__       						=		["""#version: 0.2""", """a m</w>"""]
										UpperCAmelCase__       						=		{"""unk_token""": """<unk>"""}

										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:
																for token in vocab_tokens:
																						fp.write(f'{token} {vocab_tokens[token]}\n'  )
										with open(self.merges_file							,     """w"""							,     encoding="""utf-8"""  ) as fp:
																fp.write("""\n""".join(_lowercase  )  )

				def 							A			(  self							:     Optional[int]							,     **_lowercase							:     Optional[int]  ):


										'''simple docstring'''
										kwargs.update(self.special_tokens_map  )
										return BertweetTokenizer.from_pretrained(self.tmpdirname							,     **_lowercase  )

				def 							A			(  self							:     Dict							,     _lowercase							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		"""I am VinAI Research"""
										UpperCAmelCase__       						=		"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
										return input_text, output_text

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		BertweetTokenizer(self.vocab_file							,     self.merges_file							,     **self.special_tokens_map  )
										UpperCAmelCase__       						=		"""I am VinAI Research"""
										UpperCAmelCase__       						=		"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
										UpperCAmelCase__       						=		tokenizer.tokenize(_lowercase  )
										self.assertListEqual(_lowercase							,     _lowercase  )

										UpperCAmelCase__       						=		tokens + [tokenizer.unk_token]

										UpperCAmelCase__       						=		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase  )							,     _lowercase  )


"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						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__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						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

