




import numpy as np






def    snake_case   (				A		,  A		,  A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						List[Any]				  =				n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	lowerCamelCase_				:						Optional[int]				  =				np.floor(A     )
	# we add samples according to how much "left over" probability
	# they had, until we arrive at n_samples
	lowerCamelCase_				:						List[str]				  =				int(n_draws - floored.sum()     )
	if need_to_add > 0:
		lowerCamelCase_				:						Optional[int]				  =				continuous - floored
		lowerCamelCase_				:						str				  =				np.sort(np.unique(A     )     )[::-1]
		# add according to remainder, but break ties
		# randomly to avoid biases
		for value in values:
			((lowerCamelCase_)  ,     )				:						Optional[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
			lowerCamelCase_				:						Any				  =				min(len(A     )		,  A     )
			lowerCamelCase_				:						List[str]				  =				rng.choice(A		,  size=A		,  replace=A     )
			floored[inds] += 1
			need_to_add -= add_now
			if need_to_add == 0:
				break
	return floored.astype(np.intaa     )






def    snake_case   (				A		,  A		,  A		,  A		,  A=10     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_  ,     lowerCamelCase_				:						Any				  =				np.unique(A		,  return_inverse=A     )
	lowerCamelCase_				:						List[str]				  =				classes.shape[0]
	lowerCamelCase_				:						Union[str, Any]				  =				np.bincount(A     )
	if np.min(A     ) < 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)     )
	lowerCamelCase_				:						Tuple				  =				np.split(np.argsort(A		,  kind='mergesort'     )		,  np.cumsum(A     )[:-1]     )
	for _ in range(A     ):
		lowerCamelCase_				:						List[Any]				  =				approximate_mode(A		,  A		,  A     )
		lowerCamelCase_				:						List[str]				  =				class_counts - n_i
		lowerCamelCase_				:						Optional[Any]				  =				approximate_mode(A		,  A		,  A     )

		lowerCamelCase_				:						List[Any]				  =				[]
		lowerCamelCase_				:						Optional[Any]				  =				[]

		for i in range(A     ):
			lowerCamelCase_				:						List[Any]				  =				rng.permutation(class_counts[i]     )
			lowerCamelCase_				:						Any				  =				class_indices[i].take(A		,  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]]     )
		lowerCamelCase_				:						List[Any]				  =				rng.permutation(A     )
		lowerCamelCase_				:						Any				  =				rng.permutation(A     )

		yield train, test






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							UpperCamelCase__						=		None


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__						=		{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__						=		{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					VOCAB_FILES_NAMES
					_lowerCamelCase						    =					PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase						    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase						    =					["""input_ids""", """attention_mask"""]
					_lowerCamelCase						    =					XLMRobertaTokenizer

					def __init__(							self					,			__lowercase=None					,			__lowercase=None					,			__lowercase="<s>"					,			__lowercase="</s>"					,			__lowercase="</s>"					,			__lowercase="<s>"					,			__lowercase="<unk>"					,			__lowercase="<pad>"					,			__lowercase="<mask>"					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				AddedToken(__lowercase					,			lstrip=__lowercase					,			rstrip=__lowercase   ) if isinstance(__lowercase					,			__lowercase   ) else mask_token

						super().__init__(
						    __lowercase					,			tokenizer_file=__lowercase					,			bos_token=__lowercase					,			eos_token=__lowercase					,			sep_token=__lowercase					,			cls_token=__lowercase					,			unk_token=__lowercase					,			pad_token=__lowercase					,			mask_token=__lowercase					,			**__lowercase					,			)

						lowerCamelCase_				:						str				  =				vocab_file
						lowerCamelCase_				:						str				  =				False if not self.vocab_file else True

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_				:						Any				  =				[self.cls_token_id]
						lowerCamelCase_				:						List[Any]				  =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				[self.sep_token_id]
						lowerCamelCase_				:						Dict				  =				[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep   ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'   )

						if not os.path.isdir(__lowercase   ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory."   )
							return
						lowerCamelCase_				:						List[Any]				  =				os.path.join(
						    __lowercase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

						if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowercase   ):
							copyfile(self.vocab_file					,			__lowercase   )

						return (out_vocab_file,)
