




def    snake_case   (				A		,  A     )				->				list:




	'''simple docstring'''


	lowerCamelCase_				:						Any				  =				sorted(A		,  key=lambda A     : value[2]		,  reverse=A     )

	# Create a list of size equal to the maximum deadline
	# and initialize it with -1
	lowerCamelCase_				:						int				  =				max(A		,  key=lambda A     : value[1]     )[1]
	lowerCamelCase_				:						int				  =				[-1] * max_deadline

	# Finding the maximum profit and the count of jobs
	lowerCamelCase_				:						List[Any]				  =				0
	lowerCamelCase_				:						List[Any]				  =				0
	for job in jobs:
		# Find a free time slot for this job
		# (Note that we start from the last possible slot)
		for i in range(job[1] - 1		,  -1		,  -1     ):
			if time_slots[i] == -1:
				lowerCamelCase_				:						Tuple				  =				job[0]
				count += 1
				max_profit += job[2]
				break
	return [count, max_profit]


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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,)
