
def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list     )       ->	list:







	'''simple docstring'''


	__magic_name__	:	List[str]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : value[2]   ,     reverse=_snake_case     )

	# Create a list of size equal to the maximum deadline
	# and initialize it with -1
	__magic_name__	:	Dict			     =  max(_snake_case   ,     key=lambda _snake_case     : value[1]     )[1]
	__magic_name__	:	List[Any]			     =  [-1] * max_deadline

	# Finding the maximum profit and the count of jobs
	__magic_name__	:	Optional[int]			     =  0
	__magic_name__	:	Optional[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:
				__magic_name__	:	Any			     =  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:
				snake_case				:	Any							   =						None


snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)

snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

snake_case				:	Optional[Any]							   =						{
    "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"
        ),
    },
}

snake_case				:	Optional[int]							   =						{
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					VOCAB_FILES_NAMES
			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__					   =					['input_ids', 'attention_mask']
			UpperCamelCase__					   =					XLMRobertaTokenizer







			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
				# Mask token behave like a normal word, i.e. include the space before it
				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token

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

				__magic_name__	:	Optional[int]			     =  vocab_file
				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
				__magic_name__	:	str			     =  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				__magic_name__	:	List[str]			     =  [self.sep_token_id]
				__magic_name__	:	List[str]			     =  [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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				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(_a	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
					return
				__magic_name__	:	Any			     =  os.path.join(
				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

				return (out_vocab_file,)
