





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__lowercase       :str       =  logging.get_logger(__name__)

__lowercase       :int       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowercase       :str       =  {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__lowercase       :Optional[Any]       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						VOCAB_FILES_NAMES
							snake_case_							 =						PRETRAINED_VOCAB_FILES_MAP
							snake_case_							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_							 =						["input_ids", "attention_mask"]
							snake_case_							 =						RobertaTokenizer

							def __init__(      self				:     Tuple							,	a				:     Any=None							,	a				:     str=None							,	a				:     Dict=None							,	a				:     int="replace"							,	a				:     Optional[Any]="<s>"							,	a				:     Any="</s>"							,	a				:     Union[str, Any]="</s>"							,	a				:     Dict="<s>"							,	a				:     Any="<unk>"							,	a				:     str="<pad>"							,	a				:     int="<mask>"							,	a				:     List[str]=False							,	a				:     Tuple=True							,	**a				:     int							,	)	->int:
													super().__init__(
													    a							,	a							,	tokenizer_file=a							,	errors=a							,	bos_token=a							,	eos_token=a							,	sep_token=a							,	cls_token=a							,	unk_token=a							,	pad_token=a							,	mask_token=a							,	add_prefix_space=a							,	trim_offsets=a							,	**a							,	)

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
													if pre_tok_state.get("add_prefix_space"							,	a    ) != add_prefix_space:
																			SCREAMING_SNAKE_CASE__     :				int   =					getattr(a							,	pre_tok_state.pop("type"    )    )
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					add_prefix_space
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					pre_tok_class(**a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					add_prefix_space

													SCREAMING_SNAKE_CASE__     :				int   =					"post_processor"
													SCREAMING_SNAKE_CASE__     :				Any   =					getattr(self.backend_tokenizer							,	a							,	a    )
													if tokenizer_component_instance:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					json.loads(tokenizer_component_instance.__getstate__()    )

																			# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																			if "sep" in state:
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tuple(state["sep"]    )
																			if "cls" in state:
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					tuple(state["cls"]    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					False

																			if state.get("add_prefix_space"							,	a    ) != add_prefix_space:
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					add_prefix_space
																									SCREAMING_SNAKE_CASE__     :				Tuple   =					True

																			if state.get("trim_offsets"							,	a    ) != trim_offsets:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					trim_offsets
																									SCREAMING_SNAKE_CASE__     :				str   =					True

																			if changes_to_apply:
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					getattr(a							,	state.pop("type"    )    )
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					component_class(**a    )
																									setattr(self.backend_tokenizer							,	a							,	a    )

							@property
							def      A_  (      self				:     List[str]    )	->str:
													if self._mask_token is None:
																			if self.verbose:
																									logger.error("Using mask_token, but it is not set yet."    )
																			return None
													return str(self._mask_token    )

							@mask_token.setter
							def      A_  (      self				:     Tuple							,	a				:     Optional[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					AddedToken(a							,	lstrip=a							,	rstrip=a    ) if isinstance(a							,	a    ) else value
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					value

							def      A_  (      self				:     Union[str, Any]							,	*a				:     Union[str, Any]							,	**a				:     List[Any]    )	->BatchEncoding:
													SCREAMING_SNAKE_CASE__     :				int   =					kwargs.get("is_split_into_words"							,	a    )
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*a							,	**a    )

							def      A_  (      self				:     List[str]							,	*a				:     Dict							,	**a				:     List[str]    )	->BatchEncoding:
													SCREAMING_SNAKE_CASE__     :				int   =					kwargs.get("is_split_into_words"							,	a    )

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*a							,	**a    )

							def      A_  (      self				:     Dict							,	a				:     str							,	a				:     Optional[str] = None    )	->Tuple[str]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self._tokenizer.model.save(a							,	name=a    )
													return tuple(a    )

							def      A_  (      self				:     Any							,	a				:     Union[str, Any]							,	a				:     str=None    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				int   =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																			return output

													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






							def      A_  (      self				:     Union[str, Any]							,	a				:     List[int]							,	a				:     Optional[List[int]] = None    )	->List[int]:
													SCREAMING_SNAKE_CASE__     :				str   =					[self.sep_token_id]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[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]






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) < k or k < 0:
												raise ValueError("Invalid Input"     )
						SCREAMING_SNAKE_CASE__     :				int   =					sum(array[:k]     )
						for i in range(len(_lowerCamelCase     ) - k     ):
												SCREAMING_SNAKE_CASE__     :				str   =					current_sum - array[i] + array[i + k]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max(_lowerCamelCase			,   _lowerCamelCase     )
						return max_sum


if __name__ == "__main__":
				from doctest import testmod
				from random import randint

				testmod()
				__lowercase       :List[str]       =  [randint(-1_000, 1_000) for i in range(100)]
				__lowercase       :Any       =  randint(0, 110)
				print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")