






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'vocab.txt'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

__UpperCAmelCase     							=						{
    'openbmb/cpm-ant-10b': 1024,
}




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						collections.OrderedDict()
					with open(__snake_case					,	'r'					,	encoding='utf-8'  ) as reader:
										UpperCAmelCase_   :				List[Any]						=						reader.readlines()
					for index, token in enumerate(__snake_case  ):
										UpperCAmelCase_   :				List[Any]						=						token.rstrip('\n'  )
										UpperCAmelCase_   :				Union[str, Any]						=						index
					return vocab



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase=2_0_0       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						vocab
							UpperCAmelCase_   :				int						=						unk_token
							UpperCAmelCase_   :				Optional[int]						=						max_input_chars_per_word



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						list(_UpperCamelCase       )
							if len(_UpperCamelCase       ) > self.max_input_chars_per_word:
												return [self.unk_token]

							UpperCAmelCase_   :				Dict						=						0
							UpperCAmelCase_   :				Dict						=						[]
							while start < len(_UpperCamelCase       ):
												UpperCAmelCase_   :				List[str]						=						len(_UpperCamelCase       )
												UpperCAmelCase_   :				int						=						None
												while start < end:
																	UpperCAmelCase_   :				Union[str, Any]						=						''.join(chars[start:end]       )
																	if substr in self.vocab:
																						UpperCAmelCase_   :				Any						=						substr
																						break
																	end -= 1
												if cur_substr is None:
																	sub_tokens.append(self.unk_token       )
																	start += 1
												else:
																	sub_tokens.append(_UpperCamelCase       )
																	UpperCAmelCase_   :				List[Any]						=						end

							return sub_tokens





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  VOCAB_FILES_NAMES
		_snake_case   :   List[str]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   List[str]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Optional[Any]							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Any							=  False



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase="<d>"    ,		_UpperCamelCase="</d>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="</n>"    ,		_UpperCamelCase="</_>"    ,		_UpperCamelCase="left"    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							requires_backends(self    ,		['jieba']       )
							super().__init__(
							    bod_token=_UpperCamelCase    ,		eod_token=_UpperCamelCase    ,		bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		line_token=_UpperCamelCase    ,		space_token=_UpperCamelCase    ,		padding_side=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						bod_token
							UpperCAmelCase_   :				Optional[Any]						=						eod_token
							UpperCAmelCase_   :				List[str]						=						load_vocab(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						self.encoder[space_token]
							UpperCAmelCase_   :				Union[str, Any]						=						self.encoder[line_token]

							del self.encoder[space_token]
							del self.encoder[line_token]

							UpperCAmelCase_   :				Dict						=						collections.OrderedDict(sorted(self.encoder.items()    ,		key=lambda _UpperCamelCase       : x[1]       )       )
							UpperCAmelCase_   :				List[str]						=						{v: k for k, v in self.encoder.items()}

							UpperCAmelCase_   :				int						=						WordpieceTokenizer(vocab=self.encoder    ,		unk_token=self.unk_token       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.encoder[self.bod_token]



		@property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return self.encoder[self.eod_token]



		@property
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							return self.encoder["\n"]



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return len(self.encoder       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							return dict(self.encoder    ,		**self.added_tokens_encoder       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							int:
							UpperCAmelCase_   :				Optional[Any]						=						[]
							for x in jieba.cut(_UpperCamelCase    ,		cut_all=_UpperCamelCase       ):
												output_tokens.extend(self.wordpiece_tokenizer.tokenize(_UpperCamelCase       )       )
							return output_tokens



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Dict						=						[i for i in token_ids if i >= 0]
							UpperCAmelCase_   :				str						=						[
							    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
							]
							return super()._decode(_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Optional[int]:
							return token in self.encoder



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							return "".join(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							return self.encoder.get(_UpperCamelCase    ,		self.encoder.get(self.unk_token       )       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							return self.decoder.get(_UpperCamelCase    ,		self.unk_token       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							if os.path.isdir(_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						os.path.join(
												    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
							else:
												UpperCAmelCase_   :				Dict						=						(filename_prefix + '-' if filename_prefix else '') + save_directory
							UpperCAmelCase_   :				int						=						0
							if " " in self.encoder:
												UpperCAmelCase_   :				Union[str, Any]						=						self.encoder[' ']
												del self.encoder[" "]
							if "\n" in self.encoder:
												UpperCAmelCase_   :				Optional[int]						=						self.encoder['\n']
												del self.encoder["\n"]
							UpperCAmelCase_   :				Any						=						collections.OrderedDict(sorted(self.encoder.items()    ,		key=lambda _UpperCamelCase       : x[1]       )       )
							with open(_UpperCamelCase    ,		'w'    ,		encoding='utf-8'       ) as writer:
												for token, token_index in self.encoder.items():
																	if index != token_index:
																						logger.warning(
																						    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																						    ' Please check that the vocabulary is not corrupted!'       )
																						UpperCAmelCase_   :				Optional[int]						=						token_index
																	writer.write(token + '\n'       )
																	index += 1
							return (vocab_file,)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							if token_ids_a is None:
												return [self.bos_token_id] + token_ids_a
							return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False       )     ->							List[int]:

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_UpperCamelCase    ,		token_ids_a=_UpperCamelCase    ,		already_has_special_tokens=_UpperCamelCase       )

							if token_ids_a is not None:
												return [1] + ([0] * len(_UpperCamelCase       )) + [1] + ([0] * len(_UpperCamelCase       ))
							return [1] + ([0] * len(_UpperCamelCase       ))














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) < k or k < 0:
										raise ValueError('Invalid Input'  )
					UpperCAmelCase_   :				int						=						sum(array[:k]  )
					for i in range(len(__snake_case  ) - k  ):
										UpperCAmelCase_   :				List[Any]						=						current_sum - array[i] + array[i + k]
										UpperCAmelCase_   :				List[Any]						=						max(__snake_case					,	__snake_case  )
					return max_sum


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

		testmod()
		__UpperCAmelCase     							=						[randint(-1000, 1000) for i in range(100)]
		__UpperCAmelCase     							=						randint(0, 110)
		print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')






