



'''simple docstring'''

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_mvp import MvpTokenizer


__lowerCAmelCase						      =       logging.get_logger(__name__)


__lowerCAmelCase						      =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

__lowerCAmelCase						      =       {
    '''RUCAIBox/mvp''': 1_024,
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Union[str, Any]           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   int           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   List[str]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   str           = ['input_ids', 'attention_mask']
							lowerCAmelCase	:   Dict           = MvpTokenizer







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

									_a					:				Union[str, Any]				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
									if pre_tok_state.get('add_prefix_space'     ,_UpperCAmelCase		) != add_prefix_space:
											_a					:				Union[str, Any]				=							getattr(_UpperCAmelCase     ,pre_tok_state.pop('type'		)		)
											_a					:				Dict				=							add_prefix_space
											_a					:				Optional[Any]				=							pre_tok_class(**_UpperCAmelCase		)

									_a					:				Optional[Any]				=							add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									_a					:				Tuple				=							'post_processor'
									_a					:				Optional[int]				=							getattr(self.backend_tokenizer     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									if tokenizer_component_instance:
											_a					:				Optional[int]				=							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:
													_a					:				List[Any]				=							tuple(state['sep']		)
											if "cls" in state:
													_a					:				Tuple				=							tuple(state['cls']		)

											_a					:				int				=							False

											if state.get('add_prefix_space'     ,_UpperCAmelCase		) != add_prefix_space:
													_a					:				Any				=							add_prefix_space
													_a					:				Dict				=							True

											if state.get('trim_offsets'     ,_UpperCAmelCase		) != trim_offsets:
													_a					:				Optional[int]				=							trim_offsets
													_a					:				Optional[int]				=							True

											if changes_to_apply:
													_a					:				List[Any]				=							getattr(_UpperCAmelCase     ,state.pop('type'		)		)
													_a					:				List[str]				=							component_class(**_UpperCAmelCase		)
													setattr(self.backend_tokenizer     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							@property
							def 							__lowercase      (							self :	List[Any]		):
									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 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Tuple		):
									_a					:				Tuple				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else value
									_a					:				Tuple				=							value







							def 							__lowercase      (							self :	List[Any]     ,*_UpperCAmelCase :	List[str]     ,**_UpperCAmelCase :	str		):
									_a					:				int				=							kwargs.get('is_split_into_words'     ,_UpperCAmelCase		)

									if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
											    'to use it with pretokenized inputs.'		)

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







							def 							__lowercase      (							self :	Dict     ,*_UpperCAmelCase :	Any     ,**_UpperCAmelCase :	Optional[Any]		):
									_a					:				Optional[int]				=							kwargs.get('is_split_into_words'     ,_UpperCAmelCase		)

									if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
											    'to use it with pretokenized inputs.'		)

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







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[str] = None		):
									_a					:				Any				=							self._tokenizer.model.save(_UpperCAmelCase     ,name=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Tuple=None		):
									_a					:				str				=							[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 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
									_a					:				Tuple				=							[self.sep_token_id]
									_a					:				Optional[int]				=							[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]









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




