





from __future__ import annotations

import numpy as np




def 						lowerCamelCase_	(		UpperCamelCase_       ):
      _a							:  int				= np.shape(__SCREAMING_SNAKE_CASE       )
      if rows != columns:
            _a							:  Union[str, Any]				= (
                '\'table\' has to be of square shaped array but got a '
                f"""{rows}x{columns} array:\n{table}"""
            )
            raise ValueError(__SCREAMING_SNAKE_CASE       )

      _a							:  Tuple				= np.zeros((rows, columns)       )
      _a							:  Any				= np.zeros((rows, columns)       )
      for i in range(__SCREAMING_SNAKE_CASE       ):
            for j in range(__SCREAMING_SNAKE_CASE       ):
                  _a							:  str				= sum(lower[i][k] * upper[k][j] for k in range(__SCREAMING_SNAKE_CASE       )       )
                  if upper[j][j] == 0:
                        raise ArithmeticError('''No LU decomposition exists'''       )
                  _a							:  Optional[Any]				= (table[i][j] - total) / upper[j][j]
            _a							:  Union[str, Any]				= 1
            for j in range(__SCREAMING_SNAKE_CASE			,					__SCREAMING_SNAKE_CASE       ):
                  _a							:  Optional[Any]				= sum(lower[i][k] * upper[k][j] for k in range(__SCREAMING_SNAKE_CASE       )       )
                  _a							:  Dict				= table[i][j] - total
      return lower, upper


if __name__ == "__main__":
    import doctest

    doctest.testmod()











'''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


A_			      =		logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
A_			      =		{
    "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",
    },
}

A_			      =		{
    "RUCAIBox/mvp": 1024,
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			VOCAB_FILES_NAMES
     A_											=			PRETRAINED_VOCAB_FILES_MAP
     A_											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_											=			["input_ids", "attention_mask"]
     A_											=			MvpTokenizer





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

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

           snake_case__       :   Tuple	      =    add_prefix_space

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

                 snake_case__       :   Dict	      =    False

                 if state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                       snake_case__       :   List[Any]	      =    add_prefix_space
                       snake_case__       :   List[str]	      =    True

                 if state.get('trim_offsets'     ,							__lowerCamelCase			) != trim_offsets:
                       snake_case__       :   str	      =    trim_offsets
                       snake_case__       :   List[Any]	      =    True

                 if changes_to_apply:
                       snake_case__       :   Optional[int]	      =    getattr(__lowerCamelCase     ,							state.pop('type'			)			)
                       snake_case__       :   str	      =    component_class(**__lowerCamelCase			)
                       setattr(self.backend_tokenizer     ,							__lowerCamelCase     ,							__lowerCamelCase			)





     @property
     def 			_lowerCAmelCase      (     self     :					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 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   str	      =    AddedToken(__lowerCamelCase     ,							lstrip=__lowerCamelCase     ,							rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     ,							__lowerCamelCase			) else value
           snake_case__       :   Optional[int]	      =    value





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					List[Any]			):
           snake_case__       :   Optional[int]	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           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(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					Dict			):
           snake_case__       :   Tuple	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           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(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str] = None			):
           snake_case__       :   List[str]	      =    self._tokenizer.model.save(__lowerCamelCase     ,							name=__lowerCamelCase			)
           return tuple(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]=None			):
           snake_case__       :   Optional[Any]	      =    [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 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[int]     ,							__lowerCamelCase     :					Optional[List[int]] = None			):
           snake_case__       :   Dict	      =    [self.sep_token_id]
           snake_case__       :   List[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]
