



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


__snake_case		:List[Any]   	=  logging.get_logger(__name__)


__snake_case		:Optional[int]   	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case		:Any   	=  {
    '''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''',
    },
}

__snake_case		:Any   	=  {
    '''RUCAIBox/mvp''': 1024,
}

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Tuple		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Union[str, Any]		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					Any		      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					Dict		      =		['''input_ids''', '''attention_mask''']
       UpperCamelCase__						:					Optional[int]		      =		MvpTokenizer



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	__SCREAMING_SNAKE_CASE							:  int="replace"  ,	__SCREAMING_SNAKE_CASE							:  List[Any]="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  int="<pad>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<mask>"  ,	__SCREAMING_SNAKE_CASE							:  int=False  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	tokenizer_file=__SCREAMING_SNAKE_CASE  ,	errors=__SCREAMING_SNAKE_CASE  ,	bos_token=__SCREAMING_SNAKE_CASE  ,	eos_token=__SCREAMING_SNAKE_CASE  ,	sep_token=__SCREAMING_SNAKE_CASE  ,	cls_token=__SCREAMING_SNAKE_CASE  ,	unk_token=__SCREAMING_SNAKE_CASE  ,	pad_token=__SCREAMING_SNAKE_CASE  ,	mask_token=__SCREAMING_SNAKE_CASE  ,	add_prefix_space=__SCREAMING_SNAKE_CASE  ,	trim_offsets=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)

           __a		 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''  ,	__SCREAMING_SNAKE_CASE) != add_prefix_space:
               __a		 =       getattr(__SCREAMING_SNAKE_CASE  ,	pre_tok_state.pop('''type'''))
               __a		 =       add_prefix_space
               __a		 =       pre_tok_class(**__SCREAMING_SNAKE_CASE)

           __a		 =       add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           __a		 =       '''post_processor'''
           __a		 =       getattr(self.backend_tokenizer  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           if tokenizer_component_instance:
               __a		 =       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		 =       tuple(state['''sep'''])
               if "cls" in state:
                   __a		 =       tuple(state['''cls'''])

               __a		 =       False

               if state.get('''add_prefix_space'''  ,	__SCREAMING_SNAKE_CASE) != add_prefix_space:
                   __a		 =       add_prefix_space
                   __a		 =       True

               if state.get('''trim_offsets'''  ,	__SCREAMING_SNAKE_CASE) != trim_offsets:
                   __a		 =       trim_offsets
                   __a		 =       True

               if changes_to_apply:
                   __a		 =       getattr(__SCREAMING_SNAKE_CASE  ,	state.pop('''type'''))
                   __a		 =       component_class(**__SCREAMING_SNAKE_CASE)
                   setattr(self.backend_tokenizer  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       @property
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           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							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       AddedToken(__SCREAMING_SNAKE_CASE  ,	lstrip=__SCREAMING_SNAKE_CASE  ,	rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else value
           __a		 =       value



       def 	_lowerCamelCase    (	self							:  Tuple  ,	*__SCREAMING_SNAKE_CASE							:  List[str]  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       kwargs.get('''is_split_into_words'''  ,	__SCREAMING_SNAKE_CASE)

           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(*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any  ,	*__SCREAMING_SNAKE_CASE							:  List[Any]  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       kwargs.get('''is_split_into_words'''  ,	__SCREAMING_SNAKE_CASE)

           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(*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           __a		 =       self._tokenizer.model.save(__SCREAMING_SNAKE_CASE  ,	name=__SCREAMING_SNAKE_CASE)
           return tuple(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None):


           '''simple docstring'''

           __a		 =       [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							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           __a		 =       [self.sep_token_id]
           __a		 =       [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

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase       )       ):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				-1     ,				-1       )       ):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				len(_UpperCAmelCase       )       )       ):
        if board[i][j] == 1:
            return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





