



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case		:Dict   	=  logging.get_logger(__name__)

class 					_A :



       def __init__(	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       question_encoder
           __a		 =       generator
           __a		 =       self.question_encoder



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE  ,	exist_ok=__SCREAMING_SNAKE_CASE)
           __a		 =       os.path.join(__SCREAMING_SNAKE_CASE  ,	'''question_encoder_tokenizer''')
           __a		 =       os.path.join(__SCREAMING_SNAKE_CASE  ,	'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	**__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a		 =       kwargs.pop('''config'''  ,	__SCREAMING_SNAKE_CASE)

           if config is None:
               __a		 =       RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a		 =       AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE  ,	config=config.question_encoder  ,	subfolder='''question_encoder_tokenizer''')
           __a		 =       AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE  ,	config=config.generator  ,	subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE)



       def __call__(	self							:  Tuple  ,	*__SCREAMING_SNAKE_CASE							:  str  ,	**__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self.question_encoder



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.generator





       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[str]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  str = "longest"  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	**__SCREAMING_SNAKE_CASE							:  int  ,	):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''  ,	__SCREAMING_SNAKE_CASE  ,	)
           if max_length is None:
               __a		 =       self.current_tokenizer.model_max_length
           __a		 =       self(
               __SCREAMING_SNAKE_CASE  ,	add_special_tokens=__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	max_length=__SCREAMING_SNAKE_CASE  ,	padding=__SCREAMING_SNAKE_CASE  ,	truncation=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a		 =       self.current_tokenizer.model_max_length
           __a		 =       self(
               text_target=__SCREAMING_SNAKE_CASE  ,	add_special_tokens=__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	padding=__SCREAMING_SNAKE_CASE  ,	max_length=__SCREAMING_SNAKE_CASE  ,	truncation=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)
           __a		 =       labels['''input_ids''']
           return model_inputs










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))





