






"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


A_    =							logging.get_logger(__name__)

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

A_    =							{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

A_    =							{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']
							snake_case_     =							GPTaTokenizer


							def __init__(			self      :						Optional[int]			,    snake_case      :						str=None			,    snake_case      :						Tuple=None			,    snake_case      :						Union[str, Any]=None			,    snake_case      :						str="<|endoftext|>"			,    snake_case      :						List[Any]="<|endoftext|>"			,    snake_case      :						List[str]="<|endoftext|>"			,    snake_case      :						Dict=False			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													super().__init__(
													    __A			,    __A			,    tokenizer_file=__A			,    unk_token=__A			,    bos_token=__A			,    eos_token=__A			,    add_prefix_space=__A			,    **__A			,    )

													A__						:  Any		  =  kwargs.pop("""add_bos_token"""			,    __A							)

													A__						:  Dict		  =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
													if pre_tok_state.get("""add_prefix_space"""			,    __A							) != add_prefix_space:
																			A__						:  Dict		  =  getattr(__A			,    pre_tok_state.pop("""type"""							)							)
																			A__						:  int		  =  add_prefix_space
																			A__						:  Union[str, Any]		  =  pre_tok_class(**__A							)

													A__						:  Tuple		  =  add_prefix_space


							def 						_UpperCamelCase       (			self      :						Tuple			,    *snake_case      :						Tuple			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													A__						:  int		  =  kwargs.get("""is_split_into_words"""			,    __A							)
													assert self.add_prefix_space or not is_split_into_words, (
													    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
													    "to use it with pretokenized inputs."
													)

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


							def 						_UpperCamelCase       (			self      :						List[Any]			,    *snake_case      :						Optional[int]			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.get("""is_split_into_words"""			,    __A							)

													assert self.add_prefix_space or not is_split_into_words, (
													    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
													    "to use it with pretokenized inputs."
													)

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


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													A__						:  int		  =  self._tokenizer.model.save(__A			,    name=__A							)
													return tuple(__A							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						"Conversation"							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(__A			,    add_special_tokens=__A							) + [self.eos_token_id]							)

													if len(__A							) > self.model_max_length:
																			A__						:  Tuple		  =  input_ids[-self.model_max_length :]
													return input_ids














"""simple docstring"""



from __future__ import annotations

A_    =							[]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->bool:
						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      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int      )		->bool:
						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__						:  Tuple		  =  1
																		solve(UpperCAmelCase__,						row + 1      )
																		A__						:  Optional[int]		  =  0
						return False




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]]      )		->None:
						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"))
A_    =							8
A_    =							[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))






