
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_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE				:str									=							logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE				:Optional[Any]									=							{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE				:List[Any]									=							{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	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_          =	RobertaTokenizer






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

					__A       =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
					if pre_tok_state.get("add_prefix_space"				,A					) != add_prefix_space:
									__A       =					getattr(A				,pre_tok_state.pop("type"					)					)
									__A       =					add_prefix_space
									__A       =					pre_tok_class(**A					)

					__A       =					add_prefix_space

					__A       =					"post_processor"
					__A       =					getattr(self.backend_tokenizer				,A				,A					)
					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"				,A					) != add_prefix_space:
													__A       =					add_prefix_space
													__A       =					True

									if state.get("trim_offsets"				,A					) != trim_offsets:
													__A       =					trim_offsets
													__A       =					True

									if changes_to_apply:
													__A       =					getattr(A				,state.pop("type"					)					)
													__A       =					component_class(**A					)
													setattr(self.backend_tokenizer				,A				,A					)






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					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 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					List[Any]					):
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else value
					__A       =					value






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,*A   :					List[str]				,**A   :					Optional[int]					):
					__A       =					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   :					int				,*A   :					str				,**A   :					Optional[Any]					):
					__A       =					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   :					int				,A   :					str				,A   :					Optional[str] = None					):
					__A       =					self._tokenizer.model.save(A				,name=A					)
					return tuple(A					)






	def 							UpperCamelCase_    (			self   :					Any				,A   :					Any				,A   :					Any=None					):
					__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 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					List[int]				,A   :					Optional[List[int]] = None					):
					__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]





'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


