



"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_lowercase				:				int     =		logging.get_logger(__name__)

_lowercase				:				List[Any]     =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase				:				Union[str, Any]     =		{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

_lowercase				:				List[Any]     =		{
    "junnyu/roformer_chinese_small": 1_5_3_6,
    "junnyu/roformer_chinese_base": 1_5_3_6,
    "junnyu/roformer_chinese_char_small": 5_1_2,
    "junnyu/roformer_chinese_char_base": 5_1_2,
    "junnyu/roformer_small_discriminator": 1_2_8,
    "junnyu/roformer_small_generator": 1_2_8,
}


_lowercase				:				List[Any]     =		{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							VOCAB_FILES_NAMES
					_a         =							PRETRAINED_VOCAB_FILES_MAP
					_a         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a         =							PRETRAINED_INIT_CONFIGURATION
					_a         =							RoFormerTokenizer



					def __init__( self							:						Any,						lowerCamelCase							:						Union[str, Any]=None,						lowerCamelCase							:						Dict=None,						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						Union[str, Any]="[UNK]",						lowerCamelCase							:						List[Any]="[SEP]",						lowerCamelCase							:						List[str]="[PAD]",						lowerCamelCase							:						List[str]="[CLS]",						lowerCamelCase							:						List[str]="[MASK]",						lowerCamelCase							:						int=True,						lowerCamelCase							:						List[Any]=None,						**lowerCamelCase							:						int,						)->      int:
								super().__init__(
								    __A,						tokenizer_file=__A,						do_lower_case=__A,						unk_token=__A,						sep_token=__A,						pad_token=__A,						cls_token=__A,						mask_token=__A,						tokenize_chinese_chars=__A,						strip_accents=__A,						**__A,						)

								lowerCamelCase__	:	Any				=json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
								if (
								    pre_tok_state.get('''lowercase''',						__A  ) != do_lower_case
								    or pre_tok_state.get('''strip_accents''',						__A  ) != strip_accents
								):
											lowerCamelCase__	:	List[str]				=getattr(__A,						pre_tok_state.pop('''type'''  )  )
											lowerCamelCase__	:	Optional[Any]				=do_lower_case
											lowerCamelCase__	:	Dict				=strip_accents
											lowerCamelCase__	:	List[str]				=pre_tok_class(**__A  )

								lowerCamelCase__	:	int				=do_lower_case



					def __getstate__( self							:						Union[str, Any]  )->      str:
								lowerCamelCase__	:	int				=self.__dict__.copy()
								lowerCamelCase__	:	List[Any]				=BertPreTokenizer()
								return state



					def __setstate__( self							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	Any				=d
								lowerCamelCase__	:	Optional[Any]				=self.__dict__['''_tokenizer'''].get_vocab()
								lowerCamelCase__	:	List[str]				=PreTokenizer.custom(JiebaPreTokenizer(__A  )  )



					def 	snake_case ( self							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						str=None  )->      int:
								lowerCamelCase__	:	Optional[int]				=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

								return output



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						List[int],						lowerCamelCase							:						Optional[List[int]] = None  )->      List[int]:
								lowerCamelCase__	:	Optional[int]				=[self.sep_token_id]
								lowerCamelCase__	:	Dict				=[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  ) * [0] + len(token_ids_a + sep  ) * [1]



					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						str,						lowerCamelCase							:						Optional[str] = None  )->      Tuple[str]:
								lowerCamelCase__	:	int				=self._tokenizer.model.save(__A,						name=__A  )
								return tuple(__A  )




					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int,						lowerCamelCase							:						List[Any]=None,						lowerCamelCase							:						Union[str, Any]=None,						lowerCamelCase							:						List[str]=False,						**lowerCamelCase							:						Optional[Any],						)->      Dict:
								lowerCamelCase__	:	Dict				=BertPreTokenizer()
								return super().save_pretrained(__A,						__A,						__A,						__A,						**__A  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




