
"""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


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

UpperCamelCase_					:						List[str]				= {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_					:						Optional[int]				= {
    '''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'''
        ),
    }
}

UpperCamelCase_					:						Optional[int]				= {
    '''junnyu/roformer_chinese_small''': 1536,
    '''junnyu/roformer_chinese_base''': 1536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


UpperCamelCase_					:						Optional[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 __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				PRETRAINED_INIT_CONFIGURATION
			snake_case	      =				RoFormerTokenizer




			def __init__(				self						:					List[Any]       ,   _snake_case						:					List[Any]=None       ,   _snake_case						:					str=None       ,   _snake_case						:					str=True       ,   _snake_case						:					Optional[int]="[UNK]"       ,   _snake_case						:					List[Any]="[SEP]"       ,   _snake_case						:					List[Any]="[PAD]"       ,   _snake_case						:					Tuple="[CLS]"       ,   _snake_case						:					List[Any]="[MASK]"       ,   _snake_case						:					Optional[Any]=True       ,   _snake_case						:					List[Any]=None       ,   **_snake_case						:					Any       ,   )      -> str:







								"""simple docstring"""
								super().__init__(
								    _snake_case       ,   tokenizer_file=_snake_case       ,   do_lower_case=_snake_case       ,   unk_token=_snake_case       ,   sep_token=_snake_case       ,   pad_token=_snake_case       ,   cls_token=_snake_case       ,   mask_token=_snake_case       ,   tokenize_chinese_chars=_snake_case       ,   strip_accents=_snake_case       ,   **_snake_case       ,   )

								A_				 =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    pre_tok_state.get("lowercase"       ,   _snake_case	) != do_lower_case
								    or pre_tok_state.get("strip_accents"       ,   _snake_case	) != strip_accents
								):
													A_				 =						getattr(_snake_case       ,   pre_tok_state.pop("type"	)	)
													A_				 =						do_lower_case
													A_				 =						strip_accents
													A_				 =						pre_tok_class(**_snake_case	)

								A_				 =						do_lower_case




			def __getstate__(				self						:					Optional[int]	)      -> str:







								"""simple docstring"""
								A_				 =						self.__dict__.copy()
								A_				 =						BertPreTokenizer()
								return state




			def __setstate__(				self						:					Optional[int]       ,   _snake_case						:					Optional[Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						d
								A_				 =						self.__dict__["_tokenizer"].get_vocab()
								A_				 =						PreTokenizer.custom(JiebaPreTokenizer(_snake_case	)	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Any       ,   _snake_case						:					Optional[Any]=None	)      -> int:







								"""simple docstring"""
								A_				 =						[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      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""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	) * [0] + len(token_ids_a + sep	) * [1]




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								A_				 =						self._tokenizer.model.save(_snake_case       ,   name=_snake_case	)
								return tuple(_snake_case	)






			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					Dict       ,   _snake_case						:					Dict=None       ,   _snake_case						:					int=None       ,   _snake_case						:					Any=False       ,   **_snake_case						:					int       ,   )      -> str:







								"""simple docstring"""
								A_				 =						BertPreTokenizer()
								return super().save_pretrained(_snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	)






"""simple docstring"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




