

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


A_ :				Dict						 =    logging.get_logger(__name__)

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

A_ :				List[str]						 =    {
    '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'
        ),
    }
}

A_ :				Any						 =    {
    '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,
}


A_ :				Union[str, 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 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= VOCAB_FILES_NAMES
					UpperCAmelCase__:     List[Any]													= PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:     str													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:     str													= PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:     List[str]													= RoFormerTokenizer






					def __init__(				self					, A__=None					, A__=None					, A__=True					, A__="[UNK]"					, A__="[SEP]"					, A__="[PAD]"					, A__="[CLS]"					, A__="[MASK]"					, A__=True					, A__=None					, **A__					, ):
						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__					, )

						A__							:					str       =	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
						):
							A__							:					Dict       =	getattr(A__					, pre_tok_state.pop("""type"""    )    )
							A__							:					int       =	do_lower_case
							A__							:					str       =	strip_accents
							A__							:					int       =	pre_tok_class(**A__    )

						A__							:					List[str]       =	do_lower_case






					def __getstate__(				self    ):
						A__							:					Union[str, Any]       =	self.__dict__.copy()
						A__							:					Tuple       =	BertPreTokenizer()
						return state






					def __setstate__(				self					, A__    ):
						A__							:					List[Any]       =	d
						A__							:					str       =	self.__dict__["""_tokenizer"""].get_vocab()
						A__							:					Optional[int]       =	PreTokenizer.custom(JiebaPreTokenizer(A__    )    )






					def 						__A    (				self					, A__					, A__=None    ):
						A__							:					Tuple       =	[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 						__A    (				self					, A__					, A__ = None    ):
						A__							:					Dict       =	[self.sep_token_id]
						A__							:					Tuple       =	[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 						__A    (				self					, A__					, A__ = None    ):
						A__							:					str       =	self._tokenizer.model.save(A__					, name=A__    )
						return tuple(A__    )






					def 						__A    (				self					, A__					, A__=None					, A__=None					, A__=False					, **A__					, ):
						A__							:					str       =	BertPreTokenizer()
						return super().save_pretrained(A__					, A__					, A__					, A__					, **A__    )






class 					_a		:


					'''simple docstring'''






					def __init__(				self    ):
						A__							:					str       =	""""""
						A__							:					Any       =	""""""
						A__							:					List[Any]       =	[]






					def 						__A    (				self					, A__					, A__    ):
						if m == -1:
							return n + 1
						elif n == -1:
							return m + 1
						elif self.dp[m][n] > -1:
							return self.dp[m][n]
						else:
							if self.worda[m] == self.worda[n]:
								A__							:					Optional[Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
							else:
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(A__					, n - 1    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, A__    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
								A__							:					List[Any]       =	1 + min(A__					, A__					, A__    )

							return self.dp[m][n]






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Tuple       =	worda
						A__							:					Dict       =	worda
						A__							:					Optional[Any]       =	[[-1 for _ in range(len(A__    )    )] for _ in range(len(A__    )    )]

						return self.__min_dist_top_down_dp(len(A__    ) - 1					, len(A__    ) - 1    )






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Optional[Any]       =	worda
						A__							:					Dict       =	worda
						A__							:					Union[str, Any]       =	len(A__    )
						A__							:					List[str]       =	len(A__    )
						A__							:					int       =	[[0 for _ in range(n + 1    )] for _ in range(m + 1    )]

						for i in range(m + 1    ):
							for j in range(n + 1    ):
								if i == 0:  # first string is empty
									A__							:					Tuple       =	j
								elif j == 0:  # second string is empty
									A__							:					Dict       =	i
								elif worda[i - 1] == worda[j - 1]:  # last characters are equal
									A__							:					str       =	self.dp[i - 1][j - 1]
								else:
									A__							:					Union[str, Any]       =	self.dp[i][j - 1]
									A__							:					str       =	self.dp[i - 1][j]
									A__							:					Union[str, Any]       =	self.dp[i - 1][j - 1]
									A__							:					Tuple       =	1 + min(A__					, A__					, A__    )
						return self.dp[m][n]


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    EditDistance()

	print('****************** Testing Edit Distance DP Algorithm ******************')
	print()

	A_ :				int						 =    input('Enter the first string: ').strip()
	A_ :				List[str]						 =    input('Enter the second string: ').strip()

	print()
	print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
	print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
	print()
	print('*************** End of Testing Edit Distance DP Algorithm ***************')



