

"""simple docstring"""


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a       :List[str]     =			logging.get_logger(__name__)

a       :Optional[int]     =			"▁"

a       :List[Any]     =			{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

a       :List[Any]     =			{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

a       :Optional[Any]     =			{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

a       :Tuple     =			{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

a       :Optional[Any]     =			{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}

class   __a				(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :List[str]							=			["input_ids"]

     _SCREAMING_SNAKE_CASE    :Dict							=			VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE    :Union[str, Any]							=			PRETRAINED_INIT_CONFIGURATION
     _SCREAMING_SNAKE_CASE    :Dict							=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE    :List[str]							=			PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE    :Tuple							=			RESOURCE_FILES_NAMES

     def __init__(    self						,     _a						,     _a=None						,     _a=False						,     _a="utf8"						,     _a="[UNK]"						,     _a="[SEP]"						,     _a="[PAD]"						,     _a="[CLS]"						,     _a="[MASK]"						,     _a = None						,     **_a						,     )	->   None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						{} if sp_model_kwargs is None else sp_model_kwargs
           super().__init__(
               do_lower_case=SCREAMING_SNAKE_CASE_						,     unk_token=SCREAMING_SNAKE_CASE_						,     sep_token=SCREAMING_SNAKE_CASE_						,     pad_token=SCREAMING_SNAKE_CASE_						,     cls_token=SCREAMING_SNAKE_CASE_						,     mask_token=SCREAMING_SNAKE_CASE_						,     vocab_file=SCREAMING_SNAKE_CASE_						,     encoding=SCREAMING_SNAKE_CASE_						,     sp_model_kwargs=self.sp_model_kwargs						,     **SCREAMING_SNAKE_CASE_						,     )
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						do_lower_case
           SCREAMING_SNAKE_CASE__ :   Dict   		=						sentencepiece_model_ckpt
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(SCREAMING_SNAKE_CASE_	)

           # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
           if vocab_file is not None:
                 SCREAMING_SNAKE_CASE__ :   Any   		=						self.load_vocab(filepath=SCREAMING_SNAKE_CASE_	)
           else:
                 SCREAMING_SNAKE_CASE__ :   Tuple   		=						{self.sp_model.id_to_piece(SCREAMING_SNAKE_CASE_	): id for id in range(self.sp_model.get_piece_size()	)}
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{v: k for k, v in self.vocab.items()}

     def        _a      (    self						,     _a	)	->   List[Any]:


           """simple docstring"""


           if text is None:
                 return None

           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						self.tokenize(SCREAMING_SNAKE_CASE_	)
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   List[Any]   		=						"""""", []

           for i, ch in enumerate(SCREAMING_SNAKE_CASE_	):
                 if ch in self.SP_CHAR_MAPPING:
                       SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE_	)
                 else:
                       SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						unicodedata.normalize("""NFKC"""						,     SCREAMING_SNAKE_CASE_	)
                 if self.is_whitespace(SCREAMING_SNAKE_CASE_	):
                       continue
                 normalized_text += ch
                 char_mapping.extend([i] * len(SCREAMING_SNAKE_CASE_	)	)

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						normalized_text, [], 0

           if self.do_lower_case:
                 SCREAMING_SNAKE_CASE__ :   Tuple   		=						text.lower()

           for token in split_tokens:
                 if token[:1] == "▁":
                       SCREAMING_SNAKE_CASE__ :   Any   		=						token[1:]
                 SCREAMING_SNAKE_CASE__ :   Any   		=						text[offset:].index(SCREAMING_SNAKE_CASE_	) + offset
                 SCREAMING_SNAKE_CASE__ :   Any   		=						start + len(SCREAMING_SNAKE_CASE_	)

                 token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)	)
                 SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						end
           return token_mapping

     @property
     def        _a      (    self	)	->   Union[str, Any]:


           """simple docstring"""


           return len(self.vocab	)

     def        _a      (    self	)	->   Optional[int]:


           """simple docstring"""


           return dict(self.vocab						,     **self.added_tokens_encoder	)

     def __getstate__(    self	)	->   int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						self.__dict__.copy()
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						None
           return state

     def __setstate__(    self						,     _a	)	->   List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						d

           # for backward compatibility
           if not hasattr(self						,     """sp_model_kwargs"""	):
                 SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{}

           SCREAMING_SNAKE_CASE__ :   str   		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.sentencepiece_model_ckpt	)

     def        _a      (    self						,     _a	)	->   str:


           """simple docstring"""


           return "".join((self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE_						,     SCREAMING_SNAKE_CASE_	) for c in text)	)

     def        _a      (    self						,     _a						,     _a=False						,     _a=64						,     _a=0.1	)	->   Any:


           """simple docstring"""


           if self.sp_model_kwargs.get("""enable_sampling"""	) is True:
                 SCREAMING_SNAKE_CASE__ :   Tuple   		=						True
           if self.sp_model_kwargs.get("""alpha"""	) is not None:
                 SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						self.sp_model_kwargs.get("""alpha"""	)
           if self.sp_model_kwargs.get("""nbest_size"""	) is not None:
                 SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						self.sp_model_kwargs.get("""nbest_size"""	)

           if not enable_sampling:
                 SCREAMING_SNAKE_CASE__ :   List[Any]   		=						self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE_	)
           else:
                 SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.sp_model.SampleEncodeAsPieces(SCREAMING_SNAKE_CASE_						,     SCREAMING_SNAKE_CASE_						,     SCREAMING_SNAKE_CASE_	)
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						[]
           for pi, piece in enumerate(SCREAMING_SNAKE_CASE_	):
                 if piece == SPIECE_UNDERLINE:
                       if not pieces[pi + 1].startswith(SCREAMING_SNAKE_CASE_	) and pi != 0:
                             new_pieces.append(SCREAMING_SNAKE_CASE_	)
                             continue
                       else:
                             continue
                 SCREAMING_SNAKE_CASE__ :   List[Any]   		=						0
                 for i, chunk in enumerate(SCREAMING_SNAKE_CASE_	):
                       if chunk == SPIECE_UNDERLINE:
                             continue
                       if self.is_ch_char(SCREAMING_SNAKE_CASE_	) or self.is_punct(SCREAMING_SNAKE_CASE_	):
                             if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                   new_pieces.append(piece[lst_i:i]	)
                             new_pieces.append(SCREAMING_SNAKE_CASE_	)
                             SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						i + 1
                       elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                             if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                   new_pieces.append(piece[lst_i:i]	)
                             SCREAMING_SNAKE_CASE__ :   Dict   		=						i
                       elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                             if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                   new_pieces.append(piece[lst_i:i]	)
                             SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						i
                 if len(SCREAMING_SNAKE_CASE_	) > lst_i:
                       new_pieces.append(piece[lst_i:]	)
           return new_pieces

     def        _a      (    self						,     _a	)	->   Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Tuple   		=						"""""".join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_						,     """ """	).strip()
           return out_string

     def        _a      (    self						,     _a	)	->   Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						"""""".join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_						,     """ """	).strip()
           return out_string

     def        _a      (    self						,     _a	)	->   List[str]:


           """simple docstring"""


           return self.vocab.get(SCREAMING_SNAKE_CASE_						,     self.vocab.get(self.unk_token	)	)

     def        _a      (    self						,     _a	)	->   List[Any]:


           """simple docstring"""


           return self.reverse_vocab.get(SCREAMING_SNAKE_CASE_						,     self.unk_token	)

     def        _a      (    self						,     _a						,     _a=None	)	->   List[str]:


           """simple docstring"""


           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           SCREAMING_SNAKE_CASE__ :   Any   		=						[self.cls_token_id]
           SCREAMING_SNAKE_CASE__ :   Dict   		=						[self.sep_token_id]
           return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep

     def        _a      (    self						,     _a						,     _a=None	)	->   int:


           """simple docstring"""


           if offset_mapping_a is None:
                 return [(0, 0)] + offset_mapping_a + [(0, 0)]

           return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]

     def        _a      (    self						,     _a						,     _a=None						,     _a=False	)	->   Any:


           """simple docstring"""


           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           """You should not supply a second sequence if the provided sequence of """
                           """ids is already formatted with special tokens for the model."""	)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
                 return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]

     def        _a      (    self						,     _a						,     _a = None	)	->   List[int]:


           """simple docstring"""


           if token_ids_a is None:
                 # [CLS] X [SEP]
                 return (len(SCREAMING_SNAKE_CASE_	) + 2) * [0]

           # [CLS] A [SEP] [SEP] B [SEP]
           return [0] * (len(SCREAMING_SNAKE_CASE_	) + 1) + [1] * (len(SCREAMING_SNAKE_CASE_	) + 3)

     def        _a      (    self						,     _a	)	->   Optional[int]:


           """simple docstring"""


           if "\u4e00" <= char <= "\u9fff":
                 return True
           return False

     def        _a      (    self						,     _a	)	->   Tuple:


           """simple docstring"""


           if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                 return True
           return False

     def        _a      (    self						,     _a	)	->   List[Any]:


           """simple docstring"""


           if char in ",;:.?!~，；：。？！《》【】":
                 return True
           return False

     def        _a      (    self						,     _a	)	->   Union[str, Any]:


           """simple docstring"""


           if char == " " or char == "\t" or char == "\n" or char == "\r":
                 return True
           if len(SCREAMING_SNAKE_CASE_	) == 1:
                 SCREAMING_SNAKE_CASE__ :   Dict   		=						unicodedata.category(SCREAMING_SNAKE_CASE_	)
                 if cat == "Zs":
                       return True
           return False

     def        _a      (    self						,     _a	)	->   Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[str]   		=						{}
           with io.open(SCREAMING_SNAKE_CASE_						,     """r"""						,     encoding="""utf-8"""	) as f:
                 for index, line in enumerate(SCREAMING_SNAKE_CASE_	):
                       SCREAMING_SNAKE_CASE__ :   Dict   		=						line.rstrip("""\n"""	)
                       SCREAMING_SNAKE_CASE__ :   int   		=						int(SCREAMING_SNAKE_CASE_	)

           return token_to_idx





     def        _a      (    self						,     _a						,     _a = None	)	->   Tuple[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Any   		=						0
           if os.path.isdir(SCREAMING_SNAKE_CASE_	):
                 SCREAMING_SNAKE_CASE__ :   List[Any]   		=						os.path.join(
                     SCREAMING_SNAKE_CASE_						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
           else:
                 SCREAMING_SNAKE_CASE__ :   Tuple   		=						(filename_prefix + """-""" if filename_prefix else """""") + save_directory
           with open(SCREAMING_SNAKE_CASE_						,     """w"""						,     encoding="""utf-8"""	) as writer:
                 for token, token_index in sorted(self.vocab.items()						,     key=lambda _a	: kv[1]	):
                       if index != token_index:
                             logger.warning(
                                 f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                                 """ Please check that the vocabulary is not corrupted!"""	)
                             SCREAMING_SNAKE_CASE__ :   Any   		=						token_index
                       writer.write(token + """\n"""	)
                       index += 1

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						os.path.join(SCREAMING_SNAKE_CASE_						,     """sentencepiece.bpe.model"""	)
           with open(SCREAMING_SNAKE_CASE_						,     """wb"""	) as fi:
                 SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE_	)

           return (vocab_file,)






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




