






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


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)

__lowerCamelCase :      int										=				"""▁"""

__lowerCamelCase :      Union[str, Any]										=				{"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

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

__lowerCamelCase :      str										=				{
    """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""",
    },
}

__lowerCamelCase :      Any										=				{
    """ernie-m-base""": 514,
    """ernie-m-large""": 514,
}

__lowerCamelCase :      Tuple										=				{
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["input_ids"]

					a_     =		VOCAB_FILES_NAMES
					a_     =		PRETRAINED_INIT_CONFIGURATION
					a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_     =		PRETRAINED_VOCAB_FILES_MAP
					a_     =		RESOURCE_FILES_NAMES



					def __init__( self					:  Dict							,		__A					:  List[Any]							,		__A					:  int=None							,		__A					:  str=False							,		__A					:  List[Any]="utf8"							,		__A					:  Optional[Any]="[UNK]"							,		__A					:  Optional[Any]="[SEP]"							,		__A					:  Tuple="[PAD]"							,		__A					:  int="[CLS]"							,		__A					:  Optional[int]="[MASK]"							,		__A					:  Optional[Dict[str, Any]] = None							,		**__A					:  Optional[Any]							,		):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.

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

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



					def 	_lowercase					( self					:  Any							,		__A					:  Tuple	):
						if text is None:
							return None

						snake_case__			:				str       =  self.tokenize(__A	)
						snake_case__,						snake_case__			:				Optional[Any]       =  "", []

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

						snake_case__,						snake_case__,						snake_case__			:				Optional[Any]       =  normalized_text, [], 0

						if self.do_lower_case:
							snake_case__			:				Union[str, Any]       =  text.lower()

						for token in split_tokens:
							if token[:1] == "▁":
								snake_case__			:				Optional[int]       =  token[1:]
							snake_case__			:				Optional[Any]       =  text[offset:].index(__A	) + offset
							snake_case__			:				List[str]       =  start + len(__A	)

							token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)	)
							snake_case__			:				int       =  end
						return token_mapping



					@property
					def 	_lowercase					( self					:  Tuple	):
						return len(self.vocab	)



					def 	_lowercase					( self					:  Dict	):
						return dict(self.vocab							,		**self.added_tokens_encoder	)



					def __getstate__( self					:  List[Any]	):
						snake_case__			:				Union[str, Any]       =  self.__dict__.copy()
						snake_case__			:				Optional[Any]       =  None
						return state



					def __setstate__( self					:  List[str]							,		__A					:  Optional[Any]	):
						snake_case__			:				Any       =  d

						# for backward compatibility
						if not hasattr(self							,		"sp_model_kwargs"	):
							snake_case__			:				Any       =  {}

						snake_case__			:				List[Any]       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(self.sentencepiece_model_ckpt	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str	):
						return "".join((self.SP_CHAR_MAPPING.get(__A							,		__A	) for c in text)	)



					def 	_lowercase					( self					:  Any							,		__A					:  Any							,		__A					:  List[Any]=False							,		__A					:  Optional[Any]=6_4							,		__A					:  Any=0.1	):

						if self.sp_model_kwargs.get("enable_sampling"	) is True:
							snake_case__			:				Union[str, Any]       =  True
						if self.sp_model_kwargs.get("alpha"	) is not None:
							snake_case__			:				Dict       =  self.sp_model_kwargs.get("alpha"	)
						if self.sp_model_kwargs.get("nbest_size"	) is not None:
							snake_case__			:				List[Any]       =  self.sp_model_kwargs.get("nbest_size"	)

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



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Any	):
						snake_case__			:				Union[str, Any]       =  "".join(__A	).replace(__A							,		" "	).strip()
						return out_string



					def 	_lowercase					( self					:  List[str]							,		__A					:  Optional[int]	):
						snake_case__			:				int       =  self.convert_ids_to_tokens(__A	)
						snake_case__			:				Union[str, Any]       =  "".join(__A	).replace(__A							,		" "	).strip()
						return out_string



					def 	_lowercase					( self					:  Dict							,		__A					:  int	):
						return self.vocab.get(__A							,		self.vocab.get(self.unk_token	)	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  int	):
						return self.reverse_vocab.get(__A							,		self.unk_token	)



					def 	_lowercase					( self					:  List[str]							,		__A					:  Tuple							,		__A					:  Optional[int]=None	):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						snake_case__			:				Optional[Any]       =  [self.cls_token_id]
						snake_case__			:				Union[str, Any]       =  [self.sep_token_id]
						return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



					def 	_lowercase					( self					:  List[Any]							,		__A					:  int							,		__A					:  str=None	):
						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 	_lowercase					( self					:  Optional[int]							,		__A					:  Any							,		__A					:  Dict=None							,		__A					:  List[str]=False	):

						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(__A	)) + [1, 1] + ([0] * len(__A	)) + [1]
						return [1] + ([0] * len(__A	)) + [1]



					def 	_lowercase					( self					:  List[str]							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
						if token_ids_a is None:
							# [CLS] X [SEP]
							return (len(__A	) + 2) * [0]

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



					def 	_lowercase					( self					:  List[str]							,		__A					:  int	):
						if "\u4e00" <= char <= "\u9fff":
							return True
						return False



					def 	_lowercase					( self					:  List[Any]							,		__A					:  List[str]	):
						if ("a" <= char <= "z") or ("A" <= char <= "Z"):
							return True
						return False



					def 	_lowercase					( self					:  Tuple							,		__A					:  str	):
						if char in ",;:.?!~，；：。？！《》【】":
							return True
						return False



					def 	_lowercase					( self					:  Tuple							,		__A					:  Dict	):
						if char == " " or char == "\t" or char == "\n" or char == "\r":
							return True
						if len(__A	) == 1:
							snake_case__			:				List[str]       =  unicodedata.category(__A	)
							if cat == "Zs":
								return True
						return False



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  {}
						with io.open(__A							,		"r"							,		encoding="utf-8"	) as f:
							for index, line in enumerate(__A	):
								snake_case__			:				List[str]       =  line.rstrip("\n"	)
								snake_case__			:				int       =  int(__A	)

						return token_to_idx






					def 	_lowercase					( self					:  List[Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
						snake_case__			:				List[Any]       =  0
						if os.path.isdir(__A	):
							snake_case__			:				str       =  os.path.join(
							    __A							,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
						else:
							snake_case__			:				List[str]       =  (filename_prefix + "-" if filename_prefix else "") + save_directory
						with open(__A							,		"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!"	)
									snake_case__			:				Optional[Any]       =  token_index
								writer.write(token + "\n"	)
								index += 1

						snake_case__			:				Any       =  os.path.join(__A							,		"sentencepiece.bpe.model"	)
						with open(__A							,		"wb"	) as fi:
							snake_case__			:				str       =  self.sp_model.serialized_model_proto()
							fi.write(__A	)

						return (vocab_file,)











__lowerCamelCase :      Optional[int]										=				"""Tobias Carryer"""

from time import time



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  Dict=int(time()	)	):  # noqa: B008
						snake_case__			:				List[Any]       =  multiplier
						snake_case__			:				Optional[int]       =  increment
						snake_case__			:				Optional[int]       =  modulo
						snake_case__			:				Union[str, Any]       =  seed






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Union[str, Any]       =  (self.multiplier * self.seed + self.increment) % self.modulo
						return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	__lowerCamelCase :      int										=				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
	while True:
		print(lcg.next_number())



