






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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   "▁"

lowercase    				=   {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

lowercase    				=   {
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

lowercase    				=   {
    "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",
    },
}

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

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




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					["input_ids"]

	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					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:
								# 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_           =				{} 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_           =				do_lower_case
								snake_case_           =				sentencepiece_model_ckpt
								snake_case_           =				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_           =				self.load_vocab(filepath=a   )
								else:
															snake_case_           =				{self.sp_model.id_to_piece(a   ): id for id in range(self.sp_model.get_piece_size()   )}
								snake_case_           =				{v: k for k, v in self.vocab.items()}




	def 				_UpperCamelCase     (			self			,     a   )			->       Any:
								if text is None:
															return None

								snake_case_           =				self.tokenize(a   )
								snake_case_     ,						snake_case_           =				'', []

								for i, ch in enumerate(a   ):
															if ch in self.SP_CHAR_MAPPING:
																						snake_case_           =				self.SP_CHAR_MAPPING.get(a   )
															else:
																						snake_case_           =				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_           =				normalized_text, [], 0

								if self.do_lower_case:
															snake_case_           =				text.lower()

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

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




	@property
	def 				_UpperCamelCase     (			self   )			->       str:
								return len(self.vocab   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								return dict(self.vocab			,     **self.added_tokens_encoder   )




	def __getstate__(			self   )			->       Tuple:
								snake_case_           =				self.__dict__.copy()
								snake_case_           =				None
								return state




	def __setstate__(			self			,     a   )			->       Tuple:
								snake_case_           =				d

								# for backward compatibility
								if not hasattr(self			,     'sp_model_kwargs'   ):
															snake_case_           =				{}

								snake_case_           =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(self.sentencepiece_model_ckpt   )




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								return "".join((self.SP_CHAR_MAPPING.get(a			,     a   ) for c in text)   )




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

								if self.sp_model_kwargs.get('enable_sampling'   ) is True:
															snake_case_           =				True
								if self.sp_model_kwargs.get('alpha'   ) is not None:
															snake_case_           =				self.sp_model_kwargs.get('alpha'   )
								if self.sp_model_kwargs.get('nbest_size'   ) is not None:
															snake_case_           =				self.sp_model_kwargs.get('nbest_size'   )

								if not enable_sampling:
															snake_case_           =				self.sp_model.EncodeAsPieces(a   )
								else:
															snake_case_           =				self.sp_model.SampleEncodeAsPieces(a			,     a			,     a   )
								snake_case_           =				[]
								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_           =				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_           =				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_           =				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_           =				i
															if len(a   ) > lst_i:
																						new_pieces.append(piece[lst_i:]   )
								return new_pieces




	def 				_UpperCamelCase     (			self			,     a   )			->       Any:
								snake_case_           =				''.join(a   ).replace(a			,     ' '   ).strip()
								return out_string




	def 				_UpperCamelCase     (			self			,     a   )			->       str:
								snake_case_           =				self.convert_ids_to_tokens(a   )
								snake_case_           =				''.join(a   ).replace(a			,     ' '   ).strip()
								return out_string




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								return self.vocab.get(a			,     self.vocab.get(self.unk_token   )   )




	def 				_UpperCamelCase     (			self			,     a   )			->       List[Any]:
								return self.reverse_vocab.get(a			,     self.unk_token   )




	def 				_UpperCamelCase     (			self			,     a			,     a=None   )			->       Optional[int]:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								snake_case_           =				[self.cls_token_id]
								snake_case_           =				[self.sep_token_id]
								return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




	def 				_UpperCamelCase     (			self			,     a			,     a=None   )			->       Dict:
								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 				_UpperCamelCase     (			self			,     a			,     a=None			,     a=False   )			->       Union[str, Any]:

								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 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								# 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 				_UpperCamelCase     (			self			,     a   )			->       str:
								if "\u4e00" <= char <= "\u9fff":
															return True
								return False




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[Any]:
								if ("a" <= char <= "z") or ("A" <= char <= "Z"):
															return True
								return False




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if char in ",;:.?!~，；：。？！《》【】":
															return True
								return False




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								if char == " " or char == "\t" or char == "\n" or char == "\r":
															return True
								if len(a   ) == 1:
															snake_case_           =				unicodedata.category(a   )
															if cat == "Zs":
																						return True
								return False




	def 				_UpperCamelCase     (			self			,     a   )			->       Any:
								snake_case_           =				{}
								with io.open(a			,     'r'			,     encoding='utf-8'   ) as f:
															for index, line in enumerate(a   ):
																						snake_case_           =				line.rstrip('\n'   )
																						snake_case_           =				int(a   )

								return token_to_idx






	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       Tuple[str]:
								snake_case_           =				0
								if os.path.isdir(a   ):
															snake_case_           =				os.path.join(
															    a			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
								else:
															snake_case_           =				(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_           =				token_index
																						writer.write(token + '\n'   )
																						index += 1

								snake_case_           =				os.path.join(a			,     'sentencepiece.bpe.model'   )
								with open(a			,     'wb'   ) as fi:
															snake_case_           =				self.sp_model.serialized_model_proto()
															fi.write(a   )

								return (vocab_file,)













def 		__UpperCAmelCase     (	a_ = 10**9):
							snake_case_           =				1
							snake_case_           =				2

							snake_case_           =				0
							snake_case_           =				0
							snake_case_           =				0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														snake_case_           =				2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
	print(f'{solution() = }')





