
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__	=							logging.get_logger(__name__)

lowerCamelCase__	=							"▁"

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

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

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

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: List[str]	=		["input_ids"]

						UpperCamelCase_							: List[str]	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Any	=		PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_							: str	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Optional[Any]	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Any	=		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'''

								_UpperCamelCase						    =	{} 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       ,							)
								_UpperCamelCase						    =	do_lower_case
								_UpperCamelCase						    =	sentencepiece_model_ckpt
								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	self.load_vocab(filepath=a				)
								else:
										_UpperCamelCase						    =	{self.sp_model.id_to_piece(a				): id for id in range(self.sp_model.get_piece_size()				)}
								_UpperCamelCase						    =	{v: k for k, v in self.vocab.items()}

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								if text is None:
										return None

								_UpperCamelCase						    =	self.tokenize(a				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	"""""", []

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

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	normalized_text, [], 0

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

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

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

						@property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								return len(self.vocab				)

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


								'''simple docstring'''

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

						def __getstate__(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.__dict__.copy()
								_UpperCamelCase						    =	None
								return state

						def __setstate__(			self       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	d

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

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

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a=False       ,							a=64       ,							a=0.1				)					->      Tuple:


								'''simple docstring'''

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

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

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	"""""".join(a				).replace(a       ,							""" """				).strip()
								return out_string

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.convert_ids_to_tokens(a				)
								_UpperCamelCase						    =	"""""".join(a				).replace(a       ,							""" """				).strip()
								return out_string

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


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      Dict:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a=None				)					->      int:


								'''simple docstring'''

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

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


								'''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				)					->      Tuple:


								'''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(a				)) + [1, 1] + ([0] * len(a				)) + [1]
								return [1] + ([0] * len(a				)) + [1]

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


								'''simple docstring'''

								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  A_				(			self       ,							a				)					->      Union[str, Any]:


								'''simple docstring'''

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

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


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      Any:


								'''simple docstring'''

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

								return token_to_idx







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


								'''simple docstring'''

								_UpperCamelCase						    =	0
								if os.path.isdir(a				):
										_UpperCamelCase						    =	os.path.join(
										    a       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
								else:
										_UpperCamelCase						    =	(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!"""				)
														_UpperCamelCase						    =	token_index
												writer.write(token + """\n"""				)
												index += 1

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

								return (vocab_file,)





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



