
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


snake_case :		Union[str, Any]							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  "▁"

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

snake_case :		Optional[int]							      =  {
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

snake_case :		int							      =  {
    "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",
    },
}

snake_case :		Optional[int]							      =  {
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

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





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    ["input_ids"]

			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    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						,   ):
				# 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.

				__magic_name__		:		Any	   =     {} 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						,   )
				__magic_name__		:		Union[str, Any]	   =     do_lower_case
				__magic_name__		:		List[Any]	   =     sentencepiece_model_ckpt
				__magic_name__		:		List[str]	   =     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:
					__magic_name__		:		Dict	   =     self.load_vocab(filepath=_a		)
				else:
					__magic_name__		:		Optional[int]	   =     {self.sp_model.id_to_piece(_a		): id for id in range(self.sp_model.get_piece_size()		)}
				__magic_name__		:		str	   =     {v: k for k, v in self.vocab.items()}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if text is None:
					return None

				__magic_name__		:		Optional[Any]	   =     self.tokenize(_a		)
				__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     "", []

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

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

				if self.do_lower_case:
					__magic_name__		:		Optional[int]	   =     text.lower()

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

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







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.vocab		)







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







			def __getstate__(				self		):
				__magic_name__		:		int	   =     self.__dict__.copy()
				__magic_name__		:		int	   =     None
				return state







			def __setstate__(				self						,   _a		):
				__magic_name__		:		str	   =     d

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

				__magic_name__		:		Tuple	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.sentencepiece_model_ckpt		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return "".join((self.SP_CHAR_MAPPING.get(_a						,   _a		) for c in text)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=False						,   _a=64						,   _a=0.1		):

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

				if not enable_sampling:
					__magic_name__		:		Tuple	   =     self.sp_model.EncodeAsPieces(_a		)
				else:
					__magic_name__		:		Union[str, Any]	   =     self.sp_model.SampleEncodeAsPieces(_a						,   _a						,   _a		)
				__magic_name__		:		Optional[int]	   =     []
				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
					__magic_name__		:		Dict	   =     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		)
							__magic_name__		:		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]		)
							__magic_name__		:		str	   =     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]		)
							__magic_name__		:		List[str]	   =     i
					if len(_a		) > lst_i:
						new_pieces.append(piece[lst_i:]		)
				return new_pieces







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		List[Any]	   =     "".join(_a		).replace(_a						,   " "		).strip()
				return out_string







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		List[Any]	   =     self.convert_ids_to_tokens(_a		)
				__magic_name__		:		Optional[Any]	   =     "".join(_a		).replace(_a						,   " "		).strip()
				return out_string







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.vocab.get(_a						,   self.vocab.get(self.unk_token		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.reverse_vocab.get(_a						,   self.unk_token		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None		):
				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		int	   =     [self.cls_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.sep_token_id]
				return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None						,   _a=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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = 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 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if "\u4e00" <= char <= "\u9fff":
					return True
				return False







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if ("a" <= char <= "z") or ("A" <= char <= "Z"):
					return True
				return False







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if char in ",;:.?!~，；：。？！《》【】":
					return True
				return False







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if char == " " or char == "\t" or char == "\n" or char == "\r":
					return True
				if len(_a		) == 1:
					__magic_name__		:		List[Any]	   =     unicodedata.category(_a		)
					if cat == "Zs":
						return True
				return False







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

				return token_to_idx







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Tuple	   =     0
				if os.path.isdir(_a		):
					__magic_name__		:		List[Any]	   =     os.path.join(
					    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				else:
					__magic_name__		:		int	   =     (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!"		)
							__magic_name__		:		List[str]	   =     token_index
						writer.write(token + "\n"		)
						index += 1

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

				return (vocab_file,)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
