



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: Optional[Any]      			=		logging.get_logger(__name__)

_lowercase: int      			=		"▁"

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

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

_lowercase: 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",
    },
}

_lowercase: Optional[Any]      			=		{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

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



class 	_lowercase							(   lowerCAmelCase	):





	"""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		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_="utf8"		,       lowerCamelCase_="[UNK]"		,       lowerCamelCase_="[SEP]"		,       lowerCamelCase_="[PAD]"		,       lowerCamelCase_="[CLS]"		,       lowerCamelCase_="[MASK]"		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   {} if sp_model_kwargs is None else sp_model_kwargs
						super().__init__(
						    do_lower_case=lowerCamelCase_		,       unk_token=lowerCamelCase_		,       sep_token=lowerCamelCase_		,       pad_token=lowerCamelCase_		,       cls_token=lowerCamelCase_		,       mask_token=lowerCamelCase_		,       vocab_file=lowerCamelCase_		,       encoding=lowerCamelCase_		,       sp_model_kwargs=self.sp_model_kwargs		,       **lowerCamelCase_		,       )
						a					     =   do_lower_case
						a					     =   sentencepiece_model_ckpt
						a					     =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						if text is None:
											return None

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

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

						a     ,				a     ,				a					     =   normalized_text, [], 0

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

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

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




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return len(self.vocab		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def __getstate__(self		):
						"""simple docstring"""

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




	def __setstate__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   d

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

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=64		,       lowerCamelCase_=0.1		):
						"""simple docstring"""

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

						if not enable_sampling:
											a					     =   self.sp_model.EncodeAsPieces(lowerCamelCase_		)
						else:
											a					     =   self.sp_model.SampleEncodeAsPieces(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   []
						for pi, piece in enumerate(lowerCamelCase_		):
											if piece == SPIECE_UNDERLINE:
																if not pieces[pi + 1].startswith(lowerCamelCase_		) and pi != 0:
																					new_pieces.append(lowerCamelCase_		)
																					continue
																else:
																					continue
											a					     =   0
											for i, chunk in enumerate(lowerCamelCase_		):
																if chunk == SPIECE_UNDERLINE:
																					continue
																if self.is_ch_char(lowerCamelCase_		) or self.is_punct(lowerCamelCase_		):
																					if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																										new_pieces.append(piece[lst_i:i]		)
																					new_pieces.append(lowerCamelCase_		)
																					a					     =   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]		)
																					a					     =   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]		)
																					a					     =   i
											if len(lowerCamelCase_		) > lst_i:
																new_pieces.append(piece[lst_i:]		)
						return new_pieces




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=False		):
						"""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(lowerCamelCase_		)) + [1, 1] + ([0] * len(lowerCamelCase_		)) + [1]
						return [1] + ([0] * len(lowerCamelCase_		)) + [1]




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

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

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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

						return token_to_idx




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   0
						if os.path.isdir(lowerCamelCase_		):
											a					     =   os.path.join(
											    lowerCamelCase_		,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
						else:
											a					     =   (filename_prefix + "-" if filename_prefix else "") + save_directory
						with open(lowerCamelCase_		,       "w"		,       encoding="utf-8"		) as writer:
											for token, token_index in sorted(self.vocab.items()		,       key=lambda lowerCamelCase_		: 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!"		)
																					a					     =   token_index
																writer.write(token + "\n"		)
																index += 1

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

						return (vocab_file,)





import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
