




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


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

lowerCamelCase			:			str								=       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase			:			Tuple								=       {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

lowerCamelCase			:			int								=       {
    "junnyu/roformer_chinese_small": 1_5_3_6,
    "junnyu/roformer_chinese_base": 1_5_3_6,
    "junnyu/roformer_chinese_char_small": 5_1_2,
    "junnyu/roformer_chinese_char_base": 5_1_2,
    "junnyu/roformer_small_discriminator": 1_2_8,
    "junnyu/roformer_small_generator": 1_2_8,
}


lowerCamelCase			:			Optional[Any]								=       {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}




class 		__lowercase						(a_				):




      """simple docstring"""





      _snake_case					=		VOCAB_FILES_NAMES
      _snake_case					=		PRETRAINED_VOCAB_FILES_MAP
      _snake_case					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case					=		PRETRAINED_INIT_CONFIGURATION
      _snake_case					=		RoFormerTokenizer

      def __init__( self							, A=None							, A=None							, A=True							, A="[UNK]"							, A="[SEP]"							, A="[PAD]"							, A="[CLS]"							, A="[MASK]"							, A=True							, A=None							, **A							, )						->							Any:
         super().__init__(
             _SCREAMING_SNAKE_CASE							, tokenizer_file=_SCREAMING_SNAKE_CASE							, do_lower_case=_SCREAMING_SNAKE_CASE							, unk_token=_SCREAMING_SNAKE_CASE							, sep_token=_SCREAMING_SNAKE_CASE							, pad_token=_SCREAMING_SNAKE_CASE							, cls_token=_SCREAMING_SNAKE_CASE							, mask_token=_SCREAMING_SNAKE_CASE							, tokenize_chinese_chars=_SCREAMING_SNAKE_CASE							, strip_accents=_SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE							, )

         snake_case :							Optional[int]           =			json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
         if (
             pre_tok_state.get("""lowercase"""							, _SCREAMING_SNAKE_CASE      ) != do_lower_case
             or pre_tok_state.get("""strip_accents"""							, _SCREAMING_SNAKE_CASE      ) != strip_accents
         ):
            snake_case :							str           =			getattr(_SCREAMING_SNAKE_CASE							, pre_tok_state.pop("""type"""      )      )
            snake_case :							Dict           =			do_lower_case
            snake_case :							List[str]           =			strip_accents
            snake_case :							List[str]           =			pre_tok_class(**_SCREAMING_SNAKE_CASE      )

         snake_case :							Any           =			do_lower_case

      def __getstate__( self      )						->							Tuple:
         snake_case :							Any           =			self.__dict__.copy()
         snake_case :							Optional[Any]           =			BertPreTokenizer()
         return state

      def __setstate__( self							, A      )						->							int:
         snake_case :							Tuple           =			d
         snake_case :							Any           =			self.__dict__["""_tokenizer"""].get_vocab()
         snake_case :							str           =			PreTokenizer.custom(JiebaPreTokenizer(_SCREAMING_SNAKE_CASE      )      )

      def       UpperCAmelCase ( self							, A							, A=None      )						->							Optional[int]:
         snake_case :							str           =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

         return output

      def       UpperCAmelCase ( self							, A							, A = None      )						->							Dict:
         snake_case :							Optional[Any]           =			[self.sep_token_id]
         snake_case :							str           =			[self.cls_token_id]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep      ) * [0]
         return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

      def       UpperCAmelCase ( self							, A							, A = None      )						->							List[Any]:
         snake_case :							List[Any]           =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE							, name=_SCREAMING_SNAKE_CASE      )
         return tuple(_SCREAMING_SNAKE_CASE      )

      def       UpperCAmelCase ( self							, A							, A=None							, A=None							, A=False							, **A							, )						->							List[Any]:
         snake_case :							str           =			BertPreTokenizer()
         return super().save_pretrained(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE      )








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





