



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


_lowercase: List[str]      			=		logging.get_logger(__name__)

_lowercase: Optional[int]      			=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase: int      			=		{
    "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"
        ),
    }
}

_lowercase: Tuple      			=		{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_lowercase: str      			=		{
    "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							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							VOCAB_FILES_NAMES
	__A							=							PRETRAINED_VOCAB_FILES_MAP
	__A							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A							=							PRETRAINED_INIT_CONFIGURATION
	__A							=							RoFormerTokenizer




	def __init__(self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=True		,       lowerCamelCase_="[UNK]"		,       lowerCamelCase_="[SEP]"		,       lowerCamelCase_="[PAD]"		,       lowerCamelCase_="[CLS]"		,       lowerCamelCase_="[MASK]"		,       lowerCamelCase_=True		,       lowerCamelCase_=None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__(
						    lowerCamelCase_		,       tokenizer_file=lowerCamelCase_		,       do_lower_case=lowerCamelCase_		,       unk_token=lowerCamelCase_		,       sep_token=lowerCamelCase_		,       pad_token=lowerCamelCase_		,       cls_token=lowerCamelCase_		,       mask_token=lowerCamelCase_		,       tokenize_chinese_chars=lowerCamelCase_		,       strip_accents=lowerCamelCase_		,       **lowerCamelCase_		,       )

						a					     =   json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
						if (
						    pre_tok_state.get("lowercase"		,       lowerCamelCase_		) != do_lower_case
						    or pre_tok_state.get("strip_accents"		,       lowerCamelCase_		) != strip_accents
						):
											a					     =   getattr(lowerCamelCase_		,       pre_tok_state.pop("type"		)		)
											a					     =   do_lower_case
											a					     =   strip_accents
											a					     =   pre_tok_class(**lowerCamelCase_		)

						a					     =   do_lower_case




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

						a					     =   self.__dict__.copy()
						a					     =   BertPreTokenizer()
						return state




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

						a					     =   d
						a					     =   self.__dict__["_tokenizer"].get_vocab()
						a					     =   PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase_		)		)




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

						a					     =   [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		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   [self.sep_token_id]
						a					     =   [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		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   self._tokenizer.model.save(lowerCamelCase_		,       name=lowerCamelCase_		)
						return tuple(lowerCamelCase_		)




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

						a					     =   BertPreTokenizer()
						return super().save_pretrained(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		)





def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     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]
