


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


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

_snake_case					:		int   =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case					:		Union[str, Any]   =    {
    "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"
        ),
    }
}

_snake_case					:		str   =    {
    "junnyu/roformer_chinese_small": 1_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_snake_case					:		Tuple   =    {
    "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     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							int					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							Tuple					  =  RoFormerTokenizer

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[int]=None   ,     lowerCamelCase	:  List[Any]=None   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  Any="[UNK]"   ,     lowerCamelCase	:  Dict="[SEP]"   ,     lowerCamelCase	:  str="[PAD]"   ,     lowerCamelCase	:  Optional[int]="[CLS]"   ,     lowerCamelCase	:  List[str]="[MASK]"   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														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   ,     )

														__snake_case		:      str										=       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
														):
																					__snake_case		:      Any										=       getattr(lowerCamelCase   ,     pre_tok_state.pop("type"    )    )
																					__snake_case		:      List[Any]										=       do_lower_case
																					__snake_case		:      Dict										=       strip_accents
																					__snake_case		:      Optional[Any]										=       pre_tok_class(**lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       do_lower_case

							def __getstate__(							self	:  Optional[int]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       self.__dict__.copy()
														__snake_case		:      Dict										=       BertPreTokenizer()
														return state

							def __setstate__(							self	:  Dict   ,     lowerCamelCase	:  Optional[Any]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       d
														__snake_case		:      Tuple										=       self.__dict__["_tokenizer"].get_vocab()
														__snake_case		:      Any										=       PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase    )    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int=None    )     ->   List[Any]:
														__snake_case		:      int										=       [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    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      List[str]										=       [self.sep_token_id]
														__snake_case		:      Tuple										=       [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    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[str] = None    )     ->   Tuple[str]:
														__snake_case		:      Any										=       self._tokenizer.model.save(lowerCamelCase   ,     name=lowerCamelCase    )
														return tuple(lowerCamelCase    )






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  str=None   ,     lowerCamelCase	:  Union[str, Any]=False   ,     **lowerCamelCase	:  Optional[Any]   ,     )     ->   Union[str, Any]:
														__snake_case		:      Dict										=       BertPreTokenizer()
														return super().save_pretrained(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    )





from __future__ import annotations

from functools import lru_cache
from math import ceil

_snake_case					:		Tuple      =    100

_snake_case					:		int      =    set(range(3, NUM_PRIMES, 2))
primes.add(2)
_snake_case					:		int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
			if prime not in primes:
						continue
			primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=1_0_0     )
def     lowerCAmelCase_					( __lowerCamelCase     ):
							if number_to_partition < 0:
														return set()
							elif number_to_partition == 0:
														return {1}

							__snake_case		:      set[int]										=       set()
							__snake_case		:      int
							__snake_case		:      int

							for prime in primes:
														if prime > number_to_partition:
																					continue
														for sub in partition(number_to_partition - prime     ):
																					ret.add(sub * prime     )

							return ret







def     lowerCAmelCase_					( __lowerCamelCase = 5_0_0_0     ):
							for number_to_partition in range(1							,     __lowerCamelCase     ):
														if len(partition(__lowerCamelCase     )     ) > number_unique_partitions:
																					return number_to_partition
							return None


if __name__ == "__main__":
			print(f'''{solution() = }''')

