

'''simple docstring'''




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


_a					:			List[Any]							=    logging.get_logger(__name__)

_a					:			str							=    {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a					:			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'
        ),
    }
}

_a					:			List[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,
}


_a					:			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 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			int     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Union[str, Any]     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			str     	= PRETRAINED_INIT_CONFIGURATION
  __lowerCAmelCase       :			Dict     	= 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:


    """simple docstring"""
    super().__init__(
        a_ ,			tokenizer_file=a_ ,			do_lower_case=a_ ,			unk_token=a_ ,			sep_token=a_ ,			pad_token=a_ ,			cls_token=a_ ,			mask_token=a_ ,			tokenize_chinese_chars=a_ ,			strip_accents=a_ ,			**a_ ,			)

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

    UpperCAmelCase    				=						do_lower_case
  def __getstate__(  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						BertPreTokenizer()
    return state
  def __setstate__(  self ,			a_  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						d
    UpperCAmelCase    				=						self.__dict__['_tokenizer'].get_vocab()
    UpperCAmelCase    				=						PreTokenizer.custom(JiebaPreTokenizer(a_  )  )
  def     snake_case_      (  self ,			a_ ,			a_=None  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						[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 ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[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 ,			a_ ,			a_ = None  )				->					Tuple[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self._tokenizer.model.save(a_ ,			name=a_  )
    return tuple(a_  )





  def     snake_case_      (  self ,			a_ ,			a_=None ,			a_=None ,			a_=False ,			**a_ ,			)				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						BertPreTokenizer()
    return super().save_pretrained(a_ ,			a_ ,			a_ ,			a_ ,			**a_  )





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int = 1000    ):
  UpperCAmelCase    				=						-1
  UpperCAmelCase    				=						0
  for a in range(1							, n // 3    ):
    # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
    UpperCAmelCase    				=						(n * n - 2 * a * n) // (2 * n - 2 * a)
    UpperCAmelCase    				=						n - a - b
    if c * c == (a * a + b * b):
      UpperCAmelCase    				=						a * b * c
      if candidate >= product:
        UpperCAmelCase    				=						candidate
  return product


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


