"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase__				  =   {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__				  =   {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}



class      __SCREAMING_SNAKE_CASE    (						UpperCamelCase__   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[Any]	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[str]	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:Dict	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Any	     =					BartTokenizer

  def __init__(		self   :       str					,   __a   :       Optional[Any]=None					,   __a   :       List[Any]=None					,   __a   :       Optional[int]=None					,   __a   :       int="replace"					,   __a   :       str="<s>"					,   __a   :       Tuple="</s>"					,   __a   :       Dict="</s>"					,   __a   :       Union[str, Any]="<s>"					,   __a   :       int="<unk>"					,   __a   :       Tuple="<pad>"					,   __a   :       Union[str, Any]="<mask>"					,   __a   :       Any=False					,   __a   :       Optional[int]=True					,   **__a   :       List[str]					,   )		->				str:
        super().__init__(
            __A					,   __A					,   tokenizer_file=__A					,   errors=__A					,   bos_token=__A					,   eos_token=__A					,   sep_token=__A					,   cls_token=__A					,   unk_token=__A					,   pad_token=__A					,   mask_token=__A					,   add_prefix_space=__A					,   trim_offsets=__A					,   **__A					,   )

        _UpperCamelCase      :	Any						=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"					,   __A	) != add_prefix_space:
              _UpperCamelCase      :	Optional[Any]						=       getattr(__A					,   pre_tok_state.pop("type"	)	)
              _UpperCamelCase      :	List[Any]						=       add_prefix_space
              _UpperCamelCase      :	List[Any]						=       pre_tok_class(**__A	)

        _UpperCamelCase      :	int						=       add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        _UpperCamelCase      :	Optional[int]						=       "post_processor"
        _UpperCamelCase      :	Union[str, Any]						=       getattr(self.backend_tokenizer					,   __A					,   __A	)
        if tokenizer_component_instance:
              _UpperCamelCase      :	Tuple						=       json.loads(tokenizer_component_instance.__getstate__()	)

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                    _UpperCamelCase      :	Dict						=       tuple(state["sep"]	)
              if "cls" in state:
                    _UpperCamelCase      :	List[str]						=       tuple(state["cls"]	)

              _UpperCamelCase      :	Union[str, Any]						=       False

              if state.get("add_prefix_space"					,   __A	) != add_prefix_space:
                    _UpperCamelCase      :	str						=       add_prefix_space
                    _UpperCamelCase      :	Optional[Any]						=       True

              if state.get("trim_offsets"					,   __A	) != trim_offsets:
                    _UpperCamelCase      :	Union[str, Any]						=       trim_offsets
                    _UpperCamelCase      :	List[str]						=       True

              if changes_to_apply:
                    _UpperCamelCase      :	str						=       getattr(__A					,   state.pop("type"	)	)
                    _UpperCamelCase      :	List[Any]						=       component_class(**__A	)
                    setattr(self.backend_tokenizer					,   __A					,   __A	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:



        if self._mask_token is None:
              if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."	)
              return None
        return str(self._mask_token	)

  @mask_token.setter
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       List[str]	)		->				str:
        _UpperCamelCase      :	Tuple						=       AddedToken(__A					,   lstrip=__A					,   rstrip=__A	) if isinstance(__A					,   __A	) else value
        _UpperCamelCase      :	str						=       value

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   *__a   :       Any					,   **__a   :       Tuple	)		->				BatchEncoding:
        _UpperCamelCase      :	Dict						=       kwargs.get("is_split_into_words"					,   __A	)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                  "to use it with pretokenized inputs."	)

        return super()._batch_encode_plus(*__A					,   **__A	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   *__a   :       List[Any]					,   **__a   :       str	)		->				BatchEncoding:
        _UpperCamelCase      :	Union[str, Any]						=       kwargs.get("is_split_into_words"					,   __A	)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                  "to use it with pretokenized inputs."	)

        return super()._encode_plus(*__A					,   **__A	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				Tuple[str]:
        _UpperCamelCase      :	List[Any]						=       self._tokenizer.model.save(__A					,   name=__A	)
        return tuple(__A	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Dict					,   __a   :       int=None	)		->				Any:
        _UpperCamelCase      :	Optional[int]						=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
              return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
        _UpperCamelCase      :	Any						=       [self.sep_token_id]
        _UpperCamelCase      :	List[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 + sep + token_ids_a + sep	) * [0]







"""simple docstring"""


from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__    (					lowercase_ = 1_000_000		,lowercase_ = 10			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	defaultdict						=       defaultdict(lowercase_			)

      for outer_width in range(3		,(t_limit // 4) + 2			):
            if outer_width * outer_width > t_limit:
                  _UpperCamelCase      :	Any						=       max(
                      ceil(sqrt(outer_width * outer_width - t_limit			)			)		,1			)
            else:
                  _UpperCamelCase      :	str						=       1

            hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

            for hole_width in range(lowercase_		,outer_width - 1		,2			):
                  count[outer_width * outer_width - hole_width * hole_width] += 1

      return sum(1 for n in count.values() if 1 <= n <= 10			)


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