



"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case   					=				{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case   					=				{
    '''gpt2''': 1_0_2_4,
    '''gpt2-medium''': 1_0_2_4,
    '''gpt2-large''': 1_0_2_4,
    '''gpt2-xl''': 1_0_2_4,
    '''distilgpt2''': 1_0_2_4,
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  VOCAB_FILES_NAMES
   A__		:	Any           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	Optional[Any]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Union[str, Any]           =  ['''input_ids''', '''attention_mask''']
   A__		:	Tuple           =  GPTaTokenizer



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	str=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	List[str]="<|endoftext|>"							,       __lowerCamelCase    :	Dict="<|endoftext|>"							,       __lowerCamelCase    :	Union[str, Any]="<|endoftext|>"							,       __lowerCamelCase    :	List[str]=False							,       **__lowerCamelCase    :	Any							,       ):



     """simple docstring"""
     super().__init__(
         __lowerCamelCase							,       __lowerCamelCase							,       tokenizer_file=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       add_prefix_space=__lowerCamelCase							,       **__lowerCamelCase							,       )

     _snake_case   	=      kwargs.pop('''add_bos_token'''							,       __lowerCamelCase				)

     _snake_case   	=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
     if pre_tok_state.get('''add_prefix_space'''							,       __lowerCamelCase				) != add_prefix_space:
       _snake_case   	=      getattr(__lowerCamelCase							,       pre_tok_state.pop('''type'''				)				)
       _snake_case   	=      add_prefix_space
       _snake_case   	=      pre_tok_class(**__lowerCamelCase				)

     _snake_case   	=      add_prefix_space



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       *__lowerCamelCase    :	Optional[int]							,       **__lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      kwargs.get('''is_split_into_words'''							,       __lowerCamelCase				)
     assert self.add_prefix_space or not is_split_into_words, (
         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

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



   def    __UpperCAmelCase    (       self    :	Tuple							,       *__lowerCamelCase    :	Any							,       **__lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      kwargs.get('''is_split_into_words'''							,       __lowerCamelCase				)

     assert self.add_prefix_space or not is_split_into_words, (
         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

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



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     _snake_case   	=      self._tokenizer.model.save(__lowerCamelCase							,       name=__lowerCamelCase				)
     return tuple(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	"Conversation"				):



     """simple docstring"""
     _snake_case   	=      []
     for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				) + [self.eos_token_id]				)

     if len(__lowerCamelCase				) > self.model_max_length:
       _snake_case   	=      input_ids[-self.model_max_length :]
     return input_ids










"""simple docstring"""






from math import sqrt




def 			snake_case		(		lowerCAmelCase_ = 1000000	)						->				int:
  _snake_case   	=      0
  _snake_case   	=      0
  _snake_case   	=      42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1	):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2	).is_integer():
        num_cuboids += (
            min(lowerCAmelCase_  ,						sum_shortest_sides // 2	)
            - max(1  ,						sum_shortest_sides - max_cuboid_size	)
            + 1
        )

  return max_cuboid_size


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





