




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


UpperCAmelCase_		:					Union[str, Any]          =  logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_		:					Any          =  {
    "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",
    },
}

UpperCAmelCase_		:					List[str]          =  {
    "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 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]
						UpperCamelCase      =       BartTokenizer

						def __init__(		self :List[Any]     ,			__snake_case :str=None     ,			__snake_case :List[Any]=None     ,			__snake_case :Optional[int]=None     ,			__snake_case :Union[str, Any]="replace"     ,			__snake_case :List[str]="<s>"     ,			__snake_case :List[str]="</s>"     ,			__snake_case :Optional[Any]="</s>"     ,			__snake_case :List[str]="<s>"     ,			__snake_case :Optional[Any]="<unk>"     ,			__snake_case :Dict="<pad>"     ,			__snake_case :Union[str, Any]="<mask>"     ,			__snake_case :Optional[Any]=False     ,			__snake_case :int=True     ,			**__snake_case :str     ,			):
										'''simple docstring'''

										super().__init__(
										    __snake_case     ,			__snake_case     ,			tokenizer_file=__snake_case     ,			errors=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			sep_token=__snake_case     ,			cls_token=__snake_case     ,			unk_token=__snake_case     ,			pad_token=__snake_case     ,			mask_token=__snake_case     ,			add_prefix_space=__snake_case     ,			trim_offsets=__snake_case     ,			**__snake_case     ,			)

										__magic_name__						:						List[str]										=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
										if pre_tok_state.get("""add_prefix_space"""     ,			__snake_case			) != add_prefix_space:
														__magic_name__						:						Dict										=getattr(__snake_case     ,			pre_tok_state.pop("""type"""			)			)
														__magic_name__						:						List[Any]										=add_prefix_space
														__magic_name__						:						Any										=pre_tok_class(**__snake_case			)

										__magic_name__						:						Tuple										=add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										__magic_name__						:						List[str]										="""post_processor"""
										__magic_name__						:						Tuple										=getattr(self.backend_tokenizer     ,			__snake_case     ,			__snake_case			)
										if tokenizer_component_instance:
														__magic_name__						:						Optional[Any]										=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:
																		__magic_name__						:						int										=tuple(state["""sep"""]			)
														if "cls" in state:
																		__magic_name__						:						Tuple										=tuple(state["""cls"""]			)

														__magic_name__						:						Union[str, Any]										=False

														if state.get("""add_prefix_space"""     ,			__snake_case			) != add_prefix_space:
																		__magic_name__						:						Tuple										=add_prefix_space
																		__magic_name__						:						Union[str, Any]										=True

														if state.get("""trim_offsets"""     ,			__snake_case			) != trim_offsets:
																		__magic_name__						:						Dict										=trim_offsets
																		__magic_name__						:						Optional[Any]										=True

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

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										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      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else value
										__magic_name__						:						Dict										=value

						def      A__    (		self :Union[str, Any]     ,			*__snake_case :Tuple     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=kwargs.get("""is_split_into_words"""     ,			__snake_case			)

										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(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :str     ,			*__snake_case :Optional[Any]     ,			**__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=kwargs.get("""is_split_into_words"""     ,			__snake_case			)

										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(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :Optional[Any]     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

										__magic_name__						:						int										=self._tokenizer.model.save(__snake_case     ,			name=__snake_case			)
										return tuple(__snake_case			)

						def      A__    (		self :List[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										__magic_name__						:						Any										=[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      A__    (		self :List[Any]     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=[self.sep_token_id]
										__magic_name__						:						Optional[int]										=[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'''


import math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

