


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


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

_lowercase       :      List[str]	=	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase       :      Tuple	=	[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_lowercase       :      List[Any]	=	{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_lowercase       :      Any	=	{f'''funnel-transformer/{name}''': 512 for name in _model_names}
_lowercase       :      Optional[Any]	=	{f'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}






class       _UpperCamelCase    (					__snake_case				):

    """simple docstring"""







    lowerCAmelCase 		= VOCAB_FILES_NAMES
    lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase 		= PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase 		= FunnelTokenizer
    lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase 		= 2

    def __init__(						self				,   a__=None				,   a__=None				,   a__=True				,   a__="<unk>"				,   a__="<sep>"				,   a__="<pad>"				,   a__="<cls>"				,   a__="<mask>"				,   a__="<s>"				,   a__="</s>"				,   a__=True				,   a__=True				,   a__=None				,   a__="##"				,   **a__				,   )	->				Any:
     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__				,   bos_token=a__				,   eos_token=a__				,   clean_text=a__				,   tokenize_chinese_chars=a__				,   strip_accents=a__				,   wordpieces_prefix=a__				,   **a__				,   )

     A								=				json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
     if (
         normalizer_state.get("""lowercase"""				,   a__   ) != do_lower_case
         or normalizer_state.get("""strip_accents"""				,   a__   ) != strip_accents
         or normalizer_state.get("""handle_chinese_chars"""				,   a__   ) != tokenize_chinese_chars
     ):
      A								=				getattr(a__				,   normalizer_state.pop("""type"""   )   )
      A								=				do_lower_case
      A								=				strip_accents
      A								=				tokenize_chinese_chars
      A								=				normalizer_class(**a__   )

     A								=				do_lower_case

    def      _UpperCAmelCase					(						self				,   a__				,   a__=None   )	->				Optional[Any]:
     A								=				[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      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				List[int]:
     A								=				[self.sep_token_id]
     A								=				[self.cls_token_id]
     if token_ids_a is None:
      return len(cls   ) * [self.cls_token_type_id] + len(token_ids_a + sep   ) * [0]
     return len(cls   ) * [self.cls_token_type_id] + len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]

    def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
     A								=				self._tokenizer.model.save(a__				,   name=a__   )
     return tuple(a__   )







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   list[int]:





	"""simple docstring"""







	if num <= 0:
		raise ValueError("""Input must be a positive integer"""						)

	A								=				[True] * (num + 1)

	A								=				2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p							,					num + 1							,					UpperCamelCase__						):
				A								=				False
		p += 1

	return [prime for prime in range(2							,					num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase       :      Tuple	=	int(input("Enter a positive integer: ").strip())
				print(prime_sieve_eratosthenes(user_num))



