

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       snake_case_        =			None


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
snake_case_        =			{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}


class 				SCREAMING_SNAKE_CASE__ (a_					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Optional[int]														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Optional[Any]														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	Dict														=					['''input_ids''', '''attention_mask''']
    __lowerCamelCase						:	List[Any]														=					TaTokenizer

    __lowerCamelCase						:	List[int]														=					[]





    def __init__(				self						,       a=None						,       a=None						,       a="</s>"						,       a="<unk>"						,       a="<pad>"						,       a=100						,       a=None						,       **a						,       ):

         if extra_ids > 0 and additional_special_tokens is None:
              lowercase__   :       int				=	[f"""<extra_id_{i}>""" for i in range(lowercase_)]
         elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra special tokens
              lowercase__   :       Dict				=	len(set(filter(lambda a: bool('extra_id_' in str(lowercase_))						,       lowercase_)))
              if extra_tokens != extra_ids:
                   raise ValueError(
                       f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                       ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                       ' tokens')

         super().__init__(
             lowercase_						,       tokenizer_file=lowercase_						,       eos_token=lowercase_						,       unk_token=lowercase_						,       pad_token=lowercase_						,       extra_ids=lowercase_						,       additional_special_tokens=lowercase_						,       **lowercase_						,       )

         lowercase__   :       List[str]				=	vocab_file
         lowercase__   :       Dict				=	False if not self.vocab_file else True
         lowercase__   :       List[str]				=	extra_ids





    @staticmethod
    def 				snake_case_    (				a						,       a						,       a):

         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
              lowercase__   :       Optional[Any]				=	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
              if init_max_model_length is not None and init_max_model_length != max_model_length:
                   return init_max_model_length
              elif init_max_model_length is None:
                   warnings.warn(
                       'This tokenizer was incorrectly instantiated with a model max length of'
                       f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                       ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                       ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                       f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                       f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                       f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                       ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                       ' instantiate this tokenizer with `model_max_length` set to your preferred value.'						,       lowercase_						,       )

         return max_model_length





    def 				snake_case_    (				self						,       a						,       a = None):

         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                  'tokenizer.')

         if not os.path.isdir(lowercase_):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__   :       List[str]				=	os.path.join(
             lowercase_						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

         if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
              copyfile(self.vocab_file						,       lowercase_)
              logger.info(f"""Copy vocab file to {out_vocab_file}""")

         return (out_vocab_file,)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       List[str]				=	token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a
         else:
              lowercase__   :       Optional[int]				=	token_ids_a + [self.eos_token_id]
              return self.prefix_tokens + token_ids_a + token_ids_a





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.eos_token_id]

         if token_ids_a is None:
              return len(token_ids_a + eos) * [0]
         return len(token_ids_a + eos + token_ids_a + eos) * [0]





    def 				snake_case_    (				self):
         return list(
             set(filter(lambda a: bool(re.search(r'<extra_id_\d+>'						,       lowercase_)) is not None						,       self.additional_special_tokens)))





    def 				snake_case_    (				self):
         return [self.convert_tokens_to_ids(lowercase_) for token in self.get_sentinel_tokens()]




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       snake_case_        =			int(input('''Enter number: ''').strip())
       print(
           F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
       )

