'''simple docstring'''

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


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						List[Any]    							={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_     :						Any    							=[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

A_     :						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'''
        ),
    },
}
A_     :						Dict    							={f'''funnel-transformer/{name}''': 5_12 for name in _model_names}
A_     :						Union[str, Any]    							={f'''funnel-transformer/{name}''': {'''do_lower_case''': True} for name in _model_names}


class     __UpperCAmelCase       ( __a		):
  __A :				Optional[Any]											= VOCAB_FILES_NAMES
  __A :				Optional[int]											= PRETRAINED_VOCAB_FILES_MAP
  __A :				Optional[int]											= PRETRAINED_INIT_CONFIGURATION
  __A :				str											= FunnelTokenizer
  __A :				Tuple											= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				int											= 2





  def __init__(	self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase="<unk>"    ,     _lowerCamelCase="<sep>"    ,     _lowerCamelCase="<pad>"    ,     _lowerCamelCase="<cls>"    ,     _lowerCamelCase="<mask>"    ,     _lowerCamelCase="<s>"    ,     _lowerCamelCase="</s>"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase="##"    ,     **_lowerCamelCase    ,     ):
     super().__init__(
         _lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     do_lower_case=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     sep_token=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     cls_token=_lowerCamelCase    ,     mask_token=_lowerCamelCase    ,     bos_token=_lowerCamelCase    ,     eos_token=_lowerCamelCase    ,     clean_text=_lowerCamelCase    ,     tokenize_chinese_chars=_lowerCamelCase    ,     strip_accents=_lowerCamelCase    ,     wordpieces_prefix=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

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

     lowerCAmelCase_			=      do_lower_case





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=None ):
     lowerCAmelCase_			=      [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    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.sep_token_id]
     lowerCAmelCase_			=      [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    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      self._tokenizer.model.save(_lowerCamelCase    ,     name=_lowerCamelCase )
     return tuple(_lowerCamelCase )






'''simple docstring'''

def snake_case_      (					__snake_case  :							int = 200)			->					int:
   lowerCAmelCase_			=      [1, 2, 5, 10, 20, 50, 100, 200]
   lowerCAmelCase_			=      [0] * (pence + 1)
   lowerCAmelCase_			=      1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(__snake_case	,   pence + 1	,   1):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(2_00) == 7_36_82





