

import copy
import random

from transformers import CLIPTokenizer



class     _a						(__lowerCamelCase				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						super().__init__(*A__    ,       **A__			)
						A__							:    List[str]        =		{}






					def   __A			( self    ,       A__    ,       *A__    ,       **A__			):
						A__							:    List[Any]        =		super().add_tokens(A__    ,       *A__    ,       **A__			)
						if num_added_tokens == 0:
							raise ValueError(
							    F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
							    """ `placeholder_token` that is not already in the tokenizer."""			)






					def   __A			( self    ,       A__    ,       *A__    ,       A__=1    ,       **A__			):
						A__							:    Any        =		[]
						if num_vec_per_token == 1:
							self.try_adding_tokens(A__    ,       *A__    ,       **A__			)
							output.append(A__			)
						else:
							A__							:    Any        =		[]
							for i in range(A__			):
								A__							:    List[Any]        =		placeholder_token + F"""_{i}"""
								self.try_adding_tokens(A__    ,       *A__    ,       **A__			)
								output.append(A__			)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
						for token in self.token_map:
							if token in placeholder_token:
								raise ValueError(
								    F"""The tokenizer already has placeholder token {token} that can get confused with"""
								    F""" {placeholder_token}keep placeholder tokens independent"""			)
						A__							:    Optional[Any]        =		output






					def   __A			( self    ,       A__    ,       A__=False    ,       A__=1.0			):
						if isinstance(A__    ,       A__			):
							A__							:    Optional[int]        =		[]
							for i in range(len(A__			)			):
								output.append(self.replace_placeholder_tokens_in_text(text[i]    ,       vector_shuffle=A__			)			)
							return output
						for placeholder_token in self.token_map:
							if placeholder_token in text:
								A__							:    Optional[int]        =		self.token_map[placeholder_token]
								A__							:    Optional[int]        =		tokens[: 1 + int(len(A__			) * prop_tokens_to_load			)]
								if vector_shuffle:
									A__							:    int        =		copy.copy(A__			)
									random.shuffle(A__			)
								A__							:    Any        =		text.replace(A__    ,       """ """.join(A__			)			)
						return text






					def __call__( self    ,       A__    ,       *A__    ,       A__=False    ,       A__=1.0    ,       **A__			):
						return super().__call__(
						    self.replace_placeholder_tokens_in_text(
						        A__    ,       vector_shuffle=A__    ,       prop_tokens_to_load=A__			)    ,       *A__    ,       **A__    ,       )






					def   __A			( self    ,       A__    ,       *A__    ,       A__=False    ,       A__=1.0    ,       **A__			):
						return super().encode(
						    self.replace_placeholder_tokens_in_text(
						        A__    ,       vector_shuffle=A__    ,       prop_tokens_to_load=A__			)    ,       *A__    ,       **A__    ,       )










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

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


if __name__ == "__main__":
      assert solution(200) == 73_682






