






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__magic_name__:	Dict							=      logging.get_logger(__name__)
__magic_name__:	Tuple							=      {"vocab_file": "spiece.model"}

__magic_name__:	int							=      {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

__magic_name__:	Union[str, Any]							=      {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

__magic_name__:	Dict							=      "▁"





class 			snake_case__   (				_SCREAMING_SNAKE_CASE ):
	lowercase__		:	Any				 =       VOCAB_FILES_NAMES
	lowercase__		:	str				 =       PRETRAINED_VOCAB_FILES_MAP
	lowercase__		:	int				 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	def __init__(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ,  lowerCAmelCase__="[CLS]"     ,  lowerCAmelCase__="[SEP]"     ,  lowerCAmelCase__="<unk>"     ,  lowerCAmelCase__="[SEP]"     ,  lowerCAmelCase__="<pad>"     ,  lowerCAmelCase__="[CLS]"     ,  lowerCAmelCase__="[MASK]"     ,  lowerCAmelCase__ = None     ,  **lowerCAmelCase__     ,  )				-> Any:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							__magic_name__     :  Tuple  		=   (
							    AddedToken(lowerCAmelCase_     ,  lstrip=lowerCAmelCase_     ,  rstrip=lowerCAmelCase_     ,  normalized=lowerCAmelCase_					)
							    if isinstance(lowerCAmelCase_     ,  lowerCAmelCase_					)
							    else mask_token
							)

							__magic_name__     :  List[str]  		=   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=lowerCAmelCase_     ,  remove_space=lowerCAmelCase_     ,  keep_accents=lowerCAmelCase_     ,  bos_token=lowerCAmelCase_     ,  eos_token=lowerCAmelCase_     ,  unk_token=lowerCAmelCase_     ,  sep_token=lowerCAmelCase_     ,  pad_token=lowerCAmelCase_     ,  cls_token=lowerCAmelCase_     ,  mask_token=lowerCAmelCase_     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **lowerCAmelCase_     ,  )

							__magic_name__     :  Optional[Any]  		=   do_lower_case
							__magic_name__     :  Optional[Any]  		=   remove_space
							__magic_name__     :  int  		=   keep_accents
							__magic_name__     :  Tuple  		=   vocab_file

							__magic_name__     :  Any  		=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(lowerCAmelCase_					)
	@property
	def      __magic_name__							(							self					)				-> Any:
							return len(self.sp_model					)
	def      __magic_name__							(							self					)				-> str:
							__magic_name__     :  Optional[int]  		=   {self.convert_ids_to_tokens(lowerCAmelCase_					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab
	def __getstate__(							self					)				-> Any:
							__magic_name__     :  Tuple  		=   self.__dict__.copy()
							__magic_name__     :  Optional[int]  		=   None
							return state
	def __setstate__(							self     ,  lowerCAmelCase__					)				-> List[Any]:
							__magic_name__     :  Any  		=   d

							# for backward compatibility
							if not hasattr(self     ,  """sp_model_kwargs"""					):
													__magic_name__     :  Any  		=   {}

							__magic_name__     :  List[str]  		=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> Optional[int]:
							if self.remove_space:
													__magic_name__     :  Optional[int]  		=   """ """.join(inputs.strip().split()					)
							else:
													__magic_name__     :  List[str]  		=   inputs
							__magic_name__     :  Optional[Any]  		=   outputs.replace("""``"""     ,  """\""""					).replace("""''"""     ,  """\""""					)

							if not self.keep_accents:
													__magic_name__     :  List[Any]  		=   unicodedata.normalize("""NFKD"""     ,  lowerCAmelCase_					)
													__magic_name__     :  Optional[int]  		=   """""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase_					)]					)
							if self.do_lower_case:
													__magic_name__     :  List[str]  		=   outputs.lower()

							return outputs
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> List[str]:
							__magic_name__     :  Optional[Any]  		=   self.preprocess_text(lowerCAmelCase_					)
							__magic_name__     :  Dict  		=   self.sp_model.encode(lowerCAmelCase_     ,  out_type=lowerCAmelCase_					)
							__magic_name__     :  Any  		=   []
							for piece in pieces:
													if len(lowerCAmelCase_					) > 1 and piece[-1] == str(""","""					) and piece[-2].isdigit():
																			__magic_name__     :  Optional[int]  		=   self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase_     ,  """"""					)					)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																									if len(cur_pieces[0]					) == 1:
																															__magic_name__     :  Any  		=   cur_pieces[1:]
																									else:
																															__magic_name__     :  Tuple  		=   cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]					)
																			new_pieces.extend(lowerCAmelCase_					)
													else:
																			new_pieces.append(lowerCAmelCase_					)

							return new_pieces
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> int:
							return self.sp_model.PieceToId(lowerCAmelCase_					)
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> str:
							return self.sp_model.IdToPiece(lowerCAmelCase_					)
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> Optional[Any]:
							__magic_name__     :  str  		=   []
							__magic_name__     :  Union[str, Any]  		=   """"""
							__magic_name__     :  Union[str, Any]  		=   False
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			if not prev_is_special:
																									out_string += " "
																			out_string += self.sp_model.decode(lowerCAmelCase_					) + token
																			__magic_name__     :  str  		=   True
																			__magic_name__     :  Tuple  		=   []
													else:
																			current_sub_tokens.append(lowerCAmelCase_					)
																			__magic_name__     :  Union[str, Any]  		=   False
							out_string += self.sp_model.decode(lowerCAmelCase_					)
							return out_string.strip()
	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None					)				-> Optional[Any]:
							__magic_name__     :  Optional[Any]  		=   [self.sep_token_id]
							__magic_name__     :  Dict  		=   [self.cls_token_id]
							if token_ids_a is None:
													return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep
	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = False					)				-> Union[str, Any]:

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCAmelCase_     ,  token_ids_a=lowerCAmelCase_     ,  already_has_special_tokens=lowerCAmelCase_					)

							if token_ids_a is not None:
													return [1] + ([0] * len(lowerCAmelCase_					)) + [1] + ([0] * len(lowerCAmelCase_					)) + [1]
							return [1] + ([0] * len(lowerCAmelCase_					)) + [1]
	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None					)				-> Union[str, Any]:
							__magic_name__     :  List[str]  		=   [self.sep_token_id]
							__magic_name__     :  Optional[Any]  		=   [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					) * [0] + len(token_ids_a + sep					) * [1]







	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None					)				-> str:
							if not os.path.isdir(lowerCAmelCase_					):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
													return
							__magic_name__     :  Optional[Any]  		=   os.path.join(
							    lowerCAmelCase_     ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					) and os.path.isfile(self.vocab_file					):
													copyfile(self.vocab_file     ,  lowerCAmelCase_					)
							elif not os.path.isfile(self.vocab_file					):
													with open(lowerCAmelCase_     ,  """wb"""					) as fi:
																			__magic_name__     :  List[str]  		=   self.sp_model.serialized_model_proto()
																			fi.write(lowerCAmelCase_					)

							return (out_vocab_file,)








"""simple docstring"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 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(200) == 73_682

