


import os
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


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

snake_case      :   List[Any]							= {'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case      :   str							= {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

snake_case      :   Union[str, Any]							= {
    '''camembert-base''': 5_12,
}

snake_case      :   Optional[Any]							= '''▁'''



class  __lowercase	(							UpperCAmelCase_				):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE :       Union[str, Any]	   =    VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE :       Any	   =    PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE :       Union[str, Any]	   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE :       Tuple	   =    ["input_ids", "attention_mask"]





							def __init__(     self					,			A_					,			A_="<s>"					,			A_="</s>"					,			A_="</s>"					,			A_="<s>"					,			A_="<unk>"					,			A_="<pad>"					,			A_="<mask>"					,			A_=["<s>NOTUSED", "</s>NOTUSED"]					,			A_ = None					,			**A_					,			)->						None:
											_SCREAMING_SNAKE_CASE   =			AddedToken(__lowerCamelCase					,			lstrip=__lowerCamelCase					,			rstrip=__lowerCamelCase						) if isinstance(__lowerCamelCase					,			__lowerCamelCase						) else mask_token

											_SCREAMING_SNAKE_CASE   =			{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=__lowerCamelCase					,			eos_token=__lowerCamelCase					,			unk_token=__lowerCamelCase					,			sep_token=__lowerCamelCase					,			cls_token=__lowerCamelCase					,			pad_token=__lowerCamelCase					,			mask_token=__lowerCamelCase					,			additional_special_tokens=__lowerCamelCase					,			sp_model_kwargs=self.sp_model_kwargs					,			**__lowerCamelCase					,			)
											_SCREAMING_SNAKE_CASE   =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(str(__lowerCamelCase						)						)
											_SCREAMING_SNAKE_CASE   =			vocab_file
											# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
											# sentencepiece vocabulary (this is the case for <s> and </s>
											_SCREAMING_SNAKE_CASE   =			{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
											_SCREAMING_SNAKE_CASE   =			len(self.fairseq_tokens_to_ids						)
											_SCREAMING_SNAKE_CASE   =			len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
											_SCREAMING_SNAKE_CASE   =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}





							def 		__magic_name__      (     self					,			A_					,			A_ = None						)->						List[int]:




											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE   =			[self.cls_token_id]
											_SCREAMING_SNAKE_CASE   =			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





							def 		__magic_name__      (     self					,			A_					,			A_ = None					,			A_ = False						)->						List[int]:




											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 None:
															return [1] + ([0] * len(__lowerCamelCase						)) + [1]
											return [1] + ([0] * len(__lowerCamelCase						)) + [1, 1] + ([0] * len(__lowerCamelCase						)) + [1]





							def 		__magic_name__      (     self					,			A_					,			A_ = None						)->						List[int]:
											_SCREAMING_SNAKE_CASE   =			[self.sep_token_id]
											_SCREAMING_SNAKE_CASE   =			[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 + sep + token_ids_a + sep						) * [0]





							@property
							def 		__magic_name__      (     self						)->						Union[str, Any]:
											return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)





							def 		__magic_name__      (     self						)->						List[str]:
											_SCREAMING_SNAKE_CASE   =			{self.convert_ids_to_tokens(__lowerCamelCase						): i for i in range(self.vocab_size						)}
											vocab.update(self.added_tokens_encoder						)
											return vocab





							def 		__magic_name__      (     self					,			A_						)->						List[str]:
											return self.sp_model.encode(__lowerCamelCase					,			out_type=__lowerCamelCase						)





							def 		__magic_name__      (     self					,			A_						)->						Tuple:




											if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
											elif self.sp_model.PieceToId(__lowerCamelCase						) == 0:
															# Convert sentence piece unk token to fairseq unk token index
															return self.unk_token_id
											return self.fairseq_offset + self.sp_model.PieceToId(__lowerCamelCase						)





							def 		__magic_name__      (     self					,			A_						)->						Optional[Any]:




											if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset						)





							def 		__magic_name__      (     self					,			A_						)->						Tuple:
											_SCREAMING_SNAKE_CASE   =			[]
											_SCREAMING_SNAKE_CASE   =			''
											_SCREAMING_SNAKE_CASE   =			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
																			_SCREAMING_SNAKE_CASE   =			True
																			_SCREAMING_SNAKE_CASE   =			[]
															else:
																			current_sub_tokens.append(__lowerCamelCase						)
																			_SCREAMING_SNAKE_CASE   =			False
											out_string += self.sp_model.decode(__lowerCamelCase						)
											return out_string.strip()





							def __getstate__(     self						)->						Tuple:
											_SCREAMING_SNAKE_CASE   =			self.__dict__.copy()
											_SCREAMING_SNAKE_CASE   =			None
											return state





							def __setstate__(     self					,			A_						)->						List[Any]:
											_SCREAMING_SNAKE_CASE   =			d

											# for backward compatibility
											if not hasattr(self					,			'sp_model_kwargs'						):
															_SCREAMING_SNAKE_CASE   =			{}

											_SCREAMING_SNAKE_CASE   =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(self.vocab_file						)





							def 		__magic_name__      (     self					,			A_					,			A_ = None						)->						Tuple[str]:




											if not os.path.isdir(__lowerCamelCase						):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
															return
											_SCREAMING_SNAKE_CASE   =			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:
																			_SCREAMING_SNAKE_CASE   =			self.sp_model.serialized_model_proto()
																			fi.write(__lowerCamelCase						)

											return (out_vocab_file,)







def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




