



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				=					logging.get_logger(__name__)

_snake_case				=					'▁'

_snake_case				=					{'vocab_file': 'sentencepiece.bpe.model'}

_snake_case				=					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_snake_case				=					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}






class 		lowerCAmelCase_ (			__snake_case ):







		"""simple docstring"""
		UpperCAmelCase__													=       VOCAB_FILES_NAMES
		UpperCAmelCase__													=       PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__													=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__													=       ["input_ids", "attention_mask"]
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE="<s>"     ,    _SCREAMING_SNAKE_CASE="</s>"     ,    _SCREAMING_SNAKE_CASE="</s>"     ,    _SCREAMING_SNAKE_CASE="<s>"     ,    _SCREAMING_SNAKE_CASE="<unk>"     ,    _SCREAMING_SNAKE_CASE="<pad>"     ,    _SCREAMING_SNAKE_CASE="<mask>"     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  List[str]:
									__UpperCamelCase          =  AddedToken(__UpperCamelCase     ,    lstrip=__UpperCamelCase     ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase     ,    __UpperCamelCase			) else mask_token

									__UpperCamelCase          =  {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=__UpperCamelCase     ,    eos_token=__UpperCamelCase     ,    unk_token=__UpperCamelCase     ,    sep_token=__UpperCamelCase     ,    cls_token=__UpperCamelCase     ,    pad_token=__UpperCamelCase     ,    mask_token=__UpperCamelCase     ,    sp_model_kwargs=self.sp_model_kwargs     ,    **__UpperCamelCase     ,    )

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

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__UpperCamelCase          =  {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__UpperCamelCase          =  1

									__UpperCamelCase          =  len(self.sp_model			) + self.fairseq_offset
									__UpperCamelCase          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
		def __getstate__(		self			)     ->  Tuple:
									__UpperCamelCase          =  self.__dict__.copy()
									__UpperCamelCase          =  None
									__UpperCamelCase          =  self.sp_model.serialized_model_proto()
									return state
		def __setstate__(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Tuple:
									__UpperCamelCase          =  d

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

									__UpperCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs			)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None			)     ->  Optional[Any]:


									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									__UpperCamelCase          =  [self.cls_token_id]
									__UpperCamelCase          =  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = False			)     ->  Dict:


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

									if token_ids_a is None:
																return [1] + ([0] * len(__UpperCamelCase			)) + [1]
									return [1] + ([0] * len(__UpperCamelCase			)) + [1, 1] + ([0] * len(__UpperCamelCase			)) + [1]
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None			)     ->  List[Any]:
									__UpperCamelCase          =  [self.sep_token_id]
									__UpperCamelCase          =  [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 						__lowercase(		self			)     ->  Union[str, Any]:
									return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token
		def 						__lowercase(		self			)     ->  Optional[Any]:
									__UpperCamelCase          =  {self.convert_ids_to_tokens(__UpperCamelCase			): i for i in range(self.vocab_size			)}
									vocab.update(self.added_tokens_encoder			)
									return vocab
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  List[str]:
									return self.sp_model.encode(__UpperCamelCase     ,    out_type=__UpperCamelCase			)
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Any:


									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									__UpperCamelCase          =  self.sp_model.PieceToId(__UpperCamelCase			)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  int:


									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 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Union[str, Any]:
									__UpperCamelCase          =  ''.join(__UpperCamelCase			).replace(__UpperCamelCase     ,    ' '			).strip()
									return out_string



		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None			)     ->  List[str]:


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

									if os.path.abspath(self.vocab_file			) != os.path.abspath(__UpperCamelCase			) and os.path.isfile(self.vocab_file			):
																copyfile(self.vocab_file     ,    __UpperCamelCase			)
									elif not os.path.isfile(self.vocab_file			):
																with open(__UpperCamelCase     ,    'wb'			) as fi:
																							__UpperCamelCase          =  self.sp_model.serialized_model_proto()
																							fi.write(__UpperCamelCase			)

									return (out_vocab_file,)






from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
