




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		__lowerCAmelCase		:				List[str]       			=		None


__lowerCAmelCase		:				Tuple       			=		logging.get_logger(__name__)


__lowerCAmelCase		:				Tuple       			=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase		:				List[str]       			=		{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase		:				str       			=		{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__lowerCAmelCase		:				int       			=		['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							Dict							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					MBartTokenizer

				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=None					,      __lowerCamelCase				:     Union[str, Any]=None					,      __lowerCamelCase				:     List[Any]="<s>"					,      __lowerCamelCase				:     List[Any]="</s>"					,      __lowerCamelCase				:     Tuple="</s>"					,      __lowerCamelCase				:     Optional[int]="<s>"					,      __lowerCamelCase				:     str="<unk>"					,      __lowerCamelCase				:     Union[str, Any]="<pad>"					,      __lowerCamelCase				:     List[str]="<mask>"					,      __lowerCamelCase				:     Dict=None					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     Optional[int]=None					,      **__lowerCamelCase				:     Dict					,      )					->     str:
							# Mask token behave like a normal word, i.e. include the space before it
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else mask_token

							super().__init__(
							    vocab_file=__lowerCamelCase					,      tokenizer_file=__lowerCamelCase					,      bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      src_lang=__lowerCamelCase					,      tgt_lang=__lowerCamelCase					,      additional_special_tokens=__lowerCamelCase					,      **__lowerCamelCase					,      )

							a    =			vocab_file
							a    =			False if not self.vocab_file else True

							a    =			FAIRSEQ_LANGUAGE_CODES.copy()

							if additional_special_tokens is not None:
										# Only add those special tokens if they are not already there.
										_additional_special_tokens.extend(
										    [t for t in additional_special_tokens if t not in _additional_special_tokens]							)

							self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}							)
							a    =			{
							    lang_code: self.convert_tokens_to_ids(__lowerCamelCase							) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							a    =			src_lang if src_lang is not None else "en_XX"
							a    =			self.convert_tokens_to_ids(self._src_lang							)
							a    =			tgt_lang
							self.set_src_lang_special_tokens(self._src_lang							)





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     str:
							return self._src_lang





				@src_lang.setter
				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     str							)					->     None:
							a    =			new_src_lang
							self.set_src_lang_special_tokens(self._src_lang							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							if token_ids_a is None:
										return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							a    =			[self.sep_token_id]
							a    =			[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]





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str]					,      __lowerCamelCase				:     Optional[str]					,      **__lowerCamelCase				:     List[Any]							)					->     Tuple:
							if src_lang is None or tgt_lang is None:
										raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"							)
							a    =			src_lang
							a    =			self(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase							)
							a    =			self.convert_tokens_to_ids(__lowerCamelCase							)
							a    =			tgt_lang_id
							return inputs





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     str = "en_XX"					,      __lowerCamelCase				:     Optional[List[str]] = None					,      __lowerCamelCase				:     str = "ro_RO"					,      **__lowerCamelCase				:     str					,      )					->     BatchEncoding:
							a    =			src_lang
							a    =			tgt_lang
							return super().prepare_seqaseq_batch(__lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Dict:
							return self.set_src_lang_special_tokens(self.src_lang							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Tuple:
							return self.set_tgt_lang_special_tokens(self.tgt_lang							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     List[str]							)					->     None:
							a    =			self.convert_tokens_to_ids(__lowerCamelCase							)
							a    =			[]
							a    =			[self.eos_token_id, self.cur_lang_code]

							a    =			self.convert_ids_to_tokens(self.prefix_tokens							)
							a    =			self.convert_ids_to_tokens(self.suffix_tokens							)

							a    =			processors.TemplateProcessing(
							    single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,      self.prefix_tokens + self.suffix_tokens							)							)					,      )





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     str							)					->     None:
							a    =			self.convert_tokens_to_ids(__lowerCamelCase							)
							a    =			[]
							a    =			[self.eos_token_id, self.cur_lang_code]

							a    =			self.convert_ids_to_tokens(self.prefix_tokens							)
							a    =			self.convert_ids_to_tokens(self.suffix_tokens							)

							a    =			processors.TemplateProcessing(
							    single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,      self.prefix_tokens + self.suffix_tokens							)							)					,      )





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."							)

							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
										return
							a    =			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							):
										copyfile(self.vocab_file					,      __lowerCamelCase							)

							return (out_vocab_file,)













def      _A       (		SCREAMING_SNAKE_CASE     :   int = 50						):





  """simple docstring"""



  a__	:					Any										=[1] * (length + 1)

  for row_length in range(3       ,       length + 1						):
    for block_length in range(3       ,       row_length + 1						):
      for block_start in range(row_length - block_length						):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


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






