



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

import sentencepiece as spm

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


lowerCAmelCase__		:					str							=logging.get_logger(__name__)

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

lowerCAmelCase__		:					Dict							={'''vocab_file''': '''sentencepiece.bpe.model'''}

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'''
        ),
    }
}

lowerCAmelCase__		:					Dict							={
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
lowerCAmelCase__		:					Optional[Any]							=['''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        UpperCAmelCase_  (       UpperCamelCase__	):

      '''simple docstring'''




      UpperCamelCase__      :			List[Any]      =  VOCAB_FILES_NAMES
      UpperCamelCase__      :			Union[str, Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__      :			Union[str, Any]      =  PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__      :			Optional[int]      =  ['''input_ids''', '''attention_mask''']

      UpperCamelCase__      :			List[int]      =  []
      UpperCamelCase__      :			List[int]      =  []






      def __init__(   self				,							_A				,							_A="<s>"				,							_A="</s>"				,							_A="</s>"				,							_A="<s>"				,							_A="<unk>"				,							_A="<pad>"				,							_A="<mask>"				,							_A=None				,							_A=None				,							_A=None				,							_A = None				,							_A=None				,							**_A				,							):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= AddedToken(UpperCamelCase_				,							lstrip=UpperCamelCase_				,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_				,							UpperCamelCase_		) else mask_token

             __SCREAMING_SNAKE_CASE									= {} 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_				,							tokenizer_file=UpperCamelCase_				,							src_lang=UpperCamelCase_				,							tgt_lang=UpperCamelCase_				,							additional_special_tokens=UpperCamelCase_				,							sp_model_kwargs=self.sp_model_kwargs				,							**UpperCamelCase_				,							)

             __SCREAMING_SNAKE_CASE									= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
             self.sp_model.Load(str(UpperCamelCase_		)		)
             __SCREAMING_SNAKE_CASE									= 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
             __SCREAMING_SNAKE_CASE									= {'<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
             __SCREAMING_SNAKE_CASE									= 1

             __SCREAMING_SNAKE_CASE									= len(self.sp_model		)
             __SCREAMING_SNAKE_CASE									= {
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_		)
             }
             __SCREAMING_SNAKE_CASE									= {v: k for k, v in self.lang_code_to_id.items()}
             __SCREAMING_SNAKE_CASE									= len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
             __SCREAMING_SNAKE_CASE									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
             __SCREAMING_SNAKE_CASE									= list(self.lang_code_to_id.keys()		)

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

             __SCREAMING_SNAKE_CASE									= src_lang if src_lang is not None else 'en_XX'
             __SCREAMING_SNAKE_CASE									= self.lang_code_to_id[self._src_lang]
             __SCREAMING_SNAKE_CASE									= tgt_lang
             self.set_src_lang_special_tokens(self._src_lang		)






      def __getstate__(   self		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= self.__dict__.copy()
             __SCREAMING_SNAKE_CASE									= None
             __SCREAMING_SNAKE_CASE									= self.sp_model.serialized_model_proto()
             return state






      def __setstate__(   self				,							_A		):







             '''simple docstring'''

             __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.LoadFromSerializedProto(self.sp_model_proto		)






      @property
      def 	_A     (   self		):







             '''simple docstring'''

             return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token






      @property
      def 	_A     (   self		):







             '''simple docstring'''

             return self._src_lang






      @src_lang.setter
      def 	_A     (   self				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= new_src_lang
             self.set_src_lang_special_tokens(self._src_lang		)






      def 	_A     (   self				,							_A				,							_A = None				,							_A = False		):







             '''simple docstring'''

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

             __SCREAMING_SNAKE_CASE									= [1] * len(self.prefix_tokens		)
             __SCREAMING_SNAKE_CASE									= [1] * len(self.suffix_tokens		)
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(UpperCamelCase_		)) + suffix_ones
             return prefix_ones + ([0] * len(UpperCamelCase_		)) + ([0] * len(UpperCamelCase_		)) + suffix_ones






      def 	_A     (   self				,							_A				,							_A = None		):







             '''simple docstring'''

             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 	_A     (   self				,							_A				,							_A = None		):







             '''simple docstring'''

             __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]






      def 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							**_A		):







             '''simple docstring'''

             if src_lang is None or tgt_lang is None:
                    raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
             __SCREAMING_SNAKE_CASE									= src_lang
             __SCREAMING_SNAKE_CASE									= self(UpperCamelCase_				,							add_special_tokens=UpperCamelCase_				,							return_tensors=UpperCamelCase_				,							**UpperCamelCase_		)
             __SCREAMING_SNAKE_CASE									= self.convert_tokens_to_ids(UpperCamelCase_		)
             __SCREAMING_SNAKE_CASE									= tgt_lang_id
             return inputs






      def 	_A     (   self		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= {self.convert_ids_to_tokens(UpperCamelCase_		): i for i in range(self.vocab_size		)}
             vocab.update(self.added_tokens_encoder		)
             return vocab






      def 	_A     (   self				,							_A		):







             '''simple docstring'''

             return self.sp_model.encode(UpperCamelCase_				,							out_type=UpperCamelCase_		)






      def 	_A     (   self				,							_A		):







             '''simple docstring'''

             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             __SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A		):







             '''simple docstring'''

             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 	_A     (   self				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= ''.join(UpperCamelCase_		).replace(UpperCamelCase_				,							' '		).strip()
             return out_string






      def 	_A     (   self				,							_A				,							_A = None		):







             '''simple docstring'''

             if not os.path.isdir(UpperCamelCase_		):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
                    return
             __SCREAMING_SNAKE_CASE									= 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:
                           __SCREAMING_SNAKE_CASE									= self.sp_model.serialized_model_proto()
                           fi.write(UpperCamelCase_		)

             return (out_vocab_file,)






      def 	_A     (   self				,							_A				,							_A = "en_XX"				,							_A = None				,							_A = "ro_RO"				,							**_A				,							):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= src_lang
             __SCREAMING_SNAKE_CASE									= tgt_lang
             return super().prepare_seqaseq_batch(UpperCamelCase_				,							UpperCamelCase_				,							**UpperCamelCase_		)






      def 	_A     (   self		):







             '''simple docstring'''

             return self.set_src_lang_special_tokens(self.src_lang		)






      def 	_A     (   self		):







             '''simple docstring'''

             return self.set_tgt_lang_special_tokens(self.tgt_lang		)






      def 	_A     (   self				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= self.lang_code_to_id[src_lang]
             __SCREAMING_SNAKE_CASE									= []
             __SCREAMING_SNAKE_CASE									= [self.eos_token_id, self.cur_lang_code]






      def 	_A     (   self				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= self.lang_code_to_id[lang]
             __SCREAMING_SNAKE_CASE									= []
             __SCREAMING_SNAKE_CASE									= [self.eos_token_id, self.cur_lang_code]











def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= [[0 for _ in range(a__       )] for _ in range(m + 1       )]
							for i in range(m + 1       ):
														__SCREAMING_SNAKE_CASE									= 1

							for n in range(m + 1       ):
														for k in range(1							,       a__       ):
																					memo[n][k] += memo[n][k - 1]
																					if n - k > 0:
																												memo[n][k] += memo[n - k - 1][k]

							return memo[m][m - 1]


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						try:
									lowerCAmelCase__		:					Optional[Any]							=int(input('''Enter a number: ''').strip())
									print(partition(n))
						except ValueError:
									print('''Please enter a number.''')
			else:
						try:
									lowerCAmelCase__		:					str							=int(sys.argv[1])
									print(partition(n))
						except ValueError:
									print('''Please pass a number.''')






