

"""simple docstring"""




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


_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Union[str, Any]						    =	'▁'

_UpperCAmelCase		:       str						    =	{'vocab_file': 'sentencepiece.bpe.model'}

_UpperCAmelCase		:       Tuple						    =	{
    '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'
        ),
    }
}

_UpperCAmelCase		:       str						    =	{
    '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 			lowercase    (	lowerCamelCase__      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE :			List[str]	      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE :			int	      =      ['''input_ids''', '''attention_mask''']




				def __init__( self				,       snake_case				,       snake_case="<s>"				,       snake_case="</s>"				,       snake_case="</s>"				,       snake_case="<s>"				,       snake_case="<unk>"				,       snake_case="<pad>"				,       snake_case="<mask>"				,       snake_case = None				,       **snake_case				,       ):
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_          =   AddedToken(__snake_case				,       lstrip=__snake_case				,       rstrip=__snake_case	) if isinstance(__snake_case				,       __snake_case	) else mask_token

											snake_case_          =   {} if sp_model_kwargs is None else sp_model_kwargs

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

											snake_case_          =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(str(__snake_case	)	)
											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
											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
											snake_case_          =   1

											snake_case_          =   len(self.sp_model	) + self.fairseq_offset
											snake_case_          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def __getstate__( self	):
											snake_case_          =   self.__dict__.copy()
											snake_case_          =   None
											snake_case_          =   self.sp_model.serialized_model_proto()
											return state




				def __setstate__( self				,       snake_case	):
											snake_case_          =   d

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

											snake_case_          =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)




				def    a							( self				,       snake_case				,       snake_case = None	):

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




				def    a							( self				,       snake_case				,       snake_case = None				,       snake_case = False	):

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

											if token_ids_a is None:
																		return [1] + ([0] * len(__snake_case	)) + [1]
											return [1] + ([0] * len(__snake_case	)) + [1, 1] + ([0] * len(__snake_case	)) + [1]




				def    a							( self				,       snake_case				,       snake_case = None	):
											snake_case_          =   [self.sep_token_id]
											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    a							( self	):
											return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token




				def    a							( self	):
											snake_case_          =   {self.convert_ids_to_tokens(__snake_case	): i for i in range(self.vocab_size	)}
											vocab.update(self.added_tokens_encoder	)
											return vocab




				def    a							( self				,       snake_case	):
											return self.sp_model.encode(__snake_case				,       out_type=__snake_case	)




				def    a							( self				,       snake_case	):
											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											snake_case_          =   self.sp_model.PieceToId(__snake_case	)

											# 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				,       snake_case	):
											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				,       snake_case	):
											snake_case_          =   ''.join(__snake_case	).replace(__snake_case				,       ' '	).strip()
											return out_string




				def    a							( self				,       snake_case				,       snake_case = None	):
											if not os.path.isdir(__snake_case	):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
																		return
											snake_case_          =   os.path.join(
											    __snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

											return (out_vocab_file,)







def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   len(UpperCamelCase__				)
							for _ in range(UpperCamelCase__				):
														for i in range(_ % 2       ,							arr_size - 1       ,							2				):
																					if arr[i + 1] < arr[i]:
																												snake_case_    ,   snake_case_          =   arr[i + 1], arr[i]

							return arr


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	list(range(10, 0, -1))
					print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')




