





"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[str]						       =  '''▁'''

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

__UpperCamelCase     :						Optional[int]						       =  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__UpperCamelCase     :						Dict						       =  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
__UpperCamelCase     :						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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']







class       a							(   a__  ):
 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       ['''input_ids''', '''attention_mask''']

 snake_case__             =       []
 snake_case__             =       []



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







  """simple docstring"""






  lowerCAmelCase    				= AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case     ) if isinstance(_snake_case				,       _snake_case     ) else mask_token

  lowerCAmelCase    				= {} if sp_model_kwargs is None else sp_model_kwargs

  lowerCAmelCase    				= kwargs.get('additional_special_tokens'				,       []     )
  kwargs["additional_special_tokens"] += [
      code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
  ]

  super().__init__(
      src_lang=_snake_case				,       tgt_lang=_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				,       )

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

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

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

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



 @property
 def    UpperCamelCase__  (     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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._src_lang



 @src_lang.setter
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= new_src_lang
  self.set_src_lang_special_tokens(self._src_lang     )



 def __getstate__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.__dict__.copy()
  lowerCAmelCase    				= None
  return state



 def __setstate__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= d

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

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if token in self.fairseq_tokens_to_ids:
   return self.fairseq_tokens_to_ids[token]
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case     ):







  """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    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  lowerCAmelCase    				= ''
  lowerCAmelCase    				= 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(_snake_case     ) + token
    lowerCAmelCase    				= True
    lowerCAmelCase    				= []
   else:
    current_sub_tokens.append(_snake_case     )
    lowerCAmelCase    				= False
  out_string += self.sp_model.decode(_snake_case     )
  return out_string.strip()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






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

  return (out_vocab_file,)



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None				,       _snake_case = False     ):







  """simple docstring"""






  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     )

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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       **_snake_case     ):







  """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'     )
  lowerCAmelCase    				= src_lang
  lowerCAmelCase    				= self(_snake_case				,       add_special_tokens=_snake_case				,       return_tensors=_snake_case				,       **_snake_case     )
  lowerCAmelCase    				= self.convert_tokens_to_ids(_snake_case     )
  lowerCAmelCase    				= tgt_lang_id
  return inputs



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







  """simple docstring"""






  lowerCAmelCase    				= src_lang
  lowerCAmelCase    				= tgt_lang
  return super().prepare_seqaseq_batch(_snake_case				,       _snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.set_src_lang_special_tokens(self.src_lang     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.set_tgt_lang_special_tokens(self.tgt_lang     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.lang_code_to_id[src_lang]
  lowerCAmelCase    				= [self.cur_lang_code_id]
  lowerCAmelCase    				= [self.eos_token_id]



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.lang_code_to_id[tgt_lang]
  lowerCAmelCase    				= [self.cur_lang_code_id]
  lowerCAmelCase    				= [self.eos_token_id]











def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
