





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_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= '▁'

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

UpperCAmelCase_     							= {
    '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'
        ),
    }
}

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

# fmt: off
UpperCAmelCase_     							= ['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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[Any]   		=		VOCAB_FILES_NAMES
   a						:							Any   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							Optional[Any]   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							List[Any]   		=		["input_ids", "attention_mask"]

   a						:							List[int]   		=		[]
   a						:							List[int]   		=		[]




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






       """simple docstring"""
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else mask_token

       UpperCamelCase__							:				List[Any]          =      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__magic_name__,		eos_token=__magic_name__,		unk_token=__magic_name__,		sep_token=__magic_name__,		cls_token=__magic_name__,		pad_token=__magic_name__,		mask_token=__magic_name__,		tokenizer_file=__magic_name__,		src_lang=__magic_name__,		tgt_lang=__magic_name__,		additional_special_tokens=__magic_name__,		sp_model_kwargs=self.sp_model_kwargs,		**__magic_name__,		)

       UpperCamelCase__							:				Union[str, Any]          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(str(__magic_name__							)							)
       UpperCamelCase__							:				List[Any]          =      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__							:				Any          =      {'''<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__							:				Optional[Any]          =      1

       UpperCamelCase__							:				str          =      len(self.sp_model							)
       UpperCamelCase__							:				Dict          =      {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__magic_name__							)
       }
       UpperCamelCase__							:				Union[str, Any]          =      {v: k for k, v in self.lang_code_to_id.items()}
       UpperCamelCase__							:				Dict          =      len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

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

       UpperCamelCase__							:				Any          =      src_lang if src_lang is not None else '''en_XX'''
       UpperCamelCase__							:				List[Any]          =      self.lang_code_to_id[self._src_lang]
       UpperCamelCase__							:				Dict          =      tgt_lang
       self.set_src_lang_special_tokens(self._src_lang							)




   def __getstate__(    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.__dict__.copy()
       UpperCamelCase__							:				str          =      None
       UpperCamelCase__							:				int          =      self.sp_model.serialized_model_proto()
       return state




   def __setstate__(    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      d

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

       UpperCamelCase__							:				int          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)




   @property
   def       UpperCamelCase__  (    self							) ->      Dict:






       """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							) ->      str:






       """simple docstring"""
       return self._src_lang




   @src_lang.setter
   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				str          =      new_src_lang
       self.set_src_lang_special_tokens(self._src_lang							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = False							) ->      List[int]:






       """simple docstring"""

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

       UpperCamelCase__							:				str          =      [1] * len(self.prefix_tokens							)
       UpperCamelCase__							:				List[Any]          =      [1] * len(self.suffix_tokens							)
       if token_ids_a is None:
           return prefix_ones + ([0] * len(__magic_name__							)) + suffix_ones
       return prefix_ones + ([0] * len(__magic_name__							)) + ([0] * len(__magic_name__							)) + suffix_ones




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      List[int]:






       """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,		__magic_name__,		__magic_name__ = None							) ->      List[int]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      [self.sep_token_id]
       UpperCamelCase__							:				Union[str, Any]          =      [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,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							) ->      Dict:






       """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'''							)
       UpperCamelCase__							:				List[str]          =      src_lang
       UpperCamelCase__							:				List[Any]          =      self(__magic_name__,		add_special_tokens=__magic_name__,		return_tensors=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.convert_tokens_to_ids(__magic_name__							)
       UpperCamelCase__							:				Any          =      tgt_lang_id
       return inputs




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      {self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       return self.sp_model.encode(__magic_name__,		out_type=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       UpperCamelCase__							:				int          =      self.sp_model.PieceToId(__magic_name__							)

       # 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,		__magic_name__							) ->      Tuple:






       """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,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      ''''''.join(__magic_name__							).replace(__magic_name__,		''' '''							).strip()
       return out_string




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      Tuple[str]:






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

       if os.path.abspath(self.vocab_file							) != os.path.abspath(__magic_name__							) and os.path.isfile(self.vocab_file							):
           copyfile(self.vocab_file,		__magic_name__							)
       elif not os.path.isfile(self.vocab_file							):
           with open(__magic_name__,		'''wb'''							) as fi:
               UpperCamelCase__							:				List[str]          =      self.sp_model.serialized_model_proto()
               fi.write(__magic_name__							)

       return (out_vocab_file,)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = "en_XX",		__magic_name__ = None,		__magic_name__ = "ro_RO",		**__magic_name__,		) ->      BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      src_lang
       UpperCamelCase__							:				Dict          =      tgt_lang
       return super().prepare_seqaseq_batch(__magic_name__,		__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       return self.set_src_lang_special_tokens(self.src_lang							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       return self.set_tgt_lang_special_tokens(self.tgt_lang							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.lang_code_to_id[src_lang]
       UpperCamelCase__							:				Tuple          =      []
       UpperCamelCase__							:				Optional[Any]          =      [self.eos_token_id, self.cur_lang_code]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.lang_code_to_id[lang]
       UpperCamelCase__							:				Union[str, Any]          =      []
       UpperCamelCase__							:				str          =      [self.eos_token_id, self.cur_lang_code]








def 		lowerCAmelCase_    (  __UpperCAmelCase:			float		,   __UpperCAmelCase:			int      )      ->	float:
    if digit_amount > 0:
        return round(number - int(__UpperCAmelCase      )		,   __UpperCAmelCase      )
    return number - int(__UpperCAmelCase      )


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))

