





"""simple docstring"""




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:
						UpperCamelCase_									=None


UpperCamelCase_									=logging.get_logger(__name__)


UpperCamelCase_									={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

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"""
        ),
    },
    """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""",
    },
}

UpperCamelCase_									={
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# 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 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   ['''input_ids''', '''attention_mask''']
				UpperCamelCase								=   MBartTokenizer

				UpperCamelCase								=   []
				UpperCamelCase								=   []






				def __init__(			self		:   int,			lowerCAmelCase__		:   Optional[Any]=None,			lowerCAmelCase__		:   Union[str, Any]=None,			lowerCAmelCase__		:   Optional[Any]="<s>",			lowerCAmelCase__		:   Union[str, Any]="</s>",			lowerCAmelCase__		:   Optional[int]="</s>",			lowerCAmelCase__		:   Union[str, Any]="<s>",			lowerCAmelCase__		:   List[Any]="<unk>",			lowerCAmelCase__		:   int="<pad>",			lowerCAmelCase__		:   Union[str, Any]="<mask>",			lowerCAmelCase__		:   Union[str, Any]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   int=None,			**lowerCAmelCase__		:   Tuple,			)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   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__,			)

										_UpperCamelCase      : Optional[Any]         =   vocab_file
										_UpperCamelCase      : Tuple         =   False if not self.vocab_file else True

										_UpperCamelCase      : Tuple         =   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}							)
										_UpperCamelCase      : Optional[int]         =   {
										    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__							) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										_UpperCamelCase      : Tuple         =   src_lang if src_lang is not None else '''en_XX'''
										_UpperCamelCase      : Dict         =   self.convert_tokens_to_ids(self._src_lang							)
										_UpperCamelCase      : Tuple         =   tgt_lang
										self.set_src_lang_special_tokens(self._src_lang							)






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				str:




										'''simple docstring'''



										return self._src_lang






				@src_lang.setter
				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   str							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   new_src_lang
										self.set_src_lang_special_tokens(self._src_lang							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = 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     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   [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     snake_case     (			self		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str],			lowerCAmelCase__		:   Optional[str],			**lowerCAmelCase__		:   Optional[int]							)     ->				Any:




										'''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      : Tuple         =   src_lang
										_UpperCamelCase      : Union[str, Any]         =   self(lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   tgt_lang_id
										return inputs






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str = "en_XX",			lowerCAmelCase__		:   Optional[List[str]] = None,			lowerCAmelCase__		:   str = "ro_RO",			**lowerCAmelCase__		:   Any,			)     ->				BatchEncoding:




										'''simple docstring'''



										_UpperCamelCase      : int         =   src_lang
										_UpperCamelCase      : Tuple         =   tgt_lang
										return super().prepare_seqaseq_batch(lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							)






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




										'''simple docstring'''



										return self.set_src_lang_special_tokens(self.src_lang							)






				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										return self.set_tgt_lang_special_tokens(self.tgt_lang							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Any							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   []
										_UpperCamelCase      : int         =   [self.eos_token_id, self.cur_lang_code]

										_UpperCamelCase      : Any         =   self.convert_ids_to_tokens(self.prefix_tokens							)
										_UpperCamelCase      : Dict         =   self.convert_ids_to_tokens(self.suffix_tokens							)

										_UpperCamelCase      : Dict         =   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     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   []
										_UpperCamelCase      : str         =   [self.eos_token_id, self.cur_lang_code]

										_UpperCamelCase      : List[Any]         =   self.convert_ids_to_tokens(self.prefix_tokens							)
										_UpperCamelCase      : List[str]         =   self.convert_ids_to_tokens(self.suffix_tokens							)

										_UpperCamelCase      : List[str]         =   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     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										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
										_UpperCamelCase      : Optional[Any]         =   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,)










"""simple docstring"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



