

'''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_mbartaa import MBartaaTokenizer
else:
     _lowerCAmelCase :	Union[str, Any]     				=    None


_lowerCAmelCase :	str     				=    logging.get_logger(__name__)

_lowerCAmelCase :	Optional[int]     				=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase :	Any     				=    {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCAmelCase :	Optional[int]     				=    {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
_lowerCAmelCase :	Dict     				=    ['''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     lowerCAmelCase				(    lowercase__				):


       '''simple docstring'''







       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    							=	MBartaaTokenizer

       snake_case    							=	[]
       snake_case    							=	[]

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







        '''simple docstring'''

        lowerCamelCase      						=    AddedToken(_A						,	lstrip=_A						,	rstrip=_A     ) if isinstance(_A						,	_A     ) else mask_token

        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__(
            _A						,	src_lang=_A						,	tgt_lang=_A						,	tokenizer_file=_A						,	eos_token=_A						,	sep_token=_A						,	cls_token=_A						,	unk_token=_A						,	pad_token=_A						,	mask_token=_A						,	**_A						,	)

        lowerCamelCase      						=    vocab_file
        lowerCamelCase      						=    False if not self.vocab_file else True

        lowerCamelCase      						=    {
            lang_code: self.convert_tokens_to_ids(_A     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

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

       @property
       def   lowerCamelCase__						(	self					:  Any     )   -> str:







        '''simple docstring'''

        return self._src_lang

       @src_lang.setter
       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  str     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    new_src_lang
        self.set_src_lang_special_tokens(self._src_lang     )

       def   lowerCamelCase__						(	self					:  Tuple						,	__snake_case					:  List[int]						,	__snake_case					:  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   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[str]						,	__snake_case					:  str = "en_XX"						,	__snake_case					:  Optional[List[str]] = None						,	__snake_case					:  str = "ro_RO"						,	**__snake_case					:  Tuple						,	)   -> BatchEncoding:







        '''simple docstring'''

        lowerCamelCase      						=    src_lang
        lowerCamelCase      						=    tgt_lang
        return super().prepare_seqaseq_batch(_A						,	_A						,	**_A     )

       def   lowerCamelCase__						(	self					:  List[str]     )   -> List[str]:







        '''simple docstring'''

        return self.set_src_lang_special_tokens(self.src_lang     )

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Dict:







        '''simple docstring'''

        return self.set_tgt_lang_special_tokens(self.tgt_lang     )

       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  str     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    self.convert_tokens_to_ids(_A     )
        lowerCamelCase      						=    [self.cur_lang_code_id]
        lowerCamelCase      						=    [self.eos_token_id]

        lowerCamelCase      						=    self.convert_ids_to_tokens(self.prefix_tokens     )
        lowerCamelCase      						=    self.convert_ids_to_tokens(self.suffix_tokens     )

        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  str     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    self.convert_tokens_to_ids(_A     )
        lowerCamelCase      						=    [self.cur_lang_code_id]
        lowerCamelCase      						=    [self.eos_token_id]

        lowerCamelCase      						=    self.convert_ids_to_tokens(self.prefix_tokens     )
        lowerCamelCase      						=    self.convert_ids_to_tokens(self.suffix_tokens     )

        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Any						,	__snake_case					:  Dict						,	__snake_case					:  str						,	__snake_case					:  Optional[str]						,	__snake_case					:  Optional[str]						,	**__snake_case					:  Optional[Any]     )   -> Tuple:







        '''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(_A						,	add_special_tokens=_A						,	return_tensors=_A						,	**_A     )
        lowerCamelCase      						=    self.convert_tokens_to_ids(_A     )
        lowerCamelCase      						=    tgt_lang_id
        return inputs





       def   lowerCamelCase__						(	self					:  Dict						,	__snake_case					:  str						,	__snake_case					:  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(_A     ):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
         return
        lowerCamelCase      						=    os.path.join(
            _A						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(_A     ):
         copyfile(self.vocab_file						,	_A     )

        return (out_vocab_file,)







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase				):


       '''simple docstring'''







       @register_to_config
       def __init__(	self					:  Any						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  float						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  str						,	__snake_case					:  bool = False						,	)   -> int:







        '''simple docstring'''

        super().__init__()

        lowerCamelCase      						=    nn.Linear(__snake_case						,	__snake_case						,	bias=__snake_case     )

        lowerCamelCase      						=    nn.Embedding(__snake_case						,	__snake_case     )
        lowerCamelCase      						=    False

        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )

        lowerCamelCase      						=    TaConfig(
            d_model=__snake_case						,	num_heads=__snake_case						,	d_kv=__snake_case						,	d_ff=__snake_case						,	feed_forward_proj=__snake_case						,	dropout_rate=__snake_case						,	is_decoder=__snake_case						,	is_encoder_decoder=__snake_case						,	)
        lowerCamelCase      						=    nn.ModuleList()
        for lyr_num in range(__snake_case     ):
         lowerCamelCase      						=    TaBlock(__snake_case     )
         self.encoders.append(__snake_case     )

        lowerCamelCase      						=    TaLayerNorm(__snake_case     )
        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )





       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[Any]						,	__snake_case					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    self.input_proj(__snake_case     )

        # terminal relative positional encodings
        lowerCamelCase      						=    encoder_inputs.shape[1]
        lowerCamelCase      						=    torch.arange(__snake_case						,	device=encoder_inputs.device     )

        lowerCamelCase      						=    encoder_inputs_mask.sum(-1     )
        lowerCamelCase      						=    torch.roll(input_positions.unsqueeze(0     )						,	tuple(seq_lens.tolist()     )						,	dims=0     )
        x += self.position_encoding(__snake_case     )

        lowerCamelCase      						=    self.dropout_pre(__snake_case     )

        # inverted the attention mask
        lowerCamelCase      						=    encoder_inputs.size()
        lowerCamelCase      						=    self.get_extended_attention_mask(__snake_case						,	__snake_case     )

        for lyr in self.encoders:
         lowerCamelCase      						=    lyr(__snake_case						,	__snake_case     )[0]
        lowerCamelCase      						=    self.layer_norm(__snake_case     )

        return self.dropout_post(__snake_case     ), encoder_inputs_mask




