




'''simple docstring'''


from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


lowerCAmelCase_      :  Dict				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Optional[int]				     =  OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

lowerCAmelCase_      :  Tuple				     =  OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

lowerCAmelCase_      :  List[str]				     =  OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

lowerCAmelCase_      :  Any				     =  OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

lowerCAmelCase_      :  Any				     =  OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

lowerCAmelCase_      :  Dict				     =  OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

lowerCAmelCase_      :  Optional[Any]				     =  OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

lowerCAmelCase_      :  Dict				     =  OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

lowerCAmelCase_      :  Tuple				     =  OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

lowerCAmelCase_      :  Any				     =  OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

lowerCAmelCase_      :  int				     =  OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

lowerCAmelCase_      :  Optional[int]				     =  OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

lowerCAmelCase_      :  int				     =  OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

lowerCAmelCase_      :  Any				     =  OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

lowerCAmelCase_      :  str				     =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCAmelCase_      :  str				     =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCAmelCase_      :  Dict				     =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCAmelCase_      :  List[Any]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCAmelCase_      :  List[str]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_      :  Tuple				     =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCAmelCase_      :  Any				     =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCAmelCase_      :  Any				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_      :  Optional[Any]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCAmelCase_      :  Tuple				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_      :  Union[str, Any]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCAmelCase_      :  Optional[int]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCAmelCase_      :  Tuple				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCAmelCase_      :  Optional[int]				     =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       List[str]   							=					FLAX_MODEL_MAPPING

lowerCAmelCase_      :  str				     =  auto_class_update(FlaxAutoModel)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       str   							=					FLAX_MODEL_FOR_PRETRAINING_MAPPING

lowerCAmelCase_      :  Tuple				     =  auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       Dict   							=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

lowerCAmelCase_      :  Dict				     =  auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       List[str]   							=					FLAX_MODEL_FOR_MASKED_LM_MAPPING

lowerCAmelCase_      :  str				     =  auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       int   							=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCAmelCase_      :  int				     =  auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       Union[str, Any]   							=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

lowerCAmelCase_      :  List[str]				     =  auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       Any   							=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

lowerCAmelCase_      :  Dict				     =  auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       Union[str, Any]   							=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCAmelCase_      :  Union[str, Any]				     =  auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       List[Any]   							=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

lowerCAmelCase_      :  Tuple				     =  auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       Tuple   							=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

lowerCAmelCase_      :  Optional[Any]				     =  auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       List[Any]   							=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase_      :  Optional[Any]				     =  auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       int   							=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCAmelCase_      :  List[Any]				     =  auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')







class      SCREAMING_SNAKE_CASE    (						_BaseAutoModelClass				):
      __magic_name__  :       int   							=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

lowerCAmelCase_      :  Union[str, Any]				     =  auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)






'''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


lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  str				     =  '▁'

lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_      :  Optional[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'
        ),
    }
}

lowerCAmelCase_      :  List[str]				     =  {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
lowerCAmelCase_      :  Optional[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      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[Any]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Union[str, Any]   							=					['''input_ids''', '''attention_mask''']

      __magic_name__  :       List[int]   							=					[]
      __magic_name__  :       List[int]   							=					[]






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


           '''simple docstring'''

           a_     :							Any     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else mask_token

           a_     :							Union[str, Any]     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							int     						=  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=lowercase__						,						tgt_lang=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Union[str, Any]     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(lowercase__       )       )
           a_     :							Union[str, 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
           a_     :							Tuple     						=  {"""<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
           a_     :							List[str]     						=  1

           a_     :							List[Any]     						=  len(self.sp_model       )
           a_     :							List[str]     						=  {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__       )
           }
           a_     :							str     						=  {v: k for k, v in self.lang_code_to_id.items()}
           a_     :							Any     						=  len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

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

           a_     :							List[Any]     						=  src_lang if src_lang is not None else """en_XX"""
           a_     :							Any     						=  self.lang_code_to_id[self._src_lang]
           a_     :							Union[str, Any]     						=  tgt_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      @property
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''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 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return self._src_lang






      @src_lang.setter
      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Tuple     						=  new_src_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      def __getstate__( self   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  self.__dict__.copy()
           a_     :							Dict     						=  None
           return state






      def __setstate__( self   :  Optional[Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Tuple     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
           a_     :							List[str]     						=  self.sp_model.PieceToId(lowercase__       )

           # 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 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''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 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  []
           a_     :							List[str]     						=  """"""
           a_     :							Dict     						=  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(lowercase__       ) + token
                     a_     :							str     						=  True
                     a_     :							Optional[Any]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							Union[str, Any]     						=  False
           out_string += self.sp_model.decode(lowercase__       )
           return out_string.strip()






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							Dict     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ) and os.path.isfile(self.vocab_file       ):
                copyfile(self.vocab_file						,						lowercase__       )
           elif not os.path.isfile(self.vocab_file       ):
                with open(lowercase__						,						"""wb"""       ) as fi:
                     a_     :							Dict     						=  self.sp_model.serialized_model_proto()
                     fi.write(lowercase__       )

           return (out_vocab_file,)






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None						,						lowercase__   :  bool = False       ):


           '''simple docstring'''

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

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






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = 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 							lowercase_						( self   :  Any						,						lowercase__   :  Optional[int]						,						lowercase__   :  str						,						lowercase__   :  Optional[str]						,						lowercase__   :  Optional[str]						,						**lowercase__   :  Optional[int]       ):


           '''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"""       )
           a_     :							Union[str, Any]     						=  src_lang
           a_     :							Dict     						=  self(lowercase__						,						add_special_tokens=lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )
           a_     :							Tuple     						=  self.convert_tokens_to_ids(lowercase__       )
           a_     :							Optional[Any]     						=  tgt_lang_id
           return inputs






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  str = "en_XX"						,						lowercase__   :  Optional[List[str]] = None						,						lowercase__   :  str = "ro_RO"						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							str     						=  src_lang
           a_     :							Dict     						=  tgt_lang
           return super().prepare_seqaseq_batch(lowercase__						,						lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  self.lang_code_to_id[src_lang]
           a_     :							Dict     						=  [self.cur_lang_code_id]
           a_     :							Any     						=  [self.eos_token_id]






      def 							lowercase_						( self   :  Any						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.lang_code_to_id[tgt_lang]
           a_     :							str     						=  [self.cur_lang_code_id]
           a_     :							Union[str, Any]     						=  [self.eos_token_id]
