





from collections import OrderedDict

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


lowercase_          =		logging.get_logger(__name__)


lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

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

lowercase_          =		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		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'),
    ]
)

lowercase_          =		OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

lowercase_          =		OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

lowercase_          =		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

lowercase_          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_          =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		List[Any]			 =	FLAX_MODEL_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModel)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Tuple			 =	FLAX_MODEL_FOR_PRETRAINING_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Union[str, Any]			 =	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		int			 =	FLAX_MODEL_FOR_MASKED_LM_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Any			 =	FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		int			 =	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Dict			 =	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Union[str, Any]			 =	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		int			 =	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		List[Any]			 =	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		int			 =	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Any			 =	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






lowercase_          =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class     SCREAMING_SNAKE_CASE       (_BaseAutoModelClass				):
   _UpperCamelCase   :		Optional[int]			 =	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING






lowercase_          =		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)









import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase_          =		logging.get_logger(__name__)
lowercase_          =		{"""vocab_file""": """spiece.model"""}

lowercase_          =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase_          =		{
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		List[str]			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		List[Any]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Union[str, Any]			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Any			 =	['input_ids', 'attention_mask']


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Optional[int]=False      ,					a      :    str=False      ,					a      :    str=False      ,					a      :    Tuple=None      ,					a      :    Any=None      ,					a      :    Union[str, Any]=None      ,					a      :    Union[str, Any]=None      ,					a      :    Optional[Dict[str, Any]] = None      ,					**a      :    Optional[int]      ,					)->       None:





     """simple docstring"""







     lowercase__           =  {} if sp_model_kwargs is None else sp_model_kwargs

     lowercase__           =  kwargs.get('name_or_path'	)
     if name_or_path is None:
       logger.warning(
           'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
           ' you are testing the model, this can safely be ignored'	)
       lowercase__           =  'None'

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     lowercase__           =  '<|endoftext|>' if eos_token is None else eos_token
     lowercase__           =  '<unk>' if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
       lowercase__           =  unk_token if pad_token is None else pad_token
       lowercase__           =  eos_token if bos_token is None else bos_token
     else:
       lowercase__           =  '<pad>' if pad_token is None else pad_token
       lowercase__           =  '<s>' if bos_token is None else bos_token

     super().__init__(
         do_lower_case=a      ,					remove_space=a      ,					keep_accents=a      ,					bos_token=a      ,					eos_token=a      ,					unk_token=a      ,					pad_token=a      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**a      ,					)

     lowercase__           =  do_lower_case
     lowercase__           =  remove_space
     lowercase__           =  keep_accents
     lowercase__           =  vocab_file

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(a	)

     # Used for whitespace normalization in input texts
     # fmt : off
     lowercase__           =  {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     lowercase__           =  re.compile(
         f"""[{"".join(map(a      ,					list(range(0      ,					9	)	) + list(range(11      ,					32	)	) + list(range(127      ,					160	)	) + [160, 173, 8_203]	)	)}]"""	)


   def __getstate__(   self      :    Any	)->       str:





     """simple docstring"""







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


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





     """simple docstring"""







     lowercase__           =  d

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

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


   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       int:





     """simple docstring"""







     return len(self.sp_model	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.non_printing_characters_re.sub(''      ,					a	)

     # Normalize whitespaces
     lowercase__           =  ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

     # NFC Unicode normalization
     lowercase__           =  unicodedata.normalize('NFC'      ,					a	)
     return text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					**a      :    Tuple	)->       List[str]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str	)->       int:





     """simple docstring"""







     return self.sp_model.PieceToId(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       str:





     """simple docstring"""







     return self.sp_model.IdToPiece(a	)


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    str	)->       str:





     """simple docstring"""







     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]	)->       str:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ''
     lowercase__           =  False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
         if not prev_is_special:
           out_string += " "

         out_string += self.sp_model.decode(a	) + token
         lowercase__           =  True
         lowercase__           =  []
       else:
         current_sub_tokens.append(a	)
         lowercase__           =  False
     out_string += self.sp_model.decode(a	)

     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  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	) and os.path.isfile(self.vocab_file	):
       copyfile(self.vocab_file      ,					a	)
     elif not os.path.isfile(self.vocab_file	):
       with open(a      ,					'wb'	) as fi:
         lowercase__           =  self.sp_model.serialized_model_proto()
         fi.write(a	)

     return (out_vocab_file,)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Union[str, List[str]]      ,					a      :    Union[str, bool] = False	)->       Union[List[int], List[List[int]], "torch.Tensor"]:





     """simple docstring"""







     if isinstance(a      ,					a	):
       lowercase__           =  self.preprocess_text(a	)
       lowercase__           =  self.sp_model.encode(a	)
     else:
       lowercase__           =  [self.preprocess_text(a	) for t in text]
       lowercase__           =  self.sp_model.encode(a	)

     if return_tensors is True or return_tensors == "pt":
       lowercase__           =  torch.tensor(a	)

     return token_ids


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[int, List[int]]	)->       str:





     """simple docstring"""







     return self.sp_model.decode(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    "Conversation"	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
     lowercase__           =  (
         f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a	) + f"""{self.bos_token}Bot:"""
     )
     return self.encode(text=a	)


