






'''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


UpperCamelCase     :   Union[str, Any]	      =		logging.get_logger(__name__)


UpperCamelCase     :   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'),
    ]
)

UpperCamelCase     :   Optional[Any]	      =		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'),
    ]
)

UpperCamelCase     :   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'),
    ]
)

UpperCamelCase     :   Optional[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'),
    ]
)

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

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCamelCase     :   Dict	      =		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'),
    ]
)

UpperCamelCase     :   List[Any]	      =		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'),
    ]
)

UpperCamelCase     :   List[str]	      =		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'),
    ]
)

UpperCamelCase     :   int	      =		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'),
    ]
)

UpperCamelCase     :   Tuple	      =		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'),
    ]
)

UpperCamelCase     :   Tuple	      =		OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

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




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModel)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_PRETRAINING_MAPPING



UpperCamelCase     :   Optional[int]	      =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



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




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MASKED_LM_MAPPING



UpperCamelCase     :   List[str]	      =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Any	      =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



UpperCamelCase     :   Tuple	      =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



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







'''simple docstring'''



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase_							:	Any	     =		logging.get_logger(__name__)


UpperCAmelCase_							:	str	     =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_							:	str	     =		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_							:	Union[str, Any]	     =		{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Dict										=      VOCAB_FILES_NAMES
       __lowercase	:   Dict										=      PRETRAINED_VOCAB_FILES_MAP
       __lowercase	:   Tuple										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase	:   Union[str, Any]										=      LEDTokenizer
       __lowercase	:   int										=      ['''input_ids''', '''attention_mask''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="replace"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="<unk>"     ,	__SCREAMING_SNAKE_CASE="<pad>"     ,	__SCREAMING_SNAKE_CASE="<mask>"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	List[Any]:



            '''simple docstring'''




            super().__init__(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	tokenizer_file=__SCREAMING_SNAKE_CASE     ,	errors=__SCREAMING_SNAKE_CASE     ,	bos_token=__SCREAMING_SNAKE_CASE     ,	eos_token=__SCREAMING_SNAKE_CASE     ,	sep_token=__SCREAMING_SNAKE_CASE     ,	cls_token=__SCREAMING_SNAKE_CASE     ,	unk_token=__SCREAMING_SNAKE_CASE     ,	pad_token=__SCREAMING_SNAKE_CASE     ,	mask_token=__SCREAMING_SNAKE_CASE     ,	add_prefix_space=__SCREAMING_SNAKE_CASE     ,	trim_offsets=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)

            __snake_case            =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
            if pre_tok_state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                 __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	pre_tok_state.pop('''type'''	)	)
                 __snake_case            =	add_prefix_space
                 __snake_case            =	pre_tok_class(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            __snake_case            =	'''post_processor'''
            __snake_case            =	getattr(self.backend_tokenizer     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            if tokenizer_component_instance:
                 __snake_case            =	json.loads(tokenizer_component_instance.__getstate__()	)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      __snake_case            =	tuple(state['''sep''']	)
                 if "cls" in state:
                      __snake_case            =	tuple(state['''cls''']	)

                 __snake_case            =	False

                 if state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                      __snake_case            =	add_prefix_space
                      __snake_case            =	True

                 if state.get('''trim_offsets'''     ,	__SCREAMING_SNAKE_CASE	) != trim_offsets:
                      __snake_case            =	trim_offsets
                      __snake_case            =	True

                 if changes_to_apply:
                      __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	state.pop('''type'''	)	)
                      __snake_case            =	component_class(**__SCREAMING_SNAKE_CASE	)
                      setattr(self.backend_tokenizer     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            if self._mask_token is None:
                 if self.verbose:
                      logger.error('''Using mask_token, but it is not set yet.'''	)
                 return None
            return str(self._mask_token	)


       @mask_token.setter
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	AddedToken(__SCREAMING_SNAKE_CASE     ,	lstrip=__SCREAMING_SNAKE_CASE     ,	rstrip=__SCREAMING_SNAKE_CASE	) if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else value
            __snake_case            =	value


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                     '''to use it with pretokenized inputs.'''	)

            return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                     '''to use it with pretokenized inputs.'''	)

            return super()._encode_plus(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Tuple[str]:



            '''simple docstring'''




            __snake_case            =	self._tokenizer.model.save(__SCREAMING_SNAKE_CASE     ,	name=__SCREAMING_SNAKE_CASE	)
            return tuple(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	List[int]:



            '''simple docstring'''




            __snake_case            =	[self.sep_token_id]
            __snake_case            =	[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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	dict:



            '''simple docstring'''




            __snake_case            =	super()._pad(
                encoded_inputs=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_SNAKE_CASE     ,	padding_strategy=__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	)

            # Load from model defaults
            if return_attention_mask is None:
                 __snake_case            =	'''attention_mask''' in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                 __snake_case            =	encoded_inputs[self.model_input_names[0]]
                 # `global_attention_mask` need to have the same length as other (sequential) inputs.
                 __snake_case            =	len(encoded_inputs['''global_attention_mask''']	) != len(__SCREAMING_SNAKE_CASE	)

                 if needs_to_be_padded:
                      __snake_case            =	len(__SCREAMING_SNAKE_CASE	) - len(encoded_inputs['''global_attention_mask''']	)

                      if self.padding_side == "right":
                           # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                           __snake_case            =	(
                               encoded_inputs['''global_attention_mask'''] + [-1] * difference
                           )
                      elif self.padding_side == "left":
                           __snake_case            =	[-1] * difference + encoded_inputs[
                               '''global_attention_mask'''
                           ]
                      else:
                           raise ValueError('''Invalid padding strategy:''' + str(self.padding_side	)	)

            return encoded_inputs






