


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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)


lowerCamelCase						=		{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCamelCase						=		{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

lowerCamelCase						=		'''</w>'''
lowerCamelCase						=		'''@@ '''






def 					lowerCamelCase_   (	_a	) ->    Optional[Any]:




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[int]   					= set()
		lowerCAmelCase__       :      int   					= word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				lowerCAmelCase__       :      str   					= char
		return pairs


# Speech2Text2 has no max input length
lowerCamelCase						=		{'''facebook/s2t-wav2vec2-large-en-de''': 1024}





class  _a							(    snake_case__):
	_a  :  Optional[Any]				   =							VOCAB_FILES_NAMES
	_a  :  int				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  List[Any]				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Union[str, Any]				   =							["""input_ids""", """attention_mask"""]




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<s>"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="<pad>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="</s>"		, _SCREAMING_SNAKE_CASE						:		Tuple="<unk>"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=False		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	Optional[int]:
			super().__init__(
			    unk_token=_A		, bos_token=_A		, eos_token=_A		, pad_token=_A		, do_lower_case=_A		, **_A		, )

			lowerCAmelCase__       :      str   					= do_lower_case

			with open(_A		, encoding='''utf-8'''		) as vocab_handle:
					lowerCAmelCase__       :      Union[str, Any]   					= json.load(_A		)
			lowerCAmelCase__       :      Optional[Any]   					= {v: k for k, v in self.encoder.items()}

			if merges_file is None:
					logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.'		)

					lowerCAmelCase__       :      Tuple   					= None
					lowerCAmelCase__       :      Any   					= None
			else:
					with open(_A		, encoding='''utf-8'''		) as merges_handle:
							lowerCAmelCase__       :      List[str]   					= merges_handle.read().split('''\n'''		)[:-1]

					lowerCAmelCase__       :      List[str]   					= [tuple(merge.split()[:2]		) for merge in merges]
					lowerCAmelCase__       :      Optional[int]   					= dict(zip(_A		, range(len(_A		)		)		)		)
					lowerCAmelCase__       :      Union[str, Any]   					= {}




	@property
	def 							UpperCAmelCase__( self						:		str		)->	Union[str, Any]:
			return len(self.decoder		)




	def 							UpperCAmelCase__( self						:		Tuple		)->	Dict:
			return dict(self.encoder		, **self.added_tokens_encoder		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	int:
			lowerCAmelCase__       :      Optional[Any]   					= tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
			if token in self.cache:
					return self.cache[token]
			lowerCAmelCase__       :      Union[str, Any]   					= get_pairs(_A		)

			if not pairs:
					return token

			while True:
					lowerCAmelCase__       :      Union[str, Any]   					= min(_A		, key=lambda _SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(_A		, float('''inf'''		)		)		)
					if bigram not in self.bpe_ranks:
							break
					lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= bigram
					lowerCAmelCase__       :      List[Any]   					= []
					lowerCAmelCase__       :      Optional[Any]   					= 0
					while i < len(_A		):
							try:
									lowerCAmelCase__       :      Optional[int]   					= word.index(_A		, _A		)
							except ValueError:
									new_word.extend(word[i:]		)
									break
							else:
									new_word.extend(word[i:j]		)
									lowerCAmelCase__       :      Any   					= j

							if word[i] == first and i < len(_A		) - 1 and word[i + 1] == second:
									new_word.append(first + second		)
									i += 2
							else:
									new_word.append(word[i]		)
									i += 1
					lowerCAmelCase__       :      Optional[Any]   					= tuple(_A		)
					lowerCAmelCase__       :      str   					= new_word
					if len(_A		) == 1:
							break
					else:
							lowerCAmelCase__       :      int   					= get_pairs(_A		)
			lowerCAmelCase__       :      str   					= ''' '''.join(_A		)
			if word == "\n  " + BPE_TOKEN_MERGES:
					lowerCAmelCase__       :      str   					= '''\n''' + BPE_TOKEN_MERGES

			if word.endswith(_A		):
					lowerCAmelCase__       :      Any   					= word.replace(_A		, ''''''		)

			lowerCAmelCase__       :      List[str]   					= word.replace(''' '''		, _A		)
			lowerCAmelCase__       :      Optional[int]   					= word
			return word




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		)->	Optional[Any]:



			if self.bpe_ranks is None:
					raise ValueError(
					    '''This tokenizer was instantiated without a `merges.txt` file, so'''
					    ''' that it can only be used for decoding, not for encoding.'''
					    '''Make sure to provide `merges.txt` file at instantiation to enable '''
					    '''encoding.'''		)

			if self.do_lower_case:
					lowerCAmelCase__       :      int   					= text.lower()

			lowerCAmelCase__       :      List[Any]   					= text.split()

			lowerCAmelCase__       :      List[Any]   					= []
			for token in text:
					if token:
							split_tokens.extend(list(self.bpe(_A		).split(''' '''		)		)		)

			return split_tokens




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Optional[Any]:
			return self.encoder.get(_A		, self.encoder.get(self.unk_token		)		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= self.decoder.get(_A		, self.unk_token		)
			return result




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[Any]:
			lowerCAmelCase__       :      Union[str, Any]   					= ''' '''.join(_A		)

			# make sure @@ tokens are concatenated
			lowerCAmelCase__       :      Dict   					= ''''''.join(string.split(_A		)		)

			return string




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		int = None		)->	Dict:



			if not os.path.isdir(_A		):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
					return
			lowerCAmelCase__       :      List[str]   					= os.path.join(
			    _A		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
			lowerCAmelCase__       :      List[Any]   					= os.path.join(
			    _A		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

			with open(_A		, '''w'''		, encoding='''utf-8'''		) as f:
					f.write(json.dumps(self.encoder		, indent=2		, sort_keys=_A		, ensure_ascii=_A		) + '''\n'''		)

			lowerCAmelCase__       :      List[Any]   					= 0
			if self.bpe_ranks is None:
					return (vocab_file,)

			with open(_A		, '''w'''		, encoding='''utf-8'''		) as writer:
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		, key=lambda _SCREAMING_SNAKE_CASE		: kv[1]		):
							if index != token_index:
									logger.warning(
									    F'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
									    ''' Please check that the tokenizer is not corrupted!'''		)
									lowerCAmelCase__       :      Optional[Any]   					= token_index
							writer.write(''' '''.join(_A		) + '''\n'''		)
							index += 1

			return (vocab_file, merges_file)








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						=		logging.get_logger(__name__)


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

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

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

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

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

lowerCamelCase						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCamelCase						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCamelCase						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCamelCase						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCamelCase						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)





class  _a							(    _BaseAutoModelClass):
	_a  :  List[str]				   =							FLAX_MODEL_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModel)





class  _a							(    _BaseAutoModelClass):
	_a  :  Union[str, Any]				   =							FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Optional[Any]				   =							FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Tuple				   =							FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Dict				   =							FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)





class  _a							(    _BaseAutoModelClass):
	_a  :  Union[str, Any]				   =							FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)





class  _a							(    _BaseAutoModelClass):
	_a  :  str				   =							FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Union[str, Any]				   =							FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)





class  _a							(    _BaseAutoModelClass):
	_a  :  List[Any]				   =							FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Any				   =							FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)





class  _a							(    _BaseAutoModelClass):
	_a  :  List[Any]				   =							FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)





class  _a							(    _BaseAutoModelClass):
	_a  :  List[Any]				   =							FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCamelCase						=		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')





class  _a							(    _BaseAutoModelClass):
	_a  :  Optional[Any]				   =							FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCamelCase						=		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)




