


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
		pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}





class  _a							(    _lowercase):
	_a  :  Union[str, Any]				   =							'''align_text_model'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Tuple=3_0522		, _SCREAMING_SNAKE_CASE						:		Dict=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Optional[int]=12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Tuple=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		List[Any]=512		, _SCREAMING_SNAKE_CASE						:		Optional[int]=2		, _SCREAMING_SNAKE_CASE						:		List[str]=0.02		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1E-12		, _SCREAMING_SNAKE_CASE						:		Any=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		List[Any]=True		, **_SCREAMING_SNAKE_CASE						:		str		, )->	Optional[Any]:
			super().__init__(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= vocab_size
			lowerCAmelCase__       :      int   					= hidden_size
			lowerCAmelCase__       :      List[Any]   					= num_hidden_layers
			lowerCAmelCase__       :      int   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      str   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      List[str]   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      List[Any]   					= max_position_embeddings
			lowerCAmelCase__       :      Dict   					= type_vocab_size
			lowerCAmelCase__       :      Union[str, Any]   					= initializer_range
			lowerCAmelCase__       :      Dict   					= layer_norm_eps
			lowerCAmelCase__       :      Tuple   					= position_embedding_type
			lowerCAmelCase__       :      List[str]   					= use_cache
			lowerCAmelCase__       :      Dict   					= pad_token_id




	@classmethod
	def 							UpperCAmelCase__( cls						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, os.PathLike]		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	"PretrainedConfig":
			cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= cls.get_config_dict(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			# get the text config dict if we are loading from AlignConfig
			if config_dict.get('''model_type'''		) == "align":
					lowerCAmelCase__       :      List[Any]   					= config_dict['''text_config''']

			if "model_type" in config_dict and hasattr(cls		, '''model_type'''		) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
					    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

			return cls.from_dict(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)





class  _a							(    _lowercase):
	_a  :  Any				   =							'''align_vision_model'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int = 3		, _SCREAMING_SNAKE_CASE						:		int = 600		, _SCREAMING_SNAKE_CASE						:		float = 2.0		, _SCREAMING_SNAKE_CASE						:		float = 3.1		, _SCREAMING_SNAKE_CASE						:		int = 8		, _SCREAMING_SNAKE_CASE						:		List[int] = [3, 3, 5, 3, 5, 5, 3]		, _SCREAMING_SNAKE_CASE						:		List[int] = [32, 16, 24, 40, 80, 112, 192]		, _SCREAMING_SNAKE_CASE						:		List[int] = [16, 24, 40, 80, 112, 192, 320]		, _SCREAMING_SNAKE_CASE						:		List[int] = []		, _SCREAMING_SNAKE_CASE						:		List[int] = [1, 2, 2, 2, 1, 2, 1]		, _SCREAMING_SNAKE_CASE						:		List[int] = [1, 2, 2, 3, 3, 4, 1]		, _SCREAMING_SNAKE_CASE						:		List[int] = [1, 6, 6, 6, 6, 6, 6]		, _SCREAMING_SNAKE_CASE						:		float = 0.25		, _SCREAMING_SNAKE_CASE						:		str = "swish"		, _SCREAMING_SNAKE_CASE						:		int = 2560		, _SCREAMING_SNAKE_CASE						:		str = "mean"		, _SCREAMING_SNAKE_CASE						:		float = 0.02		, _SCREAMING_SNAKE_CASE						:		float = 0.001		, _SCREAMING_SNAKE_CASE						:		float = 0.99		, _SCREAMING_SNAKE_CASE						:		float = 0.2		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	Optional[Any]:
			super().__init__(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= num_channels
			lowerCAmelCase__       :      int   					= image_size
			lowerCAmelCase__       :      List[str]   					= width_coefficient
			lowerCAmelCase__       :      Optional[Any]   					= depth_coefficient
			lowerCAmelCase__       :      Dict   					= depth_divisor
			lowerCAmelCase__       :      int   					= kernel_sizes
			lowerCAmelCase__       :      Any   					= in_channels
			lowerCAmelCase__       :      str   					= out_channels
			lowerCAmelCase__       :      Union[str, Any]   					= depthwise_padding
			lowerCAmelCase__       :      str   					= strides
			lowerCAmelCase__       :      Tuple   					= num_block_repeats
			lowerCAmelCase__       :      int   					= expand_ratios
			lowerCAmelCase__       :      Any   					= squeeze_expansion_ratio
			lowerCAmelCase__       :      List[str]   					= hidden_act
			lowerCAmelCase__       :      List[str]   					= hidden_dim
			lowerCAmelCase__       :      List[str]   					= pooling_type
			lowerCAmelCase__       :      Dict   					= initializer_range
			lowerCAmelCase__       :      Tuple   					= batch_norm_eps
			lowerCAmelCase__       :      Optional[int]   					= batch_norm_momentum
			lowerCAmelCase__       :      Any   					= drop_connect_rate
			lowerCAmelCase__       :      List[str]   					= sum(_SCREAMING_SNAKE_CASE		) * 4




	@classmethod
	def 							UpperCAmelCase__( cls						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, os.PathLike]		, **_SCREAMING_SNAKE_CASE						:		str		)->	"PretrainedConfig":
			cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= cls.get_config_dict(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			# get the vision config dict if we are loading from AlignConfig
			if config_dict.get('''model_type'''		) == "align":
					lowerCAmelCase__       :      Optional[int]   					= config_dict['''vision_config''']

			if "model_type" in config_dict and hasattr(cls		, '''model_type'''		) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
					    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

			return cls.from_dict(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)





class  _a							(    _lowercase):
	_a  :  List[str]				   =							'''align'''
	_a  :  Tuple				   =							True




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Any=None		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		str=640		, _SCREAMING_SNAKE_CASE						:		int=1.0		, _SCREAMING_SNAKE_CASE						:		int=0.02		, **_SCREAMING_SNAKE_CASE						:		int		, )->	Union[str, Any]:
			super().__init__(**_SCREAMING_SNAKE_CASE		)

			if text_config is None:
					lowerCAmelCase__       :      Optional[Any]   					= {}
					logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''		)

			if vision_config is None:
					lowerCAmelCase__       :      Union[str, Any]   					= {}
					logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''		)

			lowerCAmelCase__       :      int   					= AlignTextConfig(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= AlignVisionConfig(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Any   					= projection_dim
			lowerCAmelCase__       :      int   					= temperature_init_value
			lowerCAmelCase__       :      Union[str, Any]   					= initializer_range




	@classmethod
	def 							UpperCAmelCase__( cls						:		Tuple		, _SCREAMING_SNAKE_CASE						:		AlignTextConfig		, _SCREAMING_SNAKE_CASE						:		AlignVisionConfig		, **_SCREAMING_SNAKE_CASE						:		str		)->	int:
			return cls(text_config=text_config.to_dict()		, vision_config=vision_config.to_dict()		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		)->	Any:
			lowerCAmelCase__       :      str   					= copy.deepcopy(self.__dict__		)
			lowerCAmelCase__       :      Any   					= self.text_config.to_dict()
			lowerCAmelCase__       :      List[str]   					= self.vision_config.to_dict()
			lowerCAmelCase__       :      Tuple   					= self.__class__.model_type
			return output








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'''
)




