


from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCamelCase						=		TypeVar('''T''')






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		return (position - 1) // 2






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		return (2 * position) + 1






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		return (2 * position) + 2





class  _a							(    Generic[T]):




	def __init__( self						:		Dict		)->	None:
			lowerCAmelCase__       :      list[tuple[T, int]]   					= []
			lowerCAmelCase__       :      dict[T, int]   					= {}
			lowerCAmelCase__       :      int   					= 0




	def __len__( self						:		Any		)->	int:
			return self.elements




	def __repr__( self						:		Optional[int]		)->	str:
			return str(self.heap		)




	def 							UpperCAmelCase__( self						:		List[str]		)->	bool:
			# Check if the priority queue is empty
			return self.elements == 0




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		T		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)		)
			lowerCAmelCase__       :      str   					= self.elements
			self.elements += 1
			self._bubble_up(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
					self._swap_nodes(0		, self.elements - 1		)
			lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= self.heap[0]
					self._bubble_down(_SCREAMING_SNAKE_CASE		)
			return elem




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		T		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			# Update the weight of the given key
			lowerCAmelCase__       :      Union[str, Any]   					= self.position_map[elem]
			lowerCAmelCase__       :      List[Any]   					= (elem, weight)
			if position > 0:
					lowerCAmelCase__       :      str   					= get_parent_position(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(_SCREAMING_SNAKE_CASE		)
					else:
							self._bubble_down(_SCREAMING_SNAKE_CASE		)
			else:
					self._bubble_down(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		T		)->	None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			lowerCAmelCase__       :      str   					= self.position_map[elem]
			if curr_pos == 0:
					return None
			lowerCAmelCase__       :      Any   					= get_parent_position(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= self.heap[curr_pos]
			lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					return self._bubble_up(_SCREAMING_SNAKE_CASE		)
			return None




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		T		)->	None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			lowerCAmelCase__       :      List[Any]   					= self.position_map[elem]
			lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= self.heap[curr_pos]
			lowerCAmelCase__       :      Union[str, Any]   					= get_child_left_position(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= get_child_right_position(_SCREAMING_SNAKE_CASE		)
			if child_left_position < self.elements and child_right_position < self.elements:
					lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= self.heap[child_left_position]
					lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
							return self._bubble_down(_SCREAMING_SNAKE_CASE		)
			if child_left_position < self.elements:
					lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
							return self._bubble_down(_SCREAMING_SNAKE_CASE		)
			else:
					return None
			if child_right_position < self.elements:
					lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
							return self._bubble_down(_SCREAMING_SNAKE_CASE		)
			return None




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			# Swap the nodes at the given positions
			lowerCAmelCase__       :      str   					= self.heap[nodea_pos][0]
			lowerCAmelCase__       :      List[str]   					= self.heap[nodea_pos][0]
			lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			lowerCAmelCase__       :      str   					= nodea_pos
			lowerCAmelCase__       :      int   					= nodea_pos





class  _a							(    Generic[T]):




	def __init__( self						:		Dict		)->	None:
			lowerCAmelCase__       :      dict[T, dict[T, int]]   					= {}
			lowerCAmelCase__       :      int   					= 0




	def __repr__( self						:		str		)->	str:
			return str(self.connections		)




	def __len__( self						:		List[Any]		)->	int:
			return self.nodes




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		T		)->	None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
					lowerCAmelCase__       :      Optional[int]   					= {}
					self.nodes += 1




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		T		, _SCREAMING_SNAKE_CASE						:		T		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_SCREAMING_SNAKE_CASE		)
			self.add_node(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= weight
			lowerCAmelCase__       :      List[Any]   					= weight






def 					lowerCamelCase_   (	_a     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      dict[T, int]   					= {node: maxsize for node in graph.connections}
		lowerCAmelCase__       :      dict[T, T | None]   					= {node: None for node in graph.connections}

		lowerCAmelCase__       :      MinPriorityQueue[T]   					= MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(_a     ,       _a	)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		lowerCAmelCase__       :      Union[str, Any]   					= priority_queue.extract_min()
		lowerCAmelCase__       :      Dict   					= 0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						lowerCAmelCase__       :      Optional[int]   					= dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(_a     ,       dist[neighbour]	)
						lowerCAmelCase__       :      Union[str, Any]   					= node

    # running prim's algorithm
		while not priority_queue.is_empty():
				lowerCAmelCase__       :      str   					= priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								lowerCAmelCase__       :      List[str]   					= dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(_a     ,       dist[neighbour]	)
								lowerCAmelCase__       :      Any   					= node
		return dist, parent








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'''
)




