
from __future__ import annotations

import math
import random
from typing import Any







class 			__A:




				def __init__(     self				)    ->      None:


								'''simple docstring'''




								__a										=    []
								__a										=    0
								__a										=    0




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      bool:


								'''simple docstring'''




								return self.head == self.tail




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								self.data.append(SCREAMING_SNAKE_CASE__				)
								__a										=    self.tail + 1




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.data[self.head]
								__a										=    self.head + 1
								return ret




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.tail - self.head




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      None:


								'''simple docstring'''




								print(self.data				)
								print('''**************'''				)
								print(self.data[self.head : self.tail]				)







class 			__A:




				def __init__(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    data
								__a										=    None
								__a										=    None
								__a										=    1




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								return self.data




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      MyNode | None:


								'''simple docstring'''




								return self.left




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      MyNode | None:


								'''simple docstring'''




								return self.right




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.height




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    data




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    node




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    node




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    height



def     __lowerCAmelCase			(			a__  )       ->  int:

				if node is None:
								return 0
				return node.get_height()


def     __lowerCAmelCase			(			a__						,    a__  )       ->  int:

				if a > b:
								return a
				return b


def     __lowerCAmelCase			(			a__  )       ->  MyNode:
				print('''left rotation node:'''						,    node.get_data()  )
				__a										=    node.get_left()
				assert ret is not None
				node.set_left(ret.get_right()  )
				ret.set_right(__A  )
				__a										=    my_max(get_height(node.get_right()  )						,    get_height(node.get_left()  )  ) + 1
				node.set_height(__A  )
				__a										=    my_max(get_height(ret.get_right()  )						,    get_height(ret.get_left()  )  ) + 1
				ret.set_height(__A  )
				return ret


def     __lowerCAmelCase			(			a__  )       ->  MyNode:
				print('''right rotation node:'''						,    node.get_data()  )
				__a										=    node.get_right()
				assert ret is not None
				node.set_right(ret.get_left()  )
				ret.set_left(__A  )
				__a										=    my_max(get_height(node.get_right()  )						,    get_height(node.get_left()  )  ) + 1
				node.set_height(__A  )
				__a										=    my_max(get_height(ret.get_right()  )						,    get_height(ret.get_left()  )  ) + 1
				ret.set_height(__A  )
				return ret


def     __lowerCAmelCase			(			a__  )       ->  MyNode:
				__a										=    node.get_left()
				assert left_child is not None
				node.set_left(left_rotation(__A  )  )
				return right_rotation(__A  )


def     __lowerCAmelCase			(			a__  )       ->  MyNode:
				__a										=    node.get_right()
				assert right_child is not None
				node.set_right(right_rotation(__A  )  )
				return left_rotation(__A  )


def     __lowerCAmelCase			(			a__						,    a__  )       ->  MyNode | None:

				if node is None:
								return MyNode(__A  )
				if data < node.get_data():
								node.set_left(insert_node(node.get_left()						,    __A  )  )
								if (
								    get_height(node.get_left()  ) - get_height(node.get_right()  ) == 2
								):  # an unbalance detected
												__a										=    node.get_left()
												assert left_child is not None
												if (
												    data < left_child.get_data()
												):  # new node is the left child of the left child
																__a										=    right_rotation(__A  )
												else:
																__a										=    lr_rotation(__A  )
				else:
								node.set_right(insert_node(node.get_right()						,    __A  )  )
								if get_height(node.get_right()  ) - get_height(node.get_left()  ) == 2:
												__a										=    node.get_right()
												assert right_child is not None
												if data < right_child.get_data():
																__a										=    rl_rotation(__A  )
												else:
																__a										=    left_rotation(__A  )
				__a										=    my_max(get_height(node.get_right()  )						,    get_height(node.get_left()  )  ) + 1
				node.set_height(__A  )
				return node


def     __lowerCAmelCase			(			a__  )       ->  Any:

				while True:
								__a										=    root.get_right()
								if right_child is None:
												break
								__a										=    right_child
				return root.get_data()


def     __lowerCAmelCase			(			a__  )       ->  Any:

				while True:
								__a										=    root.get_left()
								if left_child is None:
												break
								__a										=    left_child
				return root.get_data()


def     __lowerCAmelCase			(			a__						,    a__  )       ->  MyNode | None:
				__a										=    root.get_left()
				__a										=    root.get_right()
				if root.get_data() == data:
								if left_child is not None and right_child is not None:
												__a										=    get_left_most(__A  )
												root.set_data(__A  )
												root.set_right(del_node(__A						,    __A  )  )
								elif left_child is not None:
												__a										=    left_child
								elif right_child is not None:
												__a										=    right_child
								else:
												return None
				elif root.get_data() > data:
								if left_child is None:
												print('''No such data'''  )
												return root
								else:
												root.set_left(del_node(__A						,    __A  )  )
				else:  # root.get_data() < data
								if right_child is None:
												return root
								else:
												root.set_right(del_node(__A						,    __A  )  )

				if get_height(__A  ) - get_height(__A  ) == 2:
								assert right_child is not None
								if get_height(right_child.get_right()  ) > get_height(right_child.get_left()  ):
												__a										=    left_rotation(__A  )
								else:
												__a										=    rl_rotation(__A  )
				elif get_height(__A  ) - get_height(__A  ) == -2:
								assert left_child is not None
								if get_height(left_child.get_left()  ) > get_height(left_child.get_right()  ):
												__a										=    right_rotation(__A  )
								else:
												__a										=    lr_rotation(__A  )
				__a										=    my_max(get_height(root.get_right()  )						,    get_height(root.get_left()  )  ) + 1
				root.set_height(__A  )
				return root







class 			__A:




				def __init__(     self				)    ->      None:


								'''simple docstring'''




								__a										=    None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return get_height(self.root				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								print('''insert:''' + str(SCREAMING_SNAKE_CASE__				)				)
								__a										=    insert_node(self.root		,     SCREAMING_SNAKE_CASE__				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								print('''delete:''' + str(SCREAMING_SNAKE_CASE__				)				)
								if self.root is None:
												print('''Tree is empty!'''				)
												return
								__a										=    del_node(self.root		,     SCREAMING_SNAKE_CASE__				)




				def __str__(     self		,     )    ->      str:  # a level traversale, gives a more intuitive look on the tree


								'''simple docstring'''




								__a										=    ''
								__a										=    MyQueue()
								q.push(self.root				)
								__a										=    self.get_height()
								if layer == 0:
												return output
								__a										=    0
								while not q.is_empty():
												__a										=    q.pop()
												__a										=    ' ' * int(math.pow(2		,     layer - 1				)				)
												output += space
												if node is None:
																output += "*"
																q.push(SCREAMING_SNAKE_CASE__				)
																q.push(SCREAMING_SNAKE_CASE__				)
												else:
																output += str(node.get_data()				)
																q.push(node.get_left()				)
																q.push(node.get_right()				)
												output += space
												__a										=    cnt + 1
												for i in range(100				):
																if cnt == math.pow(2		,     SCREAMING_SNAKE_CASE__				) - 1:
																				__a										=    layer - 1
																				if layer == 0:
																								output += "\n*************************************"
																								return output
																				output += "\n"
																				break
								output += "\n*************************************"
								return output



def     __lowerCAmelCase			(			)       ->  None:
				import doctest

				doctest.testmod()


if __name__ == "__main__":
					_test()
					A     :	Dict    = AVLtree()
					A     :	Union[str, Any]    = list(range(1_0))
					random.shuffle(lst)
					for i in lst:
										t.insert(i)
										print(str(t))
					random.shuffle(lst)
					for i in lst:
										t.del_node(i)
										print(str(t))



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_      : List[str]  					=       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_      : str  					=       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_      : Dict  					=       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_      : List[str]  					=       OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

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

UpperCAmelCase_      : List[str]  					=       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[str]  					=       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_      : Union[str, Any]  					=       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_      : Dict  					=       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_      : List[str]  					=       OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

UpperCAmelCase_      : Union[str, Any]  					=       OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[int]  					=       _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_      : int  					=       _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_      : Tuple  					=       _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  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModel)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Any        =						FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[int]  					=       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[str]        =						FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_      : int  					=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_      : Dict  					=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						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'
)


