




'''simple docstring'''





from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


_SCREAMING_SNAKE_CASE							:       Dict						=			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE							:       Tuple						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Tuple						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Tuple						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Tuple						=			OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

_SCREAMING_SNAKE_CASE							:       int						=			OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Dict						=			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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       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"),
    ]
)

_SCREAMING_SNAKE_CASE							:       str						=			OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

_SCREAMING_SNAKE_CASE							:       int						=			OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

_SCREAMING_SNAKE_CASE							:       Dict						=			OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

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




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Tuple							=   FLAX_MODEL_MAPPING

_SCREAMING_SNAKE_CASE							:       int						=			auto_class_update(FlaxAutoModel)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Union[str, Any]							=   FLAX_MODEL_FOR_PRETRAINING_MAPPING

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Tuple							=   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Tuple							=   FLAX_MODEL_FOR_MASKED_LM_MAPPING

_SCREAMING_SNAKE_CASE							:       str						=			auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Any							=   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_SCREAMING_SNAKE_CASE							:       Dict						=			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Optional[int]							=   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

_SCREAMING_SNAKE_CASE							:       int						=			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Tuple							=   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

_SCREAMING_SNAKE_CASE							:       Any						=			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				str							=   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

_SCREAMING_SNAKE_CASE							:       List[Any]						=			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Optional[Any]							=   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

_SCREAMING_SNAKE_CASE							:       List[str]						=			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Tuple							=   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

_SCREAMING_SNAKE_CASE							:       Any						=			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Union[str, Any]							=   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				Any							=   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

_SCREAMING_SNAKE_CASE							:       Dict						=			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class 			_snake_case		(     _BaseAutoModelClass							):
			lowerCAmelCase_					:				List[str]							=   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






							while b:
														snake_case_ ,				snake_case_      =				b, a % b
							return a


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






							return a if b == 0 else euclidean_gcd_recursive(snake_case							,    a % b					)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3							,    5					)}'					)
							print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5							,    3					)}'					)
							print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1							,    3					)}'					)
							print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3							,    6					)}'					)
							print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6							,    3					)}'					)

							print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3							,    5					)}'					)
							print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5							,    3					)}'					)
							print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1							,    3					)}'					)
							print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3							,    6					)}'					)
							print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6							,    3					)}'					)


if __name__ == "__main__":
		main()




