


# Imports
import numpy as np
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     )		->			Union[str, Any]:
             self.set_matricies(red=SCREAMING_SNAKE_CASE__     ,							green=SCREAMING_SNAKE_CASE__     ,							blue=SCREAMING_SNAKE_CASE__     ,							red_edge=SCREAMING_SNAKE_CASE__     ,							nir=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Tuple=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       int=None     )		->			List[Any]:
             if red is not None:
                    a_					:		int							=  red
             if green is not None:
                    a_					:		Any							=  green
             if blue is not None:
                    a_					:		int							=  blue
             if red_edge is not None:
                    a_					:		List[str]							=  red_edge
             if nir is not None:
                    a_					:		int							=  nir
             return True
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=""     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       int=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     )		->			List[str]:
             self.set_matricies(red=SCREAMING_SNAKE_CASE__     ,							green=SCREAMING_SNAKE_CASE__     ,							blue=SCREAMING_SNAKE_CASE__     ,							red_edge=SCREAMING_SNAKE_CASE__     ,							nir=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  {
                 'ARVI2': self.arvaa,
                 'CCCI': self.ccci,
                 'CVI': self.cvi,
                 'GLI': self.gli,
                 'NDVI': self.ndvi,
                 'BNDVI': self.bndvi,
                 'redEdgeNDVI': self.red_edge_ndvi,
                 'GNDVI': self.gndvi,
                 'GBNDVI': self.gbndvi,
                 'GRNDVI': self.grndvi,
                 'RBNDVI': self.rbndvi,
                 'PNDVI': self.pndvi,
                 'ATSAVI': self.atsavi,
                 'BWDRVI': self.bwdrvi,
                 'CIgreen': self.ci_green,
                 'CIrededge': self.ci_rededge,
                 'CI': self.ci,
                 'CTVI': self.ctvi,
                 'GDVI': self.gdvi,
                 'EVI': self.evi,
                 'GEMI': self.gemi,
                 'GOSAVI': self.gosavi,
                 'GSAVI': self.gsavi,
                 'Hue': self.hue,
                 'IVI': self.ivi,
                 'IPVI': self.ipvi,
                 'I': self.i,
                 'RVI': self.rvi,
                 'MRVI': self.mrvi,
                 'MSAVI': self.m_savi,
                 'NormG': self.norm_g,
                 'NormNIR': self.norm_nir,
                 'NormR': self.norm_r,
                 'NGRDI': self.ngrdi,
                 'RI': self.ri,
                 'S': self.s,
                 'IF': self._if,
                 'DVI': self.dvi,
                 'TVI': self.tvi,
                 'NDRE': self.ndre,
             }

             try:
                    return funcs[index]()
             except KeyError:
                    print('Index not in the list!'     )
                    return False
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			int:
             return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
                 (self.nir - self.red) / (self.nir + self.red)
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             return self.nir * (self.red / (self.green**2))
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Any:
             return (2 * self.green - self.red - self.blue) / (
                 2 * self.green + self.red + self.blue
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             return (self.nir - self.red) / (self.nir + self.red)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Dict:
             return (self.nir - self.blue) / (self.nir + self.blue)
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Any:
             return (self.redEdge - self.red) / (self.redEdge + self.red)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             return (self.nir - self.green) / (self.nir + self.green)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             return (self.nir - (self.green + self.blue)) / (
                 self.nir + (self.green + self.blue)
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			int:
             return (self.nir - (self.green + self.red)) / (
                 self.nir + (self.green + self.red)
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Union[str, Any]:
             return (self.nir - (self.green + self.red + self.blue)) / (
                 self.nir + (self.green + self.red + self.blue)
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       str=0.08     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1.22     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.03     )		->			List[Any]:
             return a * (
                 (self.nir - a * self.red - b)
                 / (a * self.nir + self.red - a * b + x * (1 + a**2))
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			int:
             return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             return (self.nir / self.green) - 1
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             return (self.nir / self.redEdge) - 1
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[Any]:
             return (self.red - self.blue) / self.red
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[str]:
             a_					:		List[Any]							=  self.ndvi()
             return ((ndvi + 0.5) / (abs(ndvi + 0.5     ))) * (abs(ndvi + 0.5     ) ** (1 / 2))
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             return self.nir - self.green
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             return 2.5 * (
                 (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             a_					:		Union[str, Any]							=  (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
                 self.nir + self.red + 0.5
             )
             return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       str=0.16     )		->			int:
             return (self.nir - self.green) / (self.nir + self.green + y)
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.5     )		->			Optional[Any]:
             return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             return np.arctan(
                 ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     )		->			Union[str, Any]:
             return (self.nir - b) / (a * self.red)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[Any]:
             return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[str]:
             return (self.red + self.green + self.blue) / 30.5
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Union[str, Any]:
             return self.nir / self.red
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Dict:
             return (self.rvi() - 1) / (self.rvi() + 1)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             return (
                 (2 * self.nir + 1)
                 - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
             ) / 2
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             return self.green / (self.nir + self.red + self.green)
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Tuple:
             return self.nir / (self.nir + self.red + self.green)
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             return self.red / (self.nir + self.red + self.green)
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[Any]:
             return (self.green - self.red) / (self.green + self.red)
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             return (self.red - self.green) / (self.red + self.green)
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[Any]							=  np.max([np.max(self.red     ), np.max(self.green     ), np.max(self.blue     )]     )
             a_					:		Tuple							=  np.min([np.min(self.red     ), np.min(self.green     ), np.min(self.blue     )]     )
             return (max_value - min_value) / max_value
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[Any]:
             return self.nir / self.red
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             return (self.ndvi() + 0.5) ** (1 / 2)

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Any:
             return (self.nir - self.redEdge) / (self.nir + self.redEdge)






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'
)


