

"""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


a					:Optional[Any]    = logging.get_logger(__name__)


a					:Any    = 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"),
    ]
)

a					:Optional[int]    = 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"),
    ]
)

a					:Union[str, 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"),
    ]
)

a					:Union[str, 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"),
    ]
)

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

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

a					: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"),
    ]
)

a					: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"),
    ]
)

a					:int    = 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"),
    ]
)

a					:List[str]    = 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"),
    ]
)

a					:Any    = 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"),
    ]
)

a					:Optional[int]    = OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

a					:List[Any]    = OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

a					:List[Any]    = OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

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

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = FLAX_MODEL_MAPPING





a					:Union[str, Any]    = auto_class_update(FlaxAutoModel)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = FLAX_MODEL_FOR_PRETRAINING_MAPPING





a					:Dict    = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING





a					:List[str]    = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = FLAX_MODEL_FOR_MASKED_LM_MAPPING





a					:Union[str, Any]    = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





a					:Optional[int]    = auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





a					:str    = auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:int       = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING





a					:Optional[int]    = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING





a					:str    = auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING





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

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:str       = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING





a					:Tuple    = auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





a					:Dict    = auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING





a					:Optional[int]    = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")

class      __a							(_BaseAutoModelClass):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING





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







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




