






"""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


_UpperCamelCase		:  Tuple						=		logging.get_logger(__name__)


_UpperCamelCase		:  int						=		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		:  List[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'),
    ]
)

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

_UpperCamelCase		:  Dict						=		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

_UpperCamelCase		:  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'),
    ]
)

_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		:  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'),
    ]
)

_UpperCamelCase		:  List[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		:  Optional[int]						=		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		:  Union[str, Any]						=		OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

_UpperCamelCase		:  List[str]						=		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

_UpperCamelCase		:  Tuple						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_UpperCamelCase		:  List[Any]						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_UpperCamelCase		:  int						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_UpperCamelCase		:  Optional[Any]						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_UpperCamelCase		:  Tuple						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase		:  List[str]						=		_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		:  Optional[int]						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase		:  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		:  Optional[Any]						=		_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		:  Optional[Any]						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_UpperCamelCase		:  List[Any]						=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     int	     =FLAX_MODEL_MAPPING


_UpperCamelCase		:  Tuple						=		auto_class_update(FlaxAutoModel)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     str	     =FLAX_MODEL_FOR_PRETRAINING_MAPPING


_UpperCamelCase		:  Optional[Any]						=		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     str	     =FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


_UpperCamelCase		:  Any						=		auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Tuple	     =FLAX_MODEL_FOR_MASKED_LM_MAPPING


_UpperCamelCase		:  Optional[int]						=		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Optional[Any]	     =FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


_UpperCamelCase		:  int						=		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     List[str]	     =FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


_UpperCamelCase		:  Optional[Any]						=		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Dict	     =FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


_UpperCamelCase		:  List[str]						=		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Union[str, Any]	     =FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


_UpperCamelCase		:  List[Any]						=		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     List[str]	     =FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


_UpperCamelCase		:  Union[str, Any]						=		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Dict	     =FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


_UpperCamelCase		:  Union[str, Any]						=		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Optional[int]	     =FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


_UpperCamelCase		:  Any						=		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     Tuple	     =FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


_UpperCamelCase		:  Union[str, Any]						=		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')

class        a     (   _BaseAutoModelClass	):
    UpperCAmelCase_ :     List[Any]	     =FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


_UpperCamelCase		:  Any						=		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)










from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




