





'''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__							:							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'''),
    ]
)

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

a__							:							Any =OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

a__							:							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'''),
    ]
)

a__							:							Optional[int] =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__							:							Optional[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__							:							int =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__							:							List[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__							:							List[str] =OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

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

a__							:							int =OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

a__							:							Optional[int] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a__							:							Tuple =_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[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a__							:							Tuple =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a__							:							Optional[int] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a__							:							Union[str, Any] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a__							:							List[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a__							:							List[str] =_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__							:							Dict =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a__							:							str =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a__							:							List[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a__							:							Union[str, Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=FLAX_MODEL_MAPPING






a__							:							List[Any] =auto_class_update(FlaxAutoModel)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=FLAX_MODEL_FOR_PRETRAINING_MAPPING






a__							:							List[Any] =auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






a__							:							Optional[int] =auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=FLAX_MODEL_FOR_MASKED_LM_MAPPING






a__							:							Tuple =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






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

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






a__							:							int =auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






a__							:							Any =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






a__							:							Optional[Any] =auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






a__							:							Tuple =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					=FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






a__							:							Optional[int] =auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






a__							:							Optional[Any] =auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






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

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING






a__							:							Tuple =auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE       :      int												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      List[str]												= {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			"""deformable_detr"""
       lowercase_ :	int     =			{
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(					self,      lowerCamelCase=True,      lowerCamelCase=None,      lowerCamelCase=3,      lowerCamelCase=3_00,      lowerCamelCase=10_24,      lowerCamelCase=6,      lowerCamelCase=10_24,      lowerCamelCase=8,      lowerCamelCase=6,      lowerCamelCase=10_24,      lowerCamelCase=8,      lowerCamelCase=0.0,      lowerCamelCase=True,      lowerCamelCase="relu",      lowerCamelCase=2_56,      lowerCamelCase=0.1,      lowerCamelCase=0.0,      lowerCamelCase=0.0,      lowerCamelCase=0.0_2,      lowerCamelCase=1.0,      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase="sine",      lowerCamelCase="resnet50",      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase=4,      lowerCamelCase=4,      lowerCamelCase=4,      lowerCamelCase=False,      lowerCamelCase=3_00,      lowerCamelCase=False,      lowerCamelCase=1,      lowerCamelCase=5,      lowerCamelCase=2,      lowerCamelCase=1,      lowerCamelCase=1,      lowerCamelCase=5,      lowerCamelCase=2,      lowerCamelCase=0.1,      lowerCamelCase=0.2_5,      lowerCamelCase=False,      **lowerCamelCase,      )       ->   Optional[int]:




           """simple docstring"""

           if backbone_config is not None and use_timm_backbone:
               raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

           if not use_timm_backbone:
               if backbone_config is None:
                   logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
                   _lowercase       :							List[str]      		=		CONFIG_MAPPING['resnet'](out_features=['stage4'])
               elif isinstance(lowerCamelCase,      lowerCamelCase):
                   _lowercase       :							List[str]      		=		backbone_config.get('model_type')
                   _lowercase       :							str      		=		CONFIG_MAPPING[backbone_model_type]
                   _lowercase       :							Optional[int]      		=		config_class.from_dict(lowerCamelCase)
           _lowercase       :							Tuple      		=		use_timm_backbone
           _lowercase       :							List[str]      		=		backbone_config
           _lowercase       :							Tuple      		=		num_channels
           _lowercase       :							Optional[Any]      		=		num_queries
           _lowercase       :							Optional[Any]      		=		max_position_embeddings
           _lowercase       :							Optional[int]      		=		d_model
           _lowercase       :							int      		=		encoder_ffn_dim
           _lowercase       :							List[Any]      		=		encoder_layers
           _lowercase       :							str      		=		encoder_attention_heads
           _lowercase       :							str      		=		decoder_ffn_dim
           _lowercase       :							Optional[Any]      		=		decoder_layers
           _lowercase       :							List[str]      		=		decoder_attention_heads
           _lowercase       :							Optional[int]      		=		dropout
           _lowercase       :							Optional[Any]      		=		attention_dropout
           _lowercase       :							int      		=		activation_dropout
           _lowercase       :							Any      		=		activation_function
           _lowercase       :							Optional[int]      		=		init_std
           _lowercase       :							int      		=		init_xavier_std
           _lowercase       :							Union[str, Any]      		=		encoder_layerdrop
           _lowercase       :							Tuple      		=		auxiliary_loss
           _lowercase       :							Union[str, Any]      		=		position_embedding_type
           _lowercase       :							str      		=		backbone
           _lowercase       :							List[Any]      		=		use_pretrained_backbone
           _lowercase       :							Any      		=		dilation
           # deformable attributes
           _lowercase       :							Any      		=		num_feature_levels
           _lowercase       :							Dict      		=		encoder_n_points
           _lowercase       :							Dict      		=		decoder_n_points
           _lowercase       :							Dict      		=		two_stage
           _lowercase       :							Union[str, Any]      		=		two_stage_num_proposals
           _lowercase       :							str      		=		with_box_refine
           if two_stage is True and with_box_refine is False:
               raise ValueError('If two_stage is True, with_box_refine must be True.')
           # Hungarian matcher
           _lowercase       :							Tuple      		=		class_cost
           _lowercase       :							int      		=		bbox_cost
           _lowercase       :							Optional[int]      		=		giou_cost
           # Loss coefficients
           _lowercase       :							Optional[Any]      		=		mask_loss_coefficient
           _lowercase       :							Dict      		=		dice_loss_coefficient
           _lowercase       :							Tuple      		=		bbox_loss_coefficient
           _lowercase       :							Optional[int]      		=		giou_loss_coefficient
           _lowercase       :							Union[str, Any]      		=		eos_coefficient
           _lowercase       :							Union[str, Any]      		=		focal_alpha
           _lowercase       :							Dict      		=		disable_custom_kernels
           super().__init__(is_encoder_decoder=lowerCamelCase,      **lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return self.encoder_attention_heads
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return self.d_model




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		copy.deepcopy(self.__dict__)
           if self.backbone_config is not None:
               _lowercase       :							Union[str, Any]      		=		self.backbone_config.to_dict()
           _lowercase       :							Tuple      		=		self.__class__.model_type
           return output


