
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A     :	List[str]    = logging.get_logger(__name__)

A     :	Optional[Any]    = {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}







class 			__A( lowercase__			):
				snake_case_						   =    '''table-transformer'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}




				def __init__(     self		,     _snake_case=True		,     _snake_case=None		,     _snake_case=3		,     _snake_case=100		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case="relu"		,     _snake_case=256		,     _snake_case=0.1		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=1.0		,     _snake_case=False		,     _snake_case="sine"		,     _snake_case="resnet50"		,     _snake_case=True		,     _snake_case=False		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=1		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=0.1		,     **_snake_case		,     )    ->      Tuple:


								'''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.'''				)
																__a										=    CONFIG_MAPPING['resnet'](out_features=['''stage4''']				)
												elif isinstance(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__				):
																__a										=    backbone_config.get('''model_type'''				)
																__a										=    CONFIG_MAPPING[backbone_model_type]
																__a										=    config_class.from_dict(SCREAMING_SNAKE_CASE__				)
												# set timm attributes to None
												__a										=    None, None, None

								__a										=    use_timm_backbone
								__a										=    backbone_config
								__a										=    num_channels
								__a										=    num_queries
								__a										=    d_model
								__a										=    encoder_ffn_dim
								__a										=    encoder_layers
								__a										=    encoder_attention_heads
								__a										=    decoder_ffn_dim
								__a										=    decoder_layers
								__a										=    decoder_attention_heads
								__a										=    dropout
								__a										=    attention_dropout
								__a										=    activation_dropout
								__a										=    activation_function
								__a										=    init_std
								__a										=    init_xavier_std
								__a										=    encoder_layerdrop
								__a										=    decoder_layerdrop
								__a										=    encoder_layers
								__a										=    auxiliary_loss
								__a										=    position_embedding_type
								__a										=    backbone
								__a										=    use_pretrained_backbone
								__a										=    dilation
								# Hungarian matcher
								__a										=    class_cost
								__a										=    bbox_cost
								__a										=    giou_cost
								# Loss coefficients
								__a										=    mask_loss_coefficient
								__a										=    dice_loss_coefficient
								__a										=    bbox_loss_coefficient
								__a										=    giou_loss_coefficient
								__a										=    eos_coefficient
								super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__		,     **SCREAMING_SNAKE_CASE__				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.encoder_attention_heads




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.d_model







class 			__A( lowercase__			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								        ('''pixel_mask''', {0: '''batch'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-5




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return 12



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'
)


