


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'
)






import copy
from typing import Dict, List, Optional

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


UpperCAmelCase_      : Optional[int]  					=       {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  CONFIG_MAPPING['swin'](
                        image_size=2_2_4     ,							in_channels=3     ,							patch_size=4     ,							embed_dim=9_6     ,							depths=[2, 2, 1_8, 2]     ,							num_heads=[3, 6, 1_2, 2_4]     ,							window_size=7     ,							drop_path_rate=0.3     ,							use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  backbone_config.pop('model_type'     )
                    a_					:		Optional[Any]							=  CONFIG_MAPPING[backbone_model_type]
                    a_					:		List[str]							=  config_class.from_dict(SCREAMING_SNAKE_CASE__     )

             # verify that the backbone is supported
             if backbone_config.model_type not in self.backbones_supported:
                    logger.warning_once(
                        F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
                        F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

             a_					:		Dict							=  backbone_config
             a_					:		List[str]							=  feature_size
             a_					:		List[str]							=  mask_feature_size
             a_					:		int							=  hidden_dim
             a_					:		Dict							=  encoder_feedforward_dim
             a_					:		str							=  activation_function
             a_					:		List[str]							=  encoder_layers
             a_					:		List[str]							=  decoder_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		str							=  dropout
             a_					:		Tuple							=  dim_feedforward
             a_					:		List[str]							=  pre_norm
             a_					:		Optional[int]							=  enforce_input_projection
             a_					:		Any							=  common_stride
             a_					:		Optional[int]							=  ignore_value
             a_					:		int							=  num_queries
             a_					:		Tuple							=  no_object_weight
             a_					:		Dict							=  class_weight
             a_					:		Optional[int]							=  mask_weight
             a_					:		Optional[int]							=  dice_weight
             a_					:		str							=  train_num_points
             a_					:		List[str]							=  oversample_ratio
             a_					:		List[Any]							=  importance_sample_ratio
             a_					:		Any							=  init_std
             a_					:		Union[str, Any]							=  init_xavier_std
             a_					:		Union[str, Any]							=  use_auxiliary_loss
             a_					:		Dict							=  feature_strides
             a_					:		List[str]							=  output_auxiliary_logits
             a_					:		Dict							=  decoder_layers

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


