




import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__SCREAMING_SNAKE_CASE     : Union[str, Any]      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : List[str]      ={
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class       A      (		_a		):
     """simple docstring"""

     _snake_case:						Optional[Any]  ="vit-hybrid"




     def __init__(     self	: Dict						,	A__	: List[Any]=None						,	A__	: Dict=7_6_8						,	A__	: int=1_2						,	A__	: Tuple=1_2						,	A__	: List[Any]=3_0_7_2						,	A__	: int="gelu"						,	A__	: Dict=0.0						,	A__	: Dict=0.0						,	A__	: Tuple=0.02						,	A__	: List[str]=1E-12						,	A__	: Tuple=2_2_4						,	A__	: List[str]=1						,	A__	: Optional[int]=3						,	A__	: Dict=[1, 1_0_2_4, 2_4, 2_4]						,	A__	: List[Any]=True						,	**A__	: Optional[int]						,	)					->							int:
           super().__init__(**A__      )

           if backbone_config is None:
                 logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'      )
                 lowerCamelCase__   	=					{
                     'global_padding': 'same',
                     'layer_type': 'bottleneck',
                     'depths': [3, 4, 9],
                     'out_features': ['stage3'],
                     'embedding_dynamic_padding': True,
                 }

           if isinstance(A__						,	A__      ):
                 if "model_type" in backbone_config:
                       lowerCamelCase__   	=					CONFIG_MAPPING[backbone_config['model_type']]
                 else:
                       logger.info(
                           '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'      )
                       lowerCamelCase__   	=					BitConfig
                 lowerCamelCase__   	=					backbone_config_class(**A__      )

           lowerCamelCase__   	=					backbone_featmap_shape
           lowerCamelCase__   	=					backbone_config
           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					patch_size
           lowerCamelCase__   	=					num_channels
           lowerCamelCase__   	=					qkv_bias




     def 			__snake_case (     self	: Optional[int]      )					->							Any:
           lowerCamelCase__   	=					copy.deepcopy(self.__dict__      )
           lowerCamelCase__   	=					self.backbone_config.to_dict()
           lowerCamelCase__   	=					self.__class__.model_type
           return output






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      ["""BartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BartForCausalLM""",
          """BartForConditionalGeneration""",
          """BartForQuestionAnswering""",
          """BartForSequenceClassification""",
          """BartModel""",
          """BartPretrainedModel""",
          """PretrainedBartModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """TFBartForConditionalGeneration""",
          """TFBartForSequenceClassification""",
          """TFBartModel""",
          """TFBartPretrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """FlaxBartDecoderPreTrainedModel""",
          """FlaxBartForCausalLM""",
          """FlaxBartForConditionalGeneration""",
          """FlaxBartForQuestionAnswering""",
          """FlaxBartForSequenceClassification""",
          """FlaxBartModel""",
          """FlaxBartPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      __lowercase				    =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
