



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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''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       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"vit-hybrid"


   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     str=None  ,lowerCamelCase_:     Union[str, Any]=768  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     List[Any]=12  ,lowerCamelCase_:     str=3072  ,lowerCamelCase_:     List[Any]="gelu"  ,lowerCamelCase_:     Any=0.0  ,lowerCamelCase_:     int=0.0  ,lowerCamelCase_:     int=0.0_2  ,lowerCamelCase_:     Tuple=1e-12  ,lowerCamelCase_:     Optional[int]=224  ,lowerCamelCase_:     Optional[int]=1  ,lowerCamelCase_:     List[Any]=3  ,lowerCamelCase_:     int=[1, 1024, 24, 24]  ,lowerCamelCase_:     Dict=True  ,**lowerCamelCase_:     Tuple  ,)	->      List[Any]:
       super().__init__(**lowerCamelCase_							)

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

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

       UpperCAmelCase_     :       Optional[Any]  		=					backbone_featmap_shape
       UpperCAmelCase_     :       Optional[Any]  		=					backbone_config
       UpperCAmelCase_     :       Dict  		=					hidden_size
       UpperCAmelCase_     :       Optional[Any]  		=					num_hidden_layers
       UpperCAmelCase_     :       List[str]  		=					num_attention_heads
       UpperCAmelCase_     :       List[str]  		=					intermediate_size
       UpperCAmelCase_     :       Dict  		=					hidden_act
       UpperCAmelCase_     :       Dict  		=					hidden_dropout_prob
       UpperCAmelCase_     :       Union[str, Any]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       int  		=					initializer_range
       UpperCAmelCase_     :       Optional[int]  		=					layer_norm_eps
       UpperCAmelCase_     :       List[Any]  		=					image_size
       UpperCAmelCase_     :       Optional[int]  		=					patch_size
       UpperCAmelCase_     :       List[Any]  		=					num_channels
       UpperCAmelCase_     :       str  		=					qkv_bias






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





'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[str]            =      [
          'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MarianForCausalLM',
          'MarianModel',
          'MarianMTModel',
          'MarianPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Optional[Any]            =      ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Any            =      ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
      from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_marian import MarianTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_marian import (
                MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarianForCausalLM,
                MarianModel,
                MarianMTModel,
                MarianPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
      import sys

      lowercase__  :	Any            =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




