'''simple docstring'''





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


a_					      = logging.get_logger(__name__)

a_					      = {
    '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 	_lowercase		(      snake_case_ ):
 lowercase         =     'vit-hybrid'

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

  """simple docstring"""





  super().__init__(**snake_case    )

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

  if isinstance(snake_case     ,   snake_case    ):
   if "model_type" in backbone_config:
    UpperCamelCase_      :		Dict				=      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_      :		Tuple				=      BitConfig
   UpperCamelCase_      :		List[str]				=      backbone_config_class(**snake_case    )

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


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Dict[str, any]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      copy.deepcopy(self.__dict__    )
  UpperCamelCase_      :		int				=      self.backbone_config.to_dict()
  UpperCamelCase_      :		Union[str, Any]				=      self.__class__.model_type
  return output




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,
)


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

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

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['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

   a_					      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



