


'''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


__UpperCAmelCase     :int												=  logging.get_logger(__name__)

__UpperCAmelCase     :Dict												=  {
    "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"""






     SCREAMING_SNAKE_CASE			:					Any				  =  "vit-hybrid"

     def __init__(							self	:						Union[str, Any]				,					snake_case	:						Tuple=None				,					snake_case	:						str=768				,					snake_case	:						Union[str, Any]=12				,					snake_case	:						str=12				,					snake_case	:						Dict=3072				,					snake_case	:						Union[str, Any]="gelu"				,					snake_case	:						Optional[int]=0.0				,					snake_case	:						Any=0.0				,					snake_case	:						Optional[Any]=0.02				,					snake_case	:						Union[str, Any]=1E-12				,					snake_case	:						Any=224				,					snake_case	:						int=1				,					snake_case	:						int=3				,					snake_case	:						Any=[1, 1024, 24, 24]				,					snake_case	:						Optional[Any]=True				,					**snake_case	:						Optional[Any]				,					)		->				List[Any]:
          super().__init__(**snake_case     )

          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(snake_case				,					snake_case     ):
               if "model_type" in backbone_config:
                    __UpperCAmelCase      :					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      :					Tuple    				=     BitConfig
               __UpperCAmelCase      :					Tuple    				=     backbone_config_class(**snake_case     )

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






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



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)