




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase       =     {
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase       =     [
       """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MobileViTV2ForImageClassification""",
       """MobileViTV2ForSemanticSegmentation""",
       """MobileViTV2Model""",
       """MobileViTV2PreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevitva import (
          MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTVaForImageClassification,
          MobileViTVaForSemanticSegmentation,
          MobileViTVaModel,
          MobileViTVaPreTrainedModel,
      )

else:
   import sys

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








"""simple docstring"""
import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			int         =   'hybrid-clip'
  UpperCamelCase		:			Optional[int]         =   True



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   Any=512    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)

     if "text_config" not in kwargs:
        raise ValueError("""`text_config` can not be `None`."""						)

     if "vision_config" not in kwargs:
        raise ValueError("""`vision_config` can not be `None`."""						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =kwargs.pop("""text_config"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =kwargs.pop("""vision_config"""						)

     SCREAMING_SNAKE_CASE_:	int            =text_config.pop("""model_type"""						)
     SCREAMING_SNAKE_CASE_:	str            =vision_config.pop("""model_type"""						)

     from transformers import AutoConfig

     SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     if vision_model_type == "clip":
        SCREAMING_SNAKE_CASE_:	Tuple            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						).vision_config
     elif vision_model_type == "clip_vision_model":
        from transformers import CLIPVisionConfig

        SCREAMING_SNAKE_CASE_:	List[str]            =CLIPVisionConfig(**lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Any            =projection_dim
     SCREAMING_SNAKE_CASE_:	int            =1.0



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   PretrainedConfig    ,						lowerCAmelCase	:   PretrainedConfig    ,						**lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**lowerCAmelCase						)





  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =copy.deepcopy(self.__dict__						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.text_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.__class__.model_type
     return output



