code: import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       int						 =							'hybrid-clip'
      UpperCAmelCase_			:       List[str]						 =							True
  
  
  
  
      def __init__(					self  :   Tuple      ,      lowerCAmelCase  :   List[Any]=512      ,      **lowerCAmelCase  :   Union[str, Any]   )	-> List[Any]:
        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`."   )
  
        a__     		=     kwargs.pop("text_config"   )
        a__     		=     kwargs.pop("vision_config"   )
  
        a__     		=     text_config.pop("model_type"   )
        a__     		=     vision_config.pop("model_type"   )
  
        from transformers import AutoConfig
  
        a__     		=     AutoConfig.for_model(lowerCAmelCase      ,      **lowerCAmelCase   )
  
        if vision_model_type == "clip":
          a__     		=     AutoConfig.for_model(lowerCAmelCase      ,      **lowerCAmelCase   ).vision_config
        elif vision_model_type == "clip_vision_model":
          from transformers import CLIPVisionConfig
  
          a__     		=     CLIPVisionConfig(**lowerCAmelCase   )
        else:
          a__     		=     AutoConfig.for_model(lowerCAmelCase      ,      **lowerCAmelCase   )
  
        a__     		=     projection_dim
        a__     		=     1.0
  
  
  
  
      @classmethod
      def 						__lowerCamelCase (					cls  :   Dict      ,      lowerCAmelCase  :   PretrainedConfig      ,      lowerCAmelCase  :   PretrainedConfig      ,      **lowerCAmelCase  :   Union[str, Any]   )	-> Tuple:
        return cls(text_config=text_config.to_dict()      ,      vision_config=vision_config.to_dict()      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> Optional[int]:
        a__     		=     copy.deepcopy(self.__dict__   )
        a__     		=     self.text_config.to_dict()
        a__     		=     self.vision_config.to_dict()
        a__     		=     self.__class__.model_type
        return output
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  