code: 
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''hybrid-clip'''
   lowerCamelCase_          =			True
   def __init__(						self	,    a=5_1_2	,    **a					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__(**a					)
  
     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(a	,    **a					)
  
     if vision_model_type == "clip":
       _A           =    AutoConfig.for_model(a	,    **a					).vision_config
     elif vision_model_type == "clip_vision_model":
       from transformers import CLIPVisionConfig
  
       _A           =    CLIPVisionConfig(**a					)
     else:
       _A           =    AutoConfig.for_model(a	,    **a					)
  
     _A           =    projection_dim
     _A           =    1.0
   @classmethod
   def lowercase_		(						cls	,    a	,    a	,    **a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return cls(text_config=text_config.to_dict()	,    vision_config=vision_config.to_dict()	,    **a					)
  
  
  
   def lowercase_		(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _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: 
  
  
  
  import platform
  from argparse import ArgumentParser
  
  import huggingface_hub
  import pandas
  import pyarrow
  
  from datasets import __version__ as version
  from datasets.commands import BaseDatasetsCLICommand
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
    return EnvironmentCommand()
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parser.add_parser('''env'''	,    help='''Print relevant system environment info.'''					)
     download_parser.set_defaults(func=a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {
         '''`datasets` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Huggingface_hub version''': huggingface_hub.__version__,
         '''PyArrow version''': pyarrow.__version__,
         '''Pandas version''': pandas.__version__,
     }
  
     print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
     print(self.format_dict(a					)					)
  
     return info
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"