code: 
  
  
  
  
  
  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
  
  
  lowercase_			       =				logging.get_logger(__name__)
  
  lowercase_			       =				{
      """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'''
      UpperCamelCase									=				"vit-hybrid"
  
  
      def __init__(	self				,	A=None				,	A=768				,	A=12				,	A=12				,	A=3072				,	A="gelu"				,	A=0.0				,	A=0.0				,	A=0.02				,	A=1e-12				,	A=224				,	A=1				,	A=3				,	A=[1, 1024, 24, 24]				,	A=True				,	**A				,	)	->     Tuple:
          super().__init__(**SCREAMING_SNAKE_CASE_ )
  
          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""" )
              _SCREAMING_SNAKE_CASE             =    {
                  'global_padding': 'same',
                  'layer_type': 'bottleneck',
                  'depths': [3, 4, 9],
                  'out_features': ['stage3'],
                  'embedding_dynamic_padding': True,
              }
  
          if isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_ ):
              if "model_type" in backbone_config:
                  _SCREAMING_SNAKE_CASE             =    CONFIG_MAPPING[backbone_config['model_type']]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""" )
                  _SCREAMING_SNAKE_CASE             =    BitConfig
              _SCREAMING_SNAKE_CASE             =    backbone_config_class(**SCREAMING_SNAKE_CASE_ )
  
          _SCREAMING_SNAKE_CASE             =    backbone_featmap_shape
          _SCREAMING_SNAKE_CASE             =    backbone_config
          _SCREAMING_SNAKE_CASE             =    hidden_size
          _SCREAMING_SNAKE_CASE             =    num_hidden_layers
          _SCREAMING_SNAKE_CASE             =    num_attention_heads
          _SCREAMING_SNAKE_CASE             =    intermediate_size
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
          _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE             =    initializer_range
          _SCREAMING_SNAKE_CASE             =    layer_norm_eps
          _SCREAMING_SNAKE_CASE             =    image_size
          _SCREAMING_SNAKE_CASE             =    patch_size
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    qkv_bias
  
  
  
  
  
      def     snake_case_(	self )	->     Dict[str, any]:
          _SCREAMING_SNAKE_CASE             =    copy.deepcopy(self.__dict__ )
          _SCREAMING_SNAKE_CASE             =    self.backbone_config.to_dict()
          _SCREAMING_SNAKE_CASE             =    self.__class__.model_type
          return output
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    {
              """model_details""": {
                  """Organization""": """testing""",
                  """Model date""": """today""",
                  """Model version""": """v2.1, Developed by Test Corp in 2019.""",
                  """Architecture""": """Convolutional Neural Network.""",
              },
              """metrics""": """BLEU and ROUGE-1""",
              """evaluation_data""": {
                  """Datasets""": {"""BLEU""": """My-great-dataset-v1""", """ROUGE-1""": """My-short-dataset-v2.1"""},
                  """Preprocessing""": """See details on https://arxiv.org/pdf/1810.03993.pdf""",
              },
              """training_data""": {
                  """Dataset""": """English Wikipedia dump dated 2018-12-01""",
                  """Preprocessing""": (
                      """Using SentencePiece vocabulary of size 52k tokens. See details on"""
                      """ https://arxiv.org/pdf/1810.03993.pdf"""
                  ),
              },
              """quantitative_analyses""": {"""BLEU""": 55.1, """ROUGE-1""": 76},
          }
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          self.assertTrue(hasattr(A				,	"""model_details""" ) )
          self.assertTrue(hasattr(A				,	"""intended_use""" ) )
          self.assertTrue(hasattr(A				,	"""factors""" ) )
          self.assertTrue(hasattr(A				,	"""metrics""" ) )
          self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
          self.assertTrue(hasattr(A				,	"""training_data""" ) )
          self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
          self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
          self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
          for key, value in self.inputs_dict.items():
              self.assertEqual(obj[key]				,	A )
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	"""modelcard.json""" )
              model_card_first.to_json_file(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_json_file(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              model_card_first.save_pretrained(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_pretrained(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  