






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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    '''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_      (  UpperCamelCase__     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Tuple	=   "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							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**__lowerCamelCase)

     if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.''')
         _UpperCAmelCase			: Optional[int]     					=			{
             "global_padding": "same",
             "layer_type": "bottleneck",
             "depths": [3, 4, 9],
             "out_features": ["stage3"],
             "embedding_dynamic_padding": True,
         }

     if isinstance(__lowerCamelCase							,					__lowerCamelCase):
         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			: str     					=			BitConfig
         _UpperCAmelCase			: Tuple     					=			backbone_config_class(**__lowerCamelCase)

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


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			copy.deepcopy(self.__dict__)
     _UpperCAmelCase			: Optional[int]     					=			self.backbone_config.to_dict()
     _UpperCAmelCase			: str     					=			self.__class__.model_type
     return output












from unittest.mock import patch

import datasets
from datasets import Dataset

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Union[str, Any]     					=			Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}	)

    with patch('''tqdm.auto.tqdm'''	) as mock_tqdm:
        datasets.disable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_not_called()

        mock_tqdm.reset_mock()

        datasets.enable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_called()




