





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


__A       : str						    = logging.get_logger(__name__)

__A       : Dict						    = {
    "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     lowerCamelCase(	a__  ):
     '''simple docstring'''


     __magic_name__        = "vit-hybrid"




     def __init__(						self							,    snake_case_=None							,    snake_case_=768							,    snake_case_=12							,    snake_case_=12							,    snake_case_=3072							,    snake_case_="gelu"							,    snake_case_=0.0							,    snake_case_=0.0							,    snake_case_=0.02							,    snake_case_=1E-12							,    snake_case_=224							,    snake_case_=1							,    snake_case_=3							,    snake_case_=[1, 1024, 24, 24]							,    snake_case_=True							,    **snake_case_							,    ):
         super().__init__(**SCREAMING_SNAKE_CASE_       )

         if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'       )
             _A    =       {
                 '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:
                 _A    =       CONFIG_MAPPING[backbone_config['model_type']]
             else:
                 logger.info(
                     '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'       )
                 _A    =       BitConfig
             _A    =       backbone_config_class(**SCREAMING_SNAKE_CASE_       )

         _A    =       backbone_featmap_shape
         _A    =       backbone_config
         _A    =       hidden_size
         _A    =       num_hidden_layers
         _A    =       num_attention_heads
         _A    =       intermediate_size
         _A    =       hidden_act
         _A    =       hidden_dropout_prob
         _A    =       attention_probs_dropout_prob
         _A    =       initializer_range
         _A    =       layer_norm_eps
         _A    =       image_size
         _A    =       patch_size
         _A    =       num_channels
         _A    =       qkv_bias




     def 					lowerCAmelCase__		(						self       ):
         _A    =       copy.deepcopy(self.__dict__       )
         _A    =       self.backbone_config.to_dict()
         _A    =       self.__class__.model_type
         return output












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





