




'''simple docstring'''







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


_lowerCamelCase							:						Optional[Any]								=  logging.get_logger(__name__)

_lowerCamelCase							:						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_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = """vit-hybrid"""






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=768,  __UpperCamelCase=12,  __UpperCamelCase=12,  __UpperCamelCase=3_072,  __UpperCamelCase="gelu",  __UpperCamelCase=0.0,  __UpperCamelCase=0.0,  __UpperCamelCase=0.02,  __UpperCamelCase=1E-12,  __UpperCamelCase=224,  __UpperCamelCase=1,  __UpperCamelCase=3,  __UpperCamelCase=[1, 1_024, 24, 24],  __UpperCamelCase=True,  **__UpperCamelCase,  ):
							super().__init__(**__UpperCamelCase )

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

							if isinstance(__UpperCamelCase,  __UpperCamelCase ):
									if "model_type" in backbone_config:
											_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." )
											_snake_case	       =       BitConfig
									_snake_case	       =       backbone_config_class(**__UpperCamelCase )

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






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











'''simple docstring'''







from functools import lru_cache






@lru_cache
def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if not isinstance(lowercase_						,							lowercase_  ):
				raise TypeError("k must be an integer."  )
		if k < 0:
				raise ValueError("k integer must be greater or equal to zero."  )
		if k == 0:
				return 0
		elif k == 1:
				return 1
		else:
				return fibonacci(k - 1  ) + fibonacci(k - 2  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       len(lowercase_  )
		# Find m such that F_m >= n where F_i is the i_th fibonacci number.
		_snake_case	       =       0
		while True:
				if fibonacci(lowercase_  ) >= len_list:
						_snake_case	       =       i
						break
				i += 1
		_snake_case	       =       0
		while fibb_k > 0:
				_snake_case	       =       min(
				    offset + fibonacci(fibb_k - 1  )						,							len_list - 1  )  # Prevent out of range
				_snake_case	       =       arr[index_k]
				if item_k_1 == val:
						return index_k
				elif val < item_k_1:
						fibb_k -= 1
				elif val > item_k_1:
						offset += fibonacci(fibb_k - 1  )
						fibb_k -= 2
		else:
				return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()





