






from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__       =     logging.get_logger(__name__)

a__       =     {
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Tuple      =  '''swinv2'''

				_lowercase				:   str      =  {
				    '''num_attention_heads''': '''num_heads''',
				    '''num_hidden_layers''': '''num_layers''',
				}



				def __init__(      self    :    str			,		UpperCamelCase__    :    Dict=2_2_4			,		UpperCamelCase__    :    List[Any]=4			,		UpperCamelCase__    :    Tuple=3			,		UpperCamelCase__    :    List[str]=9_6			,		UpperCamelCase__    :    str=[2, 2, 6, 2]			,		UpperCamelCase__    :    Tuple=[3, 6, 1_2, 2_4]			,		UpperCamelCase__    :    List[str]=7			,		UpperCamelCase__    :    str=4.0			,		UpperCamelCase__    :    List[str]=True			,		UpperCamelCase__    :    Tuple=0.0			,		UpperCamelCase__    :    Tuple=0.0			,		UpperCamelCase__    :    Optional[int]=0.1			,		UpperCamelCase__    :    Any="gelu"			,		UpperCamelCase__    :    List[Any]=False			,		UpperCamelCase__    :    Tuple=0.02			,		UpperCamelCase__    :    Optional[int]=1E-5			,		UpperCamelCase__    :    Dict=3_2			,		**UpperCamelCase__    :    str			,		):






											'''simple docstring'''







											super().__init__(**UpperCamelCase__)

											snake_case__	  =       image_size
											snake_case__	  =       patch_size
											snake_case__	  =       num_channels
											snake_case__	  =       embed_dim
											snake_case__	  =       depths
											snake_case__	  =       len(UpperCamelCase__)
											snake_case__	  =       num_heads
											snake_case__	  =       window_size
											snake_case__	  =       mlp_ratio
											snake_case__	  =       qkv_bias
											snake_case__	  =       hidden_dropout_prob
											snake_case__	  =       attention_probs_dropout_prob
											snake_case__	  =       drop_path_rate
											snake_case__	  =       hidden_act
											snake_case__	  =       use_absolute_embeddings
											snake_case__	  =       layer_norm_eps
											snake_case__	  =       initializer_range
											snake_case__	  =       encoder_stride
											# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											snake_case__	  =       int(embed_dim * 2 ** (len(UpperCamelCase__) - 1))
											snake_case__	  =       (0, 0, 0, 0)








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


if __name__ == "__main__":
	print(F'''{solution() = }''')
