



"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase       :					str      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Tuple      					=					{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''nat'''

		_lowerCamelCase			  =       {
		    '''num_attention_heads''': '''num_heads''',
		    '''num_hidden_layers''': '''num_layers''',
		}





		def __init__(					self							,    _lowercase=4							,    _lowercase=3							,    _lowercase=6_4							,    _lowercase=[3, 4, 6, 5]							,    _lowercase=[2, 4, 8, 1_6]							,    _lowercase=7							,    _lowercase=3.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=0.0							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Optional[Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

								snake_case_   : Any						=			patch_size
								snake_case_   : Optional[Any]						=			num_channels
								snake_case_   : Optional[Any]						=			embed_dim
								snake_case_   : Tuple						=			depths
								snake_case_   : int						=			len(_lowercase   )
								snake_case_   : Optional[int]						=			num_heads
								snake_case_   : List[str]						=			kernel_size
								snake_case_   : str						=			mlp_ratio
								snake_case_   : str						=			qkv_bias
								snake_case_   : str						=			hidden_dropout_prob
								snake_case_   : Tuple						=			attention_probs_dropout_prob
								snake_case_   : Tuple						=			drop_path_rate
								snake_case_   : Dict						=			hidden_act
								snake_case_   : Union[str, Any]						=			layer_norm_eps
								snake_case_   : Tuple						=			initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case_   : Union[str, Any]						=			int(embed_dim * 2 ** (len(_lowercase   ) - 1)   )
								snake_case_   : Union[str, Any]						=			layer_scale_init_value
								snake_case_   : Optional[Any]						=			["""stem"""] + [f'stage{idx}' for idx in range(1							,    len(_lowercase   ) + 1   )]
								snake_case_			,		snake_case_   : Any						=			get_aligned_output_features_output_indices(
								    out_features=_lowercase							,    out_indices=_lowercase							,    stage_names=self.stage_names   )






"""simple docstring"""





import math

import tensorflow as tf
from packaging import version
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0    )					, x.dtype    )    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : List[Any]						=			tf.cast(math.pi					, x.dtype    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi    ) * (x + coeff * tf.pow(__UpperCamelCase					, 3    ))    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			tf.convert_to_tensor(__UpperCamelCase    )

						return x * tf.tanh(tf.math.softplus(__UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			tf.cast(0.7_978_845_608					, x.dtype    )

						return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)    ))
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : str						=			tf.cast(1.702					, x.dtype    )
						return x * tf.math.sigmoid(coeff * x    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return tf.clip_by_value(_gelu(__UpperCamelCase    )					, -1_0					, 1_0    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[str]=-1    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : List[Any]						=			tf.split(__UpperCamelCase					, 2					, axis=__UpperCamelCase    )
						return a * tf.math.sigmoid(__UpperCamelCase    )


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
					def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


											'''simple docstring'''






											return tf.keras.activations.gelu(__UpperCamelCase					, approximate=__UpperCamelCase    )

					__lowerCAmelCase       :					int      					=					tf.keras.activations.gelu






					__lowerCAmelCase       :					Optional[Any]      					=					approximate_gelu_wrap
else:
					__lowerCAmelCase       :					List[Any]      					=					_gelu
					__lowerCAmelCase       :					Any      					=					_gelu_new


__lowerCAmelCase       :					Dict      					=					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}






def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if activation_string in ACTaFN:
												return ACTaFN[activation_string]
						else:
												raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()    )}'    )

