






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


__A          = logging.get_logger(__name__)

__A          = {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "regnet"
							A_       =    ["x", "y"]

							def __init__(     self:      Union[str, Any]						,   __A:      int=3						,   __A:      str=32						,   __A:      Optional[Any]=[1_28, 1_92, 5_12, 10_88]						,   __A:      str=[2, 6, 12, 2]						,   __A:      Optional[Any]=64						,   __A:      Tuple="y"						,   __A:      Tuple="relu"						,   **__A:      Union[str, Any]						,   )			-> Optional[int]:
												super().__init__(**__A      )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types      )}"""      )
												_A									=       num_channels
												_A									=       embedding_size
												_A									=       hidden_sizes
												_A									=       depths
												_A									=       groups_width
												_A									=       layer_type
												_A									=       hidden_act
												# always downsample in the first stage
												_A									=       True











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


__A          = logging.get_logger(__name__)

__A          = {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_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									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



