




'''simple docstring'''


from __future__ import annotations

class       A   :







							def __init__(  self	:  List[Any]							,      lowerCAmelCase_	:  int )      ->							None:


														"""simple docstring"""


														_a     					=    order

														# a_{0} ... a_{k}
														_a     					=    [1.0] + [0.0] * order
														# b_{0} ... b_{k}
														_a     					=    [1.0] + [0.0] * order

														# x[n-1] ... x[n-k]
														_a     					=    [0.0] * self.order
														# y[n-1] ... y[n-k]
														_a     					=    [0.0] * self.order







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  list[float]							,      lowerCAmelCase_	:  list[float] )      ->							None:


														"""simple docstring"""


														if len(lowerCAmelCase_ ) < self.order:
																					_a     					=    [1.0, *a_coeffs]

														if len(lowerCAmelCase_ ) != self.order + 1:
																					_a     					=    (
																					    F'Expected a_coeffs to have {self.order + 1} elements '
																					    F'for {self.order}-order filter, got {len(lowerCAmelCase_ )}'
																					)
																					raise ValueError(lowerCAmelCase_ )

														if len(lowerCAmelCase_ ) != self.order + 1:
																					_a     					=    (
																					    F'Expected b_coeffs to have {self.order + 1} elements '
																					    F'for {self.order}-order filter, got {len(lowerCAmelCase_ )}'
																					)
																					raise ValueError(lowerCAmelCase_ )

														_a     					=    a_coeffs
														_a     					=    b_coeffs







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  float )      ->							float:


														"""simple docstring"""


														_a     					=    0.0

														# Start at index 1 and do index 0 at the end.
														for i in range(1							,      self.order + 1 ):
																					result += (
																					    self.b_coeffs[i] * self.input_history[i - 1]
																					    - self.a_coeffs[i] * self.output_history[i - 1]
																					)

														_a     					=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

														_a     					=    self.input_history[:-1]
														_a     					=    self.output_history[:-1]

														_a     					=    sample
														_a     					=    result

														return result







'''simple docstring'''


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


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    '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       A   (		_a					,_a   ):
							lowercase_          =    'nat'

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







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    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
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

