





from typing import List

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


__UpperCAmelCase	:       Tuple    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       Optional[int]    	=							{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}







class    __snake_case	(							SCREAMING_SNAKE_CASE__      ):





							'''simple docstring'''







							lowerCAmelCase__						=					'efficientformer'




							def __init__(  self			:   Tuple							,    A			:   List[int] = [3, 2, 6, 4]							,    A			:   List[int] = [48, 96, 224, 448]							,    A			:   List[bool] = [True, True, True, True]							,    A			:   int = 448							,    A			:   int = 32							,    A			:   int = 4							,    A			:   int = 7							,    A			:   int = 5							,    A			:   int = 8							,    A			:   int = 4							,    A			:   float = 0.0							,    A			:   int = 16							,    A			:   int = 3							,    A			:   int = 3							,    A			:   int = 3							,    A			:   int = 2							,    A			:   int = 1							,    A			:   float = 0.0							,    A			:   int = 1							,    A			:   bool = True							,    A			:   bool = True							,    A			:   float = 1E-5							,    A			:   str = "gelu"							,    A			:   float = 0.02							,    A			:   float = 1E-12							,    A			:   int = 224							,    A			:   float = 1E-05							,    **A			:   List[Any]							,    ):
									super().__init__(**a_		)

									__snake_case:						Dict			  =						hidden_act
									__snake_case:						Union[str, Any]			  =						hidden_dropout_prob
									__snake_case:						List[Any]			  =						hidden_sizes
									__snake_case:						Any			  =						num_hidden_layers
									__snake_case:						Optional[Any]			  =						num_attention_heads
									__snake_case:						Optional[Any]			  =						initializer_range
									__snake_case:						List[str]			  =						layer_norm_eps
									__snake_case:						Optional[Any]			  =						patch_size
									__snake_case:						Any			  =						num_channels
									__snake_case:						Optional[int]			  =						depths
									__snake_case:						List[Any]			  =						mlp_expansion_ratio
									__snake_case:						int			  =						downsamples
									__snake_case:						List[Any]			  =						dim
									__snake_case:						Dict			  =						key_dim
									__snake_case:						str			  =						attention_ratio
									__snake_case:						Any			  =						resolution
									__snake_case:						List[str]			  =						pool_size
									__snake_case:						List[str]			  =						downsample_patch_size
									__snake_case:						Tuple			  =						downsample_stride
									__snake_case:						Optional[Any]			  =						downsample_pad
									__snake_case:						List[str]			  =						drop_path_rate
									__snake_case:						Optional[Any]			  =						num_metaad_blocks
									__snake_case:						int			  =						distillation
									__snake_case:						List[str]			  =						use_layer_scale
									__snake_case:						str			  =						layer_scale_init_value
									__snake_case:						Optional[int]			  =						image_size
									__snake_case:						List[str]			  =						batch_norm_eps













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






