






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 	a_(		lowercase__			,							lowercase__	):



     """simple docstring"""



     __snake_case		:				str  					='''convnext'''





     def __init__(		self					:	List[str]  ,  lowerCAmelCase__					:	List[Any]=3  ,  lowerCAmelCase__					:	Any=4  ,  lowerCAmelCase__					:	int=4  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[str]=None  ,  lowerCAmelCase__					:	int="gelu"  ,  lowerCAmelCase__					:	List[str]=0.02  ,  lowerCAmelCase__					:	Optional[int]=1e-12  ,  lowerCAmelCase__					:	List[str]=1e-6  ,  lowerCAmelCase__					:	List[Any]=0.0  ,  lowerCAmelCase__					:	Optional[int]=2_2_4  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	List[Any]=None  ,  **lowerCAmelCase__					:	Union[str, Any]  ,  )							->	Union[str, Any]:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		patch_size
      SCREAMING_SNAKE_CASE				    =		num_stages
      SCREAMING_SNAKE_CASE				    =		[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE				    =		[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps
      SCREAMING_SNAKE_CASE				    =		layer_scale_init_value
      SCREAMING_SNAKE_CASE				    =		drop_path_rate
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		['stem'] + [f'''stage{idx}''' for idx in range(1  ,  len(self.depths) + 1)]
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)

class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Optional[int]  					=version.parse('''1.11'''	)





     @property
     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Mapping[str, Mapping[int, str]]:

      """simple docstring"""
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ])





     @property
     def     __UpperCamelCase						(		self					:	Dict)							->	float:

      """simple docstring"""
      return 1e-5





'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






