'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''maskformer-swin'''

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







     def __init__(		self     ,			_lowerCamelCase=2_2_4     ,			_lowerCamelCase=4     ,			_lowerCamelCase=3     ,			_lowerCamelCase=9_6     ,			_lowerCamelCase=[2, 2, 6, 2]     ,			_lowerCamelCase=[3, 6, 1_2, 2_4]     ,			_lowerCamelCase=7     ,			_lowerCamelCase=4.0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-5     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )

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




'''simple docstring'''
from timeit import timeit

a=  {
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	str		    =  len(_a			) - 1
    while start_i < end_i:
        if s[start_i] == s[end_i]:
            start_i += 1
            end_i -= 1
        else:
            return False
    return True







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  len(_a			) // 2
    __UpperCamelCase	:	Tuple		    =  len(_a			)

    # We need to traverse till half of the length of string
    # as we can get access of the i'th last element from
    # i'th index.
    # eg: [0,1,2,3,4,5] => 4th index can be accessed
    # with the help of 1st index (i==n-i-1)
    # where n is length of string
    return all(s[i] == s[n - i - 1] for i in range(_a			)			)







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    if len(_a			) <= 2:
        return True
    if s[0] == s[len(_a			) - 1]:
        return is_palindrome_recursive(s[1:-1]			)
    else:
        return False







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    return s == s[::-1]







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  f"""all({name}(key) is value for key, value in test_data.items())"""
    __UpperCamelCase	:	Union[str, Any]		    =  f"""from __main__ import test_data, {name}"""
    __UpperCamelCase	:	Any		    =  5_0_0_0_0_0
    __UpperCamelCase	:	Tuple		    =  timeit(stmt=_a				,     setup=_a				,     number=_a			)
    print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""			)


if __name__ == "__main__":
  for key, value in test_data.items():
    assert is_palindrome(key) is is_palindrome_recursive(key)
    assert is_palindrome(key) is is_palindrome_slice(key)
    print(F"""{key:21} {value}""")
  print('''a man a plan a canal panama''')

  # finished 500,000 runs in 0.46793 seconds
  benchmark_function('''is_palindrome_slice''')
  # finished 500,000 runs in 0.85234 seconds
  benchmark_function('''is_palindrome''')
  # finished 500,000 runs in 1.32028 seconds
  benchmark_function('''is_palindrome_recursive''')
  # finished 500,000 runs in 2.08679 seconds
  benchmark_function('''is_palindrome_traversal''')



