


'''simple docstring'''







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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Any												="""swinv2"""

  a_       :List[str]												={
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int=2_2_4				,							SCREAMING_SNAKE_CASE__     :							Dict=4				,							SCREAMING_SNAKE_CASE__     :							Dict=3				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=9_6				,							SCREAMING_SNAKE_CASE__     :							Dict=[2, 2, 6, 2]				,							SCREAMING_SNAKE_CASE__     :							Any=[3, 6, 1_2, 2_4]				,							SCREAMING_SNAKE_CASE__     :							int=7				,							SCREAMING_SNAKE_CASE__     :							str=4.0				,							SCREAMING_SNAKE_CASE__     :							str=True				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0.0				,							SCREAMING_SNAKE_CASE__     :							int=0.0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.1				,							SCREAMING_SNAKE_CASE__     :							List[str]="gelu"				,							SCREAMING_SNAKE_CASE__     :							str=False				,							SCREAMING_SNAKE_CASE__     :							str=0.0_2				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1E-5				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=3_2				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							):





       '''simple docstring'''

       super().__init__(**SCREAMING_SNAKE_CASE__						)

       __a       =   image_size
       __a       =   patch_size
       __a       =   num_channels
       __a       =   embed_dim
       __a       =   depths
       __a       =   len(SCREAMING_SNAKE_CASE__						)
       __a       =   num_heads
       __a       =   window_size
       __a       =   mlp_ratio
       __a       =   qkv_bias
       __a       =   hidden_dropout_prob
       __a       =   attention_probs_dropout_prob
       __a       =   drop_path_rate
       __a       =   hidden_act
       __a       =   use_absolute_embeddings
       __a       =   layer_norm_eps
       __a       =   initializer_range
       __a       =   encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __a       =   int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__						) - 1)						)
       __a       =   (0, 0, 0, 0)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






