'''simple docstring'''




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


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}




class    a__				(       lowercase_			):
					lowerCamelCase  :					Any							="speech_to_text"
					lowerCamelCase  :					List[str]							=["past_key_values"]
					lowerCamelCase  :					List[str]							={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






					def __init__(			self		:		Tuple  ,    a		:		int=1_00_00  ,    a		:		Union[str, Any]=12  ,    a		:		Union[str, Any]=20_48  ,    a		:		Tuple=4  ,    a		:		List[str]=6  ,    a		:		Optional[int]=20_48  ,    a		:		int=4  ,    a		:		int=0.0  ,    a		:		List[str]=0.0  ,    a		:		int=True  ,    a		:		Any=True  ,    a		:		Optional[Any]="relu"  ,    a		:		Optional[int]=2_56  ,    a		:		str=0.1  ,    a		:		str=0.0  ,    a		:		Optional[Any]=0.0  ,    a		:		List[Any]=0.02  ,    a		:		Union[str, Any]=2  ,    a		:		str=True  ,    a		:		List[str]=1  ,    a		:		Optional[int]=0  ,    a		:		Optional[int]=2  ,    a		:		Any=60_00  ,    a		:		Optional[int]=10_24  ,    a		:		Optional[Any]=2  ,    a		:		Optional[Any]=(5, 5)  ,    a		:		List[str]=10_24  ,    a		:		int=80  ,    a		:		List[str]=1  ,    **a		:		str  ,    ):





									"""simple docstring"""



									__lowerCamelCase          =  vocab_size
									__lowerCamelCase          =  d_model
									__lowerCamelCase          =  encoder_ffn_dim
									__lowerCamelCase          =  encoder_layers
									__lowerCamelCase          =  encoder_attention_heads
									__lowerCamelCase          =  decoder_ffn_dim
									__lowerCamelCase          =  decoder_layers
									__lowerCamelCase          =  decoder_attention_heads
									__lowerCamelCase          =  dropout
									__lowerCamelCase          =  attention_dropout
									__lowerCamelCase          =  activation_dropout
									__lowerCamelCase          =  activation_function
									__lowerCamelCase          =  init_std
									__lowerCamelCase          =  encoder_layerdrop
									__lowerCamelCase          =  decoder_layerdrop
									__lowerCamelCase          =  use_cache
									__lowerCamelCase          =  encoder_layers
									__lowerCamelCase          =  scale_embedding  # scale factor will be sqrt(d_model) if True
									__lowerCamelCase          =  max_source_positions
									__lowerCamelCase          =  max_target_positions
									__lowerCamelCase          =  num_conv_layers
									__lowerCamelCase          =  list(a__   )
									__lowerCamelCase          =  conv_channels
									__lowerCamelCase          =  input_feat_per_channel
									__lowerCamelCase          =  input_channels

									if len(self.conv_kernel_sizes   ) != self.num_conv_layers:
													raise ValueError(
													    '''Configuration for convolutional module is incorrect. '''
													    '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
													    f"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes   )}`, """
													    f"""`config.num_conv_layers = {self.num_conv_layers}`."""   )

									super().__init__(
									    pad_token_id=a__  ,    bos_token_id=a__  ,    eos_token_id=a__  ,    is_encoder_decoder=a__  ,    decoder_start_token_id=a__  ,    **a__  ,    )

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

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

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

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
