


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


SCREAMING_SNAKE_CASE			: Dict				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "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 _lowerCamelCase(		_a  ):
       lowercase_					:      Union[str, Any]       					=     """speech_to_text"""
       lowercase_					:      List[Any]       					=     ["""past_key_values"""]
       lowercase_					:      Optional[Any]       					=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(   self, lowerCamelCase=1_00_00, lowerCamelCase=12, lowerCamelCase=20_48, lowerCamelCase=4, lowerCamelCase=6, lowerCamelCase=20_48, lowerCamelCase=4, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=2, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, lowerCamelCase=60_00, lowerCamelCase=10_24, lowerCamelCase=2, lowerCamelCase=(5, 5), lowerCamelCase=10_24, lowerCamelCase=80, lowerCamelCase=1, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						vocab_size
           _lowercase			:				Optional[int] 						=						d_model
           _lowercase			:				Any 						=						encoder_ffn_dim
           _lowercase			:				Tuple 						=						encoder_layers
           _lowercase			:				Tuple 						=						encoder_attention_heads
           _lowercase			:				Optional[Any] 						=						decoder_ffn_dim
           _lowercase			:				Optional[int] 						=						decoder_layers
           _lowercase			:				str 						=						decoder_attention_heads
           _lowercase			:				Union[str, Any] 						=						dropout
           _lowercase			:				str 						=						attention_dropout
           _lowercase			:				List[str] 						=						activation_dropout
           _lowercase			:				Optional[Any] 						=						activation_function
           _lowercase			:				int 						=						init_std
           _lowercase			:				str 						=						encoder_layerdrop
           _lowercase			:				List[str] 						=						decoder_layerdrop
           _lowercase			:				List[str] 						=						use_cache
           _lowercase			:				Tuple 						=						encoder_layers
           _lowercase			:				List[str] 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
           _lowercase			:				str 						=						max_source_positions
           _lowercase			:				List[str] 						=						max_target_positions
           _lowercase			:				Dict 						=						num_conv_layers
           _lowercase			:				Tuple 						=						list(lowerCamelCase)
           _lowercase			:				str 						=						conv_channels
           _lowercase			:				int 						=						input_feat_per_channel
           _lowercase			:				str 						=						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=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, is_encoder_decoder=lowerCamelCase, decoder_start_token_id=lowerCamelCase, **lowerCamelCase, )






from maths.prime_factors import prime_factors



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        _lowercase			:				str 						=						F'''Input value of [number={number}] must be an integer'''
        raise TypeError(lowerCamelCase_					)
    if number < 1:
        raise ValueError('Input must be a positive integer'					)
    return -1 if len(prime_factors(lowerCamelCase_					)					) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()


