





import math

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


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Union[str, Any]       =	"""data2vec-audio"""






		def __init__(							self,   snake_case__=32,   snake_case__=7_68,   snake_case__=12,   snake_case__=12,   snake_case__=30_72,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=0.1,   snake_case__=0.0,   snake_case__=0.1,   snake_case__=0.1,   snake_case__=0.02,   snake_case__=1E-5,   snake_case__="gelu",   snake_case__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12),   snake_case__=(5, 2, 2, 2, 2, 2, 2),   snake_case__=(10, 3, 3, 3, 3, 2, 2),   snake_case__=False,   snake_case__=16,   snake_case__=19,   snake_case__=5,   snake_case__=0.05,   snake_case__=10,   snake_case__=2,   snake_case__=0.0,   snake_case__=10,   snake_case__=0,   snake_case__="sum",   snake_case__=False,   snake_case__=False,   snake_case__=2_56,   snake_case__=(5_12, 5_12, 5_12, 5_12, 15_00),   snake_case__=(5, 3, 3, 1, 1),   snake_case__=(1, 2, 3, 1, 1),   snake_case__=5_12,   snake_case__=0,   snake_case__=1,   snake_case__=2,   snake_case__=False,   snake_case__=3,   snake_case__=2,   snake_case__=3,   snake_case__=None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									super().__init__(**snake_case__,   pad_token_id=snake_case__,   bos_token_id=snake_case__,   eos_token_id=snake_case__  )
									lowercase_	:   List[Any]  				=     hidden_size
									lowercase_	:   int  				=     feat_extract_activation
									lowercase_	:   str  				=     list(snake_case__  )
									lowercase_	:   Optional[Any]  				=     list(snake_case__  )
									lowercase_	:   str  				=     list(snake_case__  )
									lowercase_	:   int  				=     conv_bias
									lowercase_	:   int  				=     num_conv_pos_embeddings
									lowercase_	:   Optional[int]  				=     num_conv_pos_embedding_groups
									lowercase_	:   List[str]  				=     conv_pos_kernel_size
									lowercase_	:   List[str]  				=     len(self.conv_dim  )
									lowercase_	:   Dict  				=     num_hidden_layers
									lowercase_	:   Tuple  				=     intermediate_size
									lowercase_	:   str  				=     hidden_act
									lowercase_	:   str  				=     num_attention_heads
									lowercase_	:   Optional[int]  				=     hidden_dropout
									lowercase_	:   List[Any]  				=     attention_dropout
									lowercase_	:   Any  				=     activation_dropout
									lowercase_	:   Union[str, Any]  				=     feat_proj_dropout
									lowercase_	:   int  				=     final_dropout
									lowercase_	:   Union[str, Any]  				=     layerdrop
									lowercase_	:   List[Any]  				=     layer_norm_eps
									lowercase_	:   Optional[int]  				=     initializer_range
									lowercase_	:   Any  				=     vocab_size
									lowercase_	:   Union[str, Any]  				=     use_weighted_layer_sum

									if (
									    (len(self.conv_stride  ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
									):
																raise ValueError(
																    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																    f""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
																    f""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									lowercase_	:   List[str]  				=     mask_time_prob
									lowercase_	:   Optional[int]  				=     mask_time_length
									lowercase_	:   List[Any]  				=     mask_time_min_masks
									lowercase_	:   List[Any]  				=     mask_feature_prob
									lowercase_	:   Tuple  				=     mask_feature_length
									lowercase_	:   int  				=     mask_feature_min_masks

									# ctc loss
									lowercase_	:   int  				=     ctc_loss_reduction
									lowercase_	:   Tuple  				=     ctc_zero_infinity

									# adapter
									lowercase_	:   Union[str, Any]  				=     add_adapter
									lowercase_	:   int  				=     adapter_kernel_size
									lowercase_	:   Optional[Any]  				=     adapter_stride
									lowercase_	:   Union[str, Any]  				=     num_adapter_layers
									lowercase_	:   int  				=     output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									lowercase_	:   str  				=     classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									lowercase_	:   Optional[Any]  				=     list(snake_case__  )
									lowercase_	:   Any  				=     list(snake_case__  )
									lowercase_	:   Tuple  				=     list(snake_case__  )
									lowercase_	:   int  				=     xvector_output_dim






		@property
		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									return math.prod(self.conv_stride  )






def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if n_term == "":
														return []
							lowercase_	:   list  				=     []
							for temp in range(int(lowercase				)				):
														series.append(f"""1/{temp + 1}""" if series else """1"""				)
							return series


if __name__ == "__main__":
							UpperCAmelCase_							=				input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))