





"""simple docstring"""




import functools
import operator

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


a						: Tuple     	=			logging.get_logger(__name__)

a						: List[Any]     	=			{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					="""sew-d"""






							def __init__(   self      ,					lowerCAmelCase__=32      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__=2      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=256      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=("p2c", "c2p")      ,					lowerCAmelCase__="layer_norm"      ,					lowerCAmelCase__="gelu_python"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=1E-7      ,					lowerCAmelCase__=1E-5      ,					lowerCAmelCase__="group"      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,					lowerCAmelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,					lowerCAmelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=0.05      ,					lowerCAmelCase__=10      ,					lowerCAmelCase__=2      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=10      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__="mean"      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=256      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=2      ,					**lowerCAmelCase__      ,					)	-> int:
								super().__init__(**lowerCAmelCase__      ,					pad_token_id=lowerCAmelCase__      ,					bos_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      )
								a      :		Optional[Any]	=				hidden_size
								a      :		int	=				feat_extract_norm
								a      :		Any	=				feat_extract_activation
								a      :		Optional[int]	=				list(lowerCAmelCase__      )
								a      :		Union[str, Any]	=				list(lowerCAmelCase__      )
								a      :		int	=				list(lowerCAmelCase__      )
								a      :		List[str]	=				conv_bias
								a      :		Union[str, Any]	=				num_conv_pos_embeddings
								a      :		str	=				num_conv_pos_embedding_groups
								a      :		List[Any]	=				len(self.conv_dim      )
								a      :		int	=				num_hidden_layers
								a      :		Optional[int]	=				intermediate_size
								a      :		int	=				squeeze_factor
								a      :		int	=				max_position_embeddings
								a      :		List[Any]	=				position_buckets
								a      :		Union[str, Any]	=				share_att_key
								a      :		str	=				relative_attention
								a      :		Optional[Any]	=				norm_rel_ebd
								a      :		Union[str, Any]	=				list(lowerCAmelCase__      )
								a      :		Tuple	=				hidden_act
								a      :		str	=				num_attention_heads
								a      :		Optional[int]	=				hidden_dropout
								a      :		Optional[int]	=				attention_dropout
								a      :		str	=				activation_dropout
								a      :		str	=				feat_proj_dropout
								a      :		Optional[int]	=				final_dropout
								a      :		List[str]	=				layer_norm_eps
								a      :		Dict	=				feature_layer_norm_eps
								a      :		Any	=				initializer_range
								a      :		List[str]	=				vocab_size

								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)`,"
									    f"""but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)"""
									    f"""= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`."""      )

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								a      :		Union[str, Any]	=				apply_spec_augment
								a      :		Tuple	=				mask_time_prob
								a      :		Union[str, Any]	=				mask_time_length
								a      :		List[Any]	=				mask_time_min_masks
								a      :		str	=				mask_feature_prob
								a      :		Dict	=				mask_feature_length
								a      :		Tuple	=				mask_feature_min_masks

								# ctc loss
								a      :		List[str]	=				ctc_loss_reduction
								a      :		Optional[int]	=				ctc_zero_infinity

								# sequence classification
								a      :		List[Any]	=				use_weighted_layer_sum
								a      :		Tuple	=				classifier_proj_size






							@property
							def     __a   (   self      )	-> Dict:
								return functools.reduce(operator.mul      ,					self.conv_stride      ,					1      )









"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


