


'''simple docstring'''




def   _a		(lowercase__			:   float							,   lowercase__			:   float						)			->			float:






     """simple docstring"""
     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'						)
     return 0.5 * mass * abs(lowercase__						) * abs(lowercase__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)











"""simple docstring"""




import functools
import operator

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


a		:					List[Any]	      =     logging.get_logger(__name__)

a		:					List[Any]	      =     {
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Tuple    ="""unispeech-sat"""






							def __init__(  self ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=768 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=3072 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=1E-5 ,   lowerCAmelCase__="group" ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512) ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2) ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2) ,   lowerCAmelCase__=False ,   lowerCAmelCase__=128 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=False ,   lowerCAmelCase__=True ,   lowerCAmelCase__=0.05 ,   lowerCAmelCase__=10 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=10 ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=320 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=100 ,   lowerCAmelCase__=256 ,   lowerCAmelCase__=256 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__="mean" ,   lowerCAmelCase__=False ,   lowerCAmelCase__=False ,   lowerCAmelCase__=256 ,   lowerCAmelCase__=(512, 512, 512, 512, 1500) ,   lowerCAmelCase__=(5, 3, 3, 1, 1) ,   lowerCAmelCase__=(1, 2, 3, 1, 1) ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=1 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=504 ,   **lowerCAmelCase__ ,   )      ->					List[str]:
								super().__init__(**lowerCAmelCase__ ,   pad_token_id=lowerCAmelCase__ ,   bos_token_id=lowerCAmelCase__ ,   eos_token_id=lowerCAmelCase__ )
								a :						Any      =							hidden_size
								a :						Tuple      =							feat_extract_norm
								a :						Tuple      =							feat_extract_activation
								a :						Dict      =							list(lowerCAmelCase__ )
								a :						int      =							list(lowerCAmelCase__ )
								a :						Optional[Any]      =							list(lowerCAmelCase__ )
								a :						int      =							conv_bias
								a :						str      =							num_conv_pos_embeddings
								a :						Dict      =							num_conv_pos_embedding_groups
								a :						Optional[int]      =							len(self.conv_dim )
								a :						int      =							num_hidden_layers
								a :						Any      =							intermediate_size
								a :						Any      =							hidden_act
								a :						List[Any]      =							num_attention_heads
								a :						Any      =							hidden_dropout
								a :						Union[str, Any]      =							attention_dropout
								a :						Tuple      =							activation_dropout
								a :						Dict      =							feat_proj_dropout
								a :						Optional[Any]      =							final_dropout
								a :						Union[str, Any]      =							layerdrop
								a :						str      =							layer_norm_eps
								a :						Optional[int]      =							initializer_range
								a :						Optional[int]      =							vocab_size
								a :						str      =							num_clusters
								a :						Any      =							do_stable_layer_norm
								a :						str      =							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
								a :						List[Any]      =							apply_spec_augment
								a :						int      =							mask_time_prob
								a :						Optional[int]      =							mask_time_length
								a :						Dict      =							mask_time_min_masks
								a :						Optional[int]      =							mask_feature_prob
								a :						List[str]      =							mask_feature_length
								a :						Dict      =							mask_feature_min_masks

								# parameters for pretraining with codevector quantized representations
								a :						List[str]      =							num_codevectors_per_group
								a :						List[Any]      =							num_codevector_groups
								a :						Tuple      =							contrastive_logits_temperature
								a :						int      =							feat_quantizer_dropout
								a :						Optional[Any]      =							num_negatives
								a :						Optional[int]      =							codevector_dim
								a :						Tuple      =							proj_codevector_dim
								a :						Optional[int]      =							diversity_loss_weight

								# ctc loss
								a :						Dict      =							ctc_loss_reduction
								a :						Optional[Any]      =							ctc_zero_infinity

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

								# XVector-specific parameters. Feel free to ignore for other classes.
								a :						Union[str, Any]      =							list(lowerCAmelCase__ )
								a :						List[str]      =							list(lowerCAmelCase__ )
								a :						Tuple      =							list(lowerCAmelCase__ )
								a :						Optional[int]      =							xvector_output_dim






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


