





"""simple docstring"""
import functools
import operator

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


_snake_case					:		int							=			logging.get_logger(__name__)

_snake_case					:		Tuple							=			{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}






class  _UpperCAmelCase       (     lowerCamelCase_			):
					UpperCamelCase								=							"""hubert"""


					def __init__( self      :List[str]					,	__UpperCamelCase      :int=32					,	__UpperCamelCase      :List[Any]=7_68					,	__UpperCamelCase      :List[Any]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :str=30_72					,	__UpperCamelCase      :str="gelu"					,	__UpperCamelCase      :Tuple=0.1					,	__UpperCamelCase      :List[str]=0.1					,	__UpperCamelCase      :int=0.1					,	__UpperCamelCase      :List[str]=True					,	__UpperCamelCase      :str=0.0					,	__UpperCamelCase      :List[str]=0.1					,	__UpperCamelCase      :Tuple=0.1					,	__UpperCamelCase      :Union[str, Any]=0.02					,	__UpperCamelCase      :Optional[Any]=1e-5					,	__UpperCamelCase      :List[Any]="group"					,	__UpperCamelCase      :Optional[int]="gelu"					,	__UpperCamelCase      :List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)					,	__UpperCamelCase      :Optional[int]=(5, 2, 2, 2, 2, 2, 2)					,	__UpperCamelCase      :Any=(10, 3, 3, 3, 3, 2, 2)					,	__UpperCamelCase      :Optional[int]=False					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Dict=16					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :Optional[int]=True					,	__UpperCamelCase      :Optional[int]=0.05					,	__UpperCamelCase      :Any=10					,	__UpperCamelCase      :List[Any]=2					,	__UpperCamelCase      :Optional[Any]=0.0					,	__UpperCamelCase      :List[str]=10					,	__UpperCamelCase      :Optional[Any]=0					,	__UpperCamelCase      :Dict="sum"					,	__UpperCamelCase      :Any=False					,	__UpperCamelCase      :int=False					,	__UpperCamelCase      :Optional[Any]=2_56					,	__UpperCamelCase      :List[Any]=0					,	__UpperCamelCase      :Union[str, Any]=1					,	__UpperCamelCase      :int=2					,	**__UpperCamelCase      :List[str]					,	):
												super().__init__(**__UpperCamelCase					,	pad_token_id=__UpperCamelCase					,	bos_token_id=__UpperCamelCase					,	eos_token_id=__UpperCamelCase		)
												A		=  hidden_size
												A		=  feat_extract_norm
												A		=  feat_extract_activation
												A		=  list(__UpperCamelCase		)
												A		=  list(__UpperCamelCase		)
												A		=  list(__UpperCamelCase		)
												A		=  conv_bias
												A		=  num_conv_pos_embeddings
												A		=  num_conv_pos_embedding_groups
												A		=  len(self.conv_dim		)
												A		=  num_hidden_layers
												A		=  intermediate_size
												A		=  hidden_act
												A		=  num_attention_heads
												A		=  hidden_dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  feat_proj_layer_norm
												A		=  feat_proj_dropout
												A		=  final_dropout
												A		=  layerdrop
												A		=  layer_norm_eps
												A		=  initializer_range
												A		=  vocab_size
												A		=  do_stable_layer_norm
												A		=  use_weighted_layer_sum
												A		=  classifier_proj_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)`, 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		=  apply_spec_augment
												A		=  mask_time_prob
												A		=  mask_time_length
												A		=  mask_time_min_masks
												A		=  mask_feature_prob
												A		=  mask_feature_length
												A		=  mask_feature_min_masks

												# ctc loss
												A		=  ctc_loss_reduction
												A		=  ctc_zero_infinity


					@property
					def      lowerCamelCase	( self      :Optional[int]		):
												return functools.reduce(operator.mul					,	self.conv_stride					,	1		)









"""simple docstring"""
import numpy as np
import requests






def  A__    (      ):
							A		=  requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"	)
							A		=  response.text.splitlines()
							A		=  []
							for item in lines:
														A		=  item.split(","	)
														data.append(UpperCamelCase	)
							data.pop(0	)  # This is for removing the labels from the list
							A		=  np.matrix(UpperCamelCase	)
							return dataset






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  len_data

							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.dot(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta - (alpha / n) * sum_grad
							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.sum(np.square(UpperCamelCase	)	)
							A		=  sum_elem / (2 * len_data)
							return error






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							A		=  0.0_00_15_50

							A		=  data_x.shape[1]
							A		=  data_x.shape[0] - 1

							A		=  np.zeros((1, no_features)	)

							for i in range(0 ,  UpperCamelCase	):
														A		=  run_steep_gradient_descent(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														A		=  sum_of_square_error(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														print(F"At Iteration {i + 1} - Error is {error:.5f}"	)

							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  sum(abs(y - predicted_y[i]	) for i, y in enumerate(UpperCamelCase	)	)
							return total / len(UpperCamelCase	)






def  A__    (      ):
							A		=  collect_dataset()

							A		=  data.shape[0]
							A		=  np.c_[np.ones(UpperCamelCase	), data[:, :-1]].astype(UpperCamelCase	)
							A		=  data[:, -1].astype(UpperCamelCase	)

							A		=  run_linear_regression(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta.shape[1]
							print("Resultant Feature vector : "	)
							for i in range(0 ,  UpperCamelCase	):
														print(F"{theta[0, i]:.5f}"	)


if __name__ == "__main__":
		main()


