





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Tuple							=			logging.get_logger(__name__)

_snake_case					:		int							=			{
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''opt'''
					UpperCamelCase								=							['''past_key_values''']


					def __init__( self      :Any					,	__UpperCamelCase      :Union[str, Any]=5_02_72					,	__UpperCamelCase      :List[Any]=7_68					,	__UpperCamelCase      :int=12					,	__UpperCamelCase      :List[str]=30_72					,	__UpperCamelCase      :Optional[int]=20_48					,	__UpperCamelCase      :Dict=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :Union[str, Any]=None					,	__UpperCamelCase      :Any=0.1					,	__UpperCamelCase      :Dict=0.0					,	__UpperCamelCase      :Optional[int]=12					,	__UpperCamelCase      :str="relu"					,	__UpperCamelCase      :Dict=0.0					,	__UpperCamelCase      :Dict=0.02					,	__UpperCamelCase      :Union[str, Any]=True					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=2					,	__UpperCamelCase      :Optional[Any]=2					,	__UpperCamelCase      :Dict=True					,	__UpperCamelCase      :int=True					,	**__UpperCamelCase      :Dict					,	):
												super().__init__(
												    pad_token_id=__UpperCamelCase					,	bos_token_id=__UpperCamelCase					,	eos_token_id=__UpperCamelCase					,	**__UpperCamelCase					,	)
												A		=  vocab_size
												A		=  max_position_embeddings
												A		=  num_attention_heads
												A		=  word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
												A		=  ffn_dim
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_function
												A		=  init_std
												A		=  layerdrop
												A		=  use_cache
												A		=  do_layer_norm_before
												# We keep these variables at `True` for backward compatibility.
												A		=  enable_bias
												A		=  layer_norm_elementwise_affine

												# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
												# with checkpoints that have been fine-tuned before transformers v4.20.1
												# see https://github.com/facebookresearch/metaseq/pull/164
												A		=  _remove_final_layer_norm









"""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()


