




import numpy as np
import requests






def 					snake_case					(							)							->							int:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A						,   A						,   A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							   =							len_data

	lowerCamelCase_							:		Dict							   =							np.dot(A						,   data_x.transpose()			)
	prod -= data_y.transpose()
	lowerCamelCase_							:		Dict							   =							np.dot(A						,   A			)
	lowerCamelCase_							:		Optional[int]							   =							theta - (alpha / n) * sum_grad
	return theta






def 					snake_case					(							A						,   A						,   A						,   A			)							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							np.dot(A						,   data_x.transpose()			)
	prod -= data_y.transpose()
	lowerCamelCase_							:		Union[str, Any]							   =							np.sum(np.square(A			)			)
	lowerCamelCase_							:		Union[str, Any]							   =							sum_elem / (2 * len_data)
	return error






def 					snake_case					(							A						,   A			)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							100000
	lowerCamelCase_							:		Any							   =							0.000_1550

	lowerCamelCase_							:		Any							   =							data_x.shape[1]
	lowerCamelCase_							:		Tuple							   =							data_x.shape[0] - 1

	lowerCamelCase_							:		Tuple							   =							np.zeros((1, no_features)			)

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

	return theta






def 					snake_case					(							A						,   A			)							->							int:




	'''simple docstring'''


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






def 					snake_case					(							)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							collect_dataset()

	lowerCamelCase_							:		Optional[Any]							   =							data.shape[0]
	lowerCamelCase_							:		Any							   =							np.c_[np.ones(A			), data[:, :-1]].astype(A			)
	lowerCamelCase_							:		List[Any]							   =							data[:, -1].astype(A			)

	lowerCamelCase_							:		Tuple							   =							run_linear_regression(A						,   A			)
	lowerCamelCase_							:		Optional[Any]							   =							theta.shape[1]
	print('Resultant Feature vector : '			)
	for i in range(0						,   A			):
		print(f"{theta[0, i]:.5f}"			)


if __name__ == "__main__":
							main()





"""simple docstring"""







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


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """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     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






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



