


import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
a       	=  pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
a       	=  dataset.iloc[:, 1:2].values
a       	=  dataset.iloc[:, 2].values


a	,       a	,       a	,       a       	=  train_test_split(X, y, test_size=0.2, random_state=0)


a       	=  PolynomialFeatures(degree=4)
a       	=  poly_reg.fit_transform(X)
a       	=  LinearRegression()
pol_reg.fit(X_poly, y)


def       UpperCAmelCase_					(   ):
					plt.scatter(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				color="""red"""	)
					plt.plot(SCREAMING_SNAKE_CASE_    ,				pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_	)	)    ,				color="""blue"""	)
					plt.title("""Truth or Bluff (Linear Regression)"""	)
					plt.xlabel("""Position level"""	)
					plt.ylabel("""Salary"""	)
					plt.show()


if __name__ == "__main__":
							viz_polymonial()

							# Predicting a new result with Polymonial Regression
							pol_reg.predict(poly_reg.fit_transform([[5.5]]))
							# output should be 132148.43750003









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       "falcon"
 __lowerCamelCase    :				List[str]								=       ["past_key_values"]




 def __init__(						self      ,  _lowerCAmelCase=65024      ,  _lowerCAmelCase=4544      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=71      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=11      ,  _lowerCAmelCase=11      ,  **_lowerCAmelCase      ,  )     ->				Union[str, Any]:
     _lowerCAmelCase						      =   vocab_size
     # Backward compatibility with n_embed kwarg
     _lowerCAmelCase						      =   kwargs.pop("n_embed"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size if n_embed is None else n_embed
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id
     _lowerCAmelCase						      =   num_attention_heads if num_kv_heads is None else num_kv_heads
     _lowerCAmelCase						      =   alibi
     _lowerCAmelCase						      =   new_decoder_architecture
     _lowerCAmelCase						      =   multi_query  # Ignored when new_decoder_architecture is True
     _lowerCAmelCase						      =   parallel_attn
     _lowerCAmelCase						      =   bias

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return self.hidden_size // self.num_attention_heads





 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return not self.alibi


