



"""simple docstring"""







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


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


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



def      __UpperCAmelCase      (    )							->      Tuple:
		plt.scatter(__lowerCamelCase						,     __lowerCamelCase						,     color='''red'''	)
		plt.plot(__lowerCamelCase						,     pol_reg.predict(poly_reg.fit_transform(__lowerCamelCase	)	)						,     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

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a  :     int       =  logging.get_logger(__name__)

__a  :     str       =  {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						'''deta'''
			__a  : Optional[int]			 =						{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}





			def __init__(   self      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=9_00      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=10_24      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__="relu"      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__="sine"      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=3_00      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.25      ,   **lowerCAmelCase__      ,   )     ->					List[Any]:

							'''simple docstring'''


							if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
											__lowercase 			=							CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']						)
							else:
											if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
															__lowercase 			=							backbone_config.pop('''model_type'''						)
															__lowercase 			=							CONFIG_MAPPING[backbone_model_type]
															__lowercase 			=							config_class.from_dict(lowerCAmelCase__						)

							__lowercase 			=							backbone_config
							__lowercase 			=							num_queries
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							d_model
							__lowercase 			=							encoder_ffn_dim
							__lowercase 			=							encoder_layers
							__lowercase 			=							encoder_attention_heads
							__lowercase 			=							decoder_ffn_dim
							__lowercase 			=							decoder_layers
							__lowercase 			=							decoder_attention_heads
							__lowercase 			=							dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							activation_function
							__lowercase 			=							init_std
							__lowercase 			=							init_xavier_std
							__lowercase 			=							encoder_layerdrop
							__lowercase 			=							auxiliary_loss
							__lowercase 			=							position_embedding_type
							# deformable attributes
							__lowercase 			=							num_feature_levels
							__lowercase 			=							encoder_n_points
							__lowercase 			=							decoder_n_points
							__lowercase 			=							two_stage
							__lowercase 			=							two_stage_num_proposals
							__lowercase 			=							with_box_refine
							__lowercase 			=							assign_first_stage
							if two_stage is True and with_box_refine is False:
											raise ValueError('''If two_stage is True, with_box_refine must be True.'''						)
							# Hungarian matcher
							__lowercase 			=							class_cost
							__lowercase 			=							bbox_cost
							__lowercase 			=							giou_cost
							# Loss coefficients
							__lowercase 			=							mask_loss_coefficient
							__lowercase 			=							dice_loss_coefficient
							__lowercase 			=							bbox_loss_coefficient
							__lowercase 			=							giou_loss_coefficient
							__lowercase 			=							eos_coefficient
							__lowercase 			=							focal_alpha
							super().__init__(is_encoder_decoder=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.d_model





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							copy.deepcopy(self.__dict__						)
							__lowercase 			=							self.backbone_config.to_dict()
							__lowercase 			=							self.__class__.model_type
							return output