
from ...configuration_utils import PretrainedConfig


A		: List[Any]								=     {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}



class    a_ (			_a    ):
			a	:			List[Any]					   =							'''tapas'''






			def __init__(      self				,			__UpperCamelCase=30_522				,			__UpperCamelCase=768				,			__UpperCamelCase=12				,			__UpperCamelCase=12				,			__UpperCamelCase=3_072				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=1_024				,			__UpperCamelCase=[3, 256, 256, 2, 256, 256, 10]				,			__UpperCamelCase=0.02				,			__UpperCamelCase=1E-12				,			__UpperCamelCase=0				,			__UpperCamelCase=10.0				,			__UpperCamelCase=0				,			__UpperCamelCase=1.0				,			__UpperCamelCase=None				,			__UpperCamelCase=1.0				,			__UpperCamelCase=False				,			__UpperCamelCase=None				,			__UpperCamelCase=1.0				,			__UpperCamelCase=1.0				,			__UpperCamelCase=False				,			__UpperCamelCase=False				,			__UpperCamelCase="ratio"				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=64				,			__UpperCamelCase=32				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			__UpperCamelCase=False				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			__UpperCamelCase=False				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase				,			):
									super().__init__(pad_token_id=__UpperCamelCase				,			**__UpperCamelCase  )

									# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
									_lowercase					       =					vocab_size
									_lowercase					       =					hidden_size
									_lowercase					       =					num_hidden_layers
									_lowercase					       =					num_attention_heads
									_lowercase					       =					hidden_act
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_dropout_prob
									_lowercase					       =					attention_probs_dropout_prob
									_lowercase					       =					max_position_embeddings
									_lowercase					       =					type_vocab_sizes
									_lowercase					       =					initializer_range
									_lowercase					       =					layer_norm_eps

									# Fine-tuning task hyperparameters
									_lowercase					       =					positive_label_weight
									_lowercase					       =					num_aggregation_labels
									_lowercase					       =					aggregation_loss_weight
									_lowercase					       =					use_answer_as_supervision
									_lowercase					       =					answer_loss_importance
									_lowercase					       =					use_normalized_answer_loss
									_lowercase					       =					huber_loss_delta
									_lowercase					       =					temperature
									_lowercase					       =					aggregation_temperature
									_lowercase					       =					use_gumbel_for_cells
									_lowercase					       =					use_gumbel_for_aggregation
									_lowercase					       =					average_approximation_function
									_lowercase					       =					cell_selection_preference
									_lowercase					       =					answer_loss_cutoff
									_lowercase					       =					max_num_rows
									_lowercase					       =					max_num_columns
									_lowercase					       =					average_logits_per_cell
									_lowercase					       =					select_one_column
									_lowercase					       =					allow_empty_column_selection
									_lowercase					       =					init_cell_selection_weights_to_zero
									_lowercase					       =					reset_position_index_per_cell
									_lowercase					       =					disable_per_token_loss

									# Aggregation hyperparameters
									_lowercase					       =					aggregation_labels
									_lowercase					       =					no_aggregation_label_index

									if isinstance(self.aggregation_labels				,			__UpperCamelCase  ):
															_lowercase					       =					{int(__UpperCamelCase  ): v for k, v in aggregation_labels.items()}

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


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						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