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


A   :     List[Any]       =				train_test_split(X, y, test_size=0.2, random_state=0)


A   :     Dict       =				PolynomialFeatures(degree=4)
A   :     str       =				poly_reg.fit_transform(X)
A   :     Tuple       =				LinearRegression()
pol_reg.fit(X_poly, y)


def    a__       (						):
      plt.scatter(UpperCamelCase__   ,					UpperCamelCase__   ,					color="red" )
      plt.plot(UpperCamelCase__   ,					pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) )   ,					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 unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
       import jax

       from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
       from transformers.models.bert.modeling_flax_bert import FlaxBertModel
       from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxBertModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxRobertaModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''bert-base is not a local folder and is not a valid model identifier''' ):
               A__											=						FlaxAutoModel.from_pretrained('''bert-base''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
               A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase,revision='''aaaaaa''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''',):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )




 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(__lowerCamelCase,'''Use `from_pt=True` to load this model''' ):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )





