"""simple docstring"""







import os
import sys
import unittest


_UpperCamelCase					      =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCamelCase					      =  os.path.join(git_repo_path, """src""", """transformers""")

_UpperCamelCase					      =  """
{0} = None
"""

_UpperCamelCase					      =  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


_UpperCamelCase					      =  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")""" )
          self.assertIsNone(A )

          UpperCAmelCase          =	find_backend("""    if not is_tokenizers_available():""" )
          self.assertEqual(A     ,"""tokenizers""" )

          UpperCAmelCase          =	find_backend("""    if not is_tensorflow_text_available():""" )
          self.assertEqual(A     ,"""tensorflow_text""" )

          UpperCAmelCase          =	find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):""" )
          self.assertEqual(A     ,"""sentencepiece_and_tokenizers""" )

          UpperCAmelCase          =	find_backend(
              """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):""" )
          self.assertEqual(A     ,"""sentencepiece_and_tensorflow_text""" )

          UpperCAmelCase          =	find_backend(
              """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):""" )
          self.assertEqual(A     ,"""sentencepiece_and_tokenizers_and_vision""" )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	read_init()
          # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
          self.assertIn("""torch"""     ,A )
          self.assertIn("""tensorflow_text"""     ,A )
          self.assertIn("""sentencepiece_and_tokenizers"""     ,A )

          # Likewise, we can't assert on the exact content of a key
          self.assertIn("""BertModel"""     ,objects["""torch"""] )
          self.assertIn("""TFBertModel"""     ,objects["""tf"""] )
          self.assertIn("""FlaxBertModel"""     ,objects["""flax"""] )
          self.assertIn("""BertModel"""     ,objects["""torch"""] )
          self.assertIn("""TFBertTokenizer"""     ,objects["""tensorflow_text"""] )
          self.assertIn("""convert_slow_tokenizer"""     ,objects["""sentencepiece_and_tokenizers"""] )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_dummy_object("""CONSTANT"""     ,"""'torch'""" )
          self.assertEqual(A     ,"""\nCONSTANT = None\n""" )

          UpperCAmelCase          =	create_dummy_object("""function"""     ,"""'torch'""" )
          self.assertEqual(
              A     ,"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n""" )

          UpperCAmelCase          =	"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
          UpperCAmelCase          =	create_dummy_object("""FakeClass"""     ,"""'torch'""" )
          self.assertEqual(A     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
          UpperCAmelCase          =	create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
          self.assertEqual(dummy_files["""torch"""]     ,A )











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


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


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

def _a			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_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



