

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


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


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




def 				UpperCamelCase(					):
 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







'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=30					,      lowercase_			:					Tuple=400					,      lowercase_			:					Tuple=True					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					List[str]=0.9					,      lowercase_			:					Tuple=None					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=[0.5, 0.5, 0.5]					,      lowercase_			:					List[str]=[0.5, 0.5, 0.5]					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[int]						 =			size if size is not None else {'shortest_edge': 30}
      UpperCAmelCase	:     int						 =			crop_size if crop_size is not None else {'height': 30, 'width': 30}
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     int						 =			num_channels
      UpperCAmelCase	:     int						 =			min_resolution
      UpperCAmelCase	:     Optional[int]						 =			max_resolution
      UpperCAmelCase	:     str						 =			do_resize_and_center_crop
      UpperCAmelCase	:     int						 =			size
      UpperCAmelCase	:     Dict						 =			crop_pct
      UpperCAmelCase	:     Union[str, Any]						 =			crop_size
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Optional[Any]						 =			image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std




     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  PoolFormerImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			PoolFormerImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize_and_center_crop'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)
      self.assertTrue(hasattr(lowercase_					,      'crop_pct'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 30}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}	)

      UpperCAmelCase	:     Any						 =			self.image_processing_class.from_dict(self.image_processor_dict					,      size=42					,      crop_size=84	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 42}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 84, 'width': 84}	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      pass


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Any						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     Dict						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      self.assertEqual(
          encoded_images.shape					,      (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )

      # Test batched
      UpperCAmelCase	:     str						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      np.ndarray	)

      # Test not batched input
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )

      # Test batched
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )




     def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test not batched input
      UpperCAmelCase	:     int						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )

      # Test batched
      UpperCAmelCase	:     Optional[int]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )




