"""simple docstring"""







import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	hf_hub_download(
              repo_id="""nateraw/video-demo"""     ,filename="""archery.mp4"""     ,repo_type="""dataset""" )
          UpperCAmelCase          =	VideoClassificationPipeline(model=UpperCamelCase__     ,image_processor=UpperCamelCase__     ,top_k=2 )
          UpperCAmelCase          =	[
              example_video_filepath,
              "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
          ]
          return video_classifier, examples






    def     _UpperCamelCase							(			self     ,A     ,A ):


          for example in examples:
                UpperCAmelCase          =	video_classifier(UpperCamelCase__ )

                self.assertEqual(
                    UpperCamelCase__     ,[
                        {"""score""": ANY(UpperCamelCase__ ), """label""": ANY(UpperCamelCase__ )},
                        {"""score""": ANY(UpperCamelCase__ ), """label""": ANY(UpperCamelCase__ )},
                    ]     ,)






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
          UpperCAmelCase          =	VideoMAEFeatureExtractor(
              size={"""shortest_edge""": 10}     ,crop_size={"""height""": 10, """width""": 10} )
          UpperCAmelCase          =	pipeline(
              """video-classification"""     ,model=UpperCamelCase__     ,feature_extractor=UpperCamelCase__     ,frame_sampling_rate=4 )

          UpperCAmelCase          =	hf_hub_download(repo_id="""nateraw/video-demo"""     ,filename="""archery.mp4"""     ,repo_type="""dataset""" )
          UpperCAmelCase          =	video_classifier(UpperCamelCase__     ,top_k=2 )
          self.assertEqual(
              nested_simplify(UpperCamelCase__     ,decimals=4 )     ,[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}]     ,)

          UpperCAmelCase          =	video_classifier(
              [
                  video_file_path,
                  video_file_path,
              ]     ,top_k=2     ,)
          self.assertEqual(
              nested_simplify(UpperCamelCase__     ,decimals=4 )     ,[
                  [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
                  [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
              ]     ,)






    @require_tf
    def     _UpperCamelCase							(			self ):
          pass











"""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



