






"""simple docstring"""







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
  from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["""bs"""]     ,model_result["""ss"""] ):
                      UpperCAmelCase          =	model_result["""result"""][batch_size][sequence_length]
                      self.assertIsNotNone(A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sgugger/tiny-distilbert-classification"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,only_pretrain_model=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,torchscript=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    @unittest.skipIf(torch_device == """cpu"""     ,"""Cant do half precision""" )
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,fpaa=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	AutoConfig.from_pretrained(A )
          # set architectures equal to `None`
          UpperCAmelCase          =	None
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A     ,configs=[config] )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result )
          self.check_results_dict_not_empty(results.memory_train_result )






    @unittest.skipIf(torch_device == """cpu"""     ,"""Can't do half precision""" )
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,fpaa=A     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result )
          self.check_results_dict_not_empty(results.memory_train_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	AutoConfig.from_pretrained(A )
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A     ,configs=[config] )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tinier_bart"""
          UpperCAmelCase          =	AutoConfig.from_pretrained(A )
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A     ,configs=[config] )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result )
          self.check_results_dict_not_empty(results.memory_inference_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          UpperCAmelCase          =	AutoConfig.from_pretrained(A )
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A     ,configs=[config] )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result )
          self.check_results_dict_not_empty(results.memory_train_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tinier_bart"""
          UpperCAmelCase          =	AutoConfig.from_pretrained(A )
          UpperCAmelCase          =	PyTorchBenchmarkArguments(
              models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,multi_process=A     ,)
          UpperCAmelCase          =	PyTorchBenchmark(A     ,configs=[config] )
          UpperCAmelCase          =	benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result )
          self.check_results_dict_not_empty(results.memory_train_result )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""
          with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCAmelCase          =	PyTorchBenchmarkArguments(
                    models=[MODEL_ID]     ,training=A     ,inference=A     ,save_to_csv=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,inference_time_csv_file=os.path.join(A     ,"""inf_time.csv""" )     ,train_memory_csv_file=os.path.join(A     ,"""train_mem.csv""" )     ,inference_memory_csv_file=os.path.join(A     ,"""inf_mem.csv""" )     ,train_time_csv_file=os.path.join(A     ,"""train_time.csv""" )     ,env_info_csv_file=os.path.join(A     ,"""env.csv""" )     ,multi_process=A     ,)
                UpperCAmelCase          =	PyTorchBenchmark(A )
                benchmark.run()
                self.assertTrue(Path(os.path.join(A     ,"""inf_time.csv""" ) ).exists() )
                self.assertTrue(Path(os.path.join(A     ,"""train_time.csv""" ) ).exists() )
                self.assertTrue(Path(os.path.join(A     ,"""inf_mem.csv""" ) ).exists() )
                self.assertTrue(Path(os.path.join(A     ,"""train_mem.csv""" ) ).exists() )
                self.assertTrue(Path(os.path.join(A     ,"""env.csv""" ) ).exists() )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""sshleifer/tiny-gpt2"""

          def _check_summary_is_not_empty(A ):
                self.assertTrue(hasattr(A     ,"""sequential""" ) )
                self.assertTrue(hasattr(A     ,"""cumulative""" ) )
                self.assertTrue(hasattr(A     ,"""current""" ) )
                self.assertTrue(hasattr(A     ,"""total""" ) )

          with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCAmelCase          =	PyTorchBenchmarkArguments(
                    models=[MODEL_ID]     ,training=A     ,inference=A     ,sequence_lengths=[8]     ,batch_sizes=[1]     ,log_filename=os.path.join(A     ,"""log.txt""" )     ,log_print=A     ,trace_memory_line_by_line=A     ,multi_process=A     ,)
                UpperCAmelCase          =	PyTorchBenchmark(A )
                UpperCAmelCase          =	benchmark.run()
                _check_summary_is_not_empty(result.inference_summary )
                _check_summary_is_not_empty(result.train_summary )
                self.assertTrue(Path(os.path.join(A     ,"""log.txt""" ) ).exists() )











"""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



