



"""simple docstring"""






import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def      _lowercase							(    __snake_case      )		->  Optional[Any]:  # picklable for multiprocessing
			return x.sum()


def      _lowercase							(    __snake_case      )		->  Union[str, Any]:  # picklable for multiprocessing
			return i + 1



@dataclass
class      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{}
				__lowerCAmelCase :    Union[str, Any]				=			[]
				__lowerCAmelCase :    List[Any]				=			1
				__lowerCAmelCase :    Optional[int]				=			[1, 2]
				__lowerCAmelCase :    Union[str, Any]				=			{"a": 1, "b": 2}
				__lowerCAmelCase :    Optional[Any]				=			{"a": [1, 2], "b": [3, 4]}
				__lowerCAmelCase :    Dict				=			{"a": {"1": 1}, "b": 2}
				__lowerCAmelCase :    Tuple				=			{"a": 1, "b": 2, "c": 3, "d": 4}
				__lowerCAmelCase :    Tuple				=			{}
				__lowerCAmelCase :    Optional[int]				=			[]
				__lowerCAmelCase :    str				=			2
				__lowerCAmelCase :    List[str]				=			[2, 3]
				__lowerCAmelCase :    List[Any]				=			{"a": 2, "b": 3}
				__lowerCAmelCase :    Dict				=			{"a": [2, 3], "b": [4, 5]}
				__lowerCAmelCase :    str				=			{"a": {"1": 2}, "b": 3}
				__lowerCAmelCase :    Dict				=			{"a": 2, "b": 3, "c": 4, "d": 5}
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[str]				=			2
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[str]				=			{"a": np.eye(2), "b": np.zeros(3), "c": np.ones(2)}
				__lowerCAmelCase :    Union[str, Any]				=			{"a": 2, "b": 0, "c": 2}
				__lowerCAmelCase :    Any				=			{
				    "a": np.eye(2).astype(_SCREAMING_SNAKE_CASE),
				    "b": np.zeros(3).astype(_SCREAMING_SNAKE_CASE),
				    "c": np.ones(2).astype(_SCREAMING_SNAKE_CASE),
				}
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     map_numpy=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     map_numpy=_SCREAMING_SNAKE_CASE).items()}			,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}			,     )
				self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     map_numpy=_SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     map_numpy=_SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE).items()}			,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}			,     )
				with self.assertRaises(_SCREAMING_SNAKE_CASE):  # can't pickle a local lambda
							map_nested(lambda _SCREAMING_SNAKE_CASE: x + 1			,     _SCREAMING_SNAKE_CASE			,     num_proc=_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			{"a": 1, "b": 2}
				__lowerCAmelCase :    Union[str, Any]				=			{"a": 3, "b": 4}
				__lowerCAmelCase :    List[Any]				=			{"a": 5, "b": 6}
				__lowerCAmelCase :    Union[str, Any]				=			sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))])
				self.assertEqual(sorted(zip_dict(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))			,     _SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[Any]:

				"""simple docstring"""

				class      A__  :




					'''simple docstring'''





					SCREAMING_SNAKE_CASE        =      'bar'






				__lowerCAmelCase :    int				=			Foo()
				self.assertEqual(foo.my_attr			,     "bar")
				with temporary_assignment(_SCREAMING_SNAKE_CASE			,     "my_attr"			,     "BAR"):
							self.assertEqual(foo.my_attr			,     "BAR")
				self.assertEqual(foo.my_attr			,     "bar")






@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"   ,[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]   ,)
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			with patch("datasets.utils.py_utils._single_map_nested"      ) as mock_single_map_nested, patch(
			    "datasets.parallel.parallel.Pool"      ) as mock_multiprocessing_pool:
						__lowerCAmelCase :    Optional[Any]				=			{F"""{i}""": i for i in range(__snake_case      )}
						__lowerCAmelCase :    str				=			map_nested(lambda __snake_case      : x + 10   ,__snake_case   ,num_proc=__snake_case   ,parallel_min_length=16      )
						if expected_num_proc == 1:
									assert mock_single_map_nested.called
									assert not mock_multiprocessing_pool.called
						else:
									assert not mock_single_map_nested.called
									assert mock_multiprocessing_pool.called
									assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@require_tf
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[Any]:

				"""simple docstring"""

				import tensorflow as tf
				from tensorflow.keras import layers

				__lowerCAmelCase :    str				=			layers.Dense(2)

				def gen_random_output():
							__lowerCAmelCase :    List[str]				=			tf.random.uniform((1, 3))
							return model(_SCREAMING_SNAKE_CASE).numpy()

				with temp_seed(42			,     set_tensorflow=_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Tuple				=			gen_random_output()
				with temp_seed(42			,     set_tensorflow=_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Any				=			gen_random_output()
				__lowerCAmelCase :    Dict				=			gen_random_output()

				np.testing.assert_equal(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertGreater(np.abs(outa - outa).sum()			,     0)

	@require_torch
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Dict:

				"""simple docstring"""

				import torch

				def gen_random_output():
							__lowerCAmelCase :    Dict				=			torch.nn.Linear(3			,     2)
							__lowerCAmelCase :    List[Any]				=			torch.rand(1			,     3)
							return model(_SCREAMING_SNAKE_CASE).detach().numpy()

				with temp_seed(42			,     set_pytorch=_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[Any]				=			gen_random_output()
				with temp_seed(42			,     set_pytorch=_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[str]				=			gen_random_output()
				__lowerCAmelCase :    Any				=			gen_random_output()

				np.testing.assert_equal(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertGreater(np.abs(outa - outa).sum()			,     0)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[Any]:

				"""simple docstring"""

				def gen_random_output():
							return np.random.rand(1			,     3)

				with temp_seed(42):
							__lowerCAmelCase :    List[str]				=			gen_random_output()
				with temp_seed(42):
							__lowerCAmelCase :    List[str]				=			gen_random_output()
				__lowerCAmelCase :    Union[str, Any]				=			gen_random_output()

				np.testing.assert_equal(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertGreater(np.abs(outa - outa).sum()			,     0)






@pytest.mark.parametrize("input_data"   ,[{}]      )
def      _lowercase							(    __snake_case      )		->  int:
			__lowerCAmelCase :    List[str]				=			NestedDataStructure(__snake_case      ).data
			assert output_data == input_data


@pytest.mark.parametrize(
    "data, expected_output"   ,[
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]   ,)
def      _lowercase							(    __snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    Dict				=			NestedDataStructure(__snake_case      ).flatten()
			assert output == expected_output


def      _lowercase							(    )		->  Tuple:
			__lowerCAmelCase :    Optional[int]				=			A(x=1   ,y="foobar"      )
			__lowerCAmelCase :    Any				=			{"x": 1, "y": "foobar"}
			assert asdict(__snake_case      ) == expected_output

			__lowerCAmelCase :    Union[str, Any]				=			{"a": {"b": A(x=10   ,y="foo"      )}, "c": [A(x=20   ,y="bar"      )]}
			__lowerCAmelCase :    int				=			{"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
			assert asdict(__snake_case      ) == expected_output

			with pytest.raises(__snake_case      ):
						asdict([1, A(x=10   ,y="foo"      )]      )


def      _lowercase							(    __snake_case      )		->  Optional[Any]:
			return text.split()


def      _lowercase							(    __snake_case      )		->  List[str]:
			yield (time.time(), content)
			time.sleep(2      )
			yield (time.time(), content)





def      _lowercase							(    )		->  List[str]:
			with Pool(2      ) as pool:
						__lowerCAmelCase :    str				=			list(iflatmap_unordered(__snake_case   ,_split_text   ,kwargs_iterable=[{"text": "hello there"}] * 10      )      )
						assert out.count("hello"      ) == 10
						assert out.count("there"      ) == 10
						assert len(__snake_case      ) == 20

			# check multiprocess from pathos (uses dill for pickling)
			with multiprocess.Pool(2      ) as pool:
						__lowerCAmelCase :    str				=			list(iflatmap_unordered(__snake_case   ,_split_text   ,kwargs_iterable=[{"text": "hello there"}] * 10      )      )
						assert out.count("hello"      ) == 10
						assert out.count("there"      ) == 10
						assert len(__snake_case      ) == 20

			# check that we get items as fast as possible
			with Pool(2      ) as pool:
						__lowerCAmelCase :    Dict				=			[]
						for yield_time, content in iflatmap_unordered(
						    __snake_case   ,_aseconds_generator_of_aitems_with_timing   ,kwargs_iterable=[{"content": "a"}, {"content": "b"}]      ):
									assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
									out.append(__snake_case      )
						assert out.count("a"      ) == 2
						assert out.count("b"      ) == 2
						assert len(__snake_case      ) == 4




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


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			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