'''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  UpperCamelCase__					(					_lowercase    :				int     )							->	Dict:  # picklable for multiprocessing
  return x.sum()






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Optional[int]:  # picklable for multiprocessing
  return i + 1




@dataclass
class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					42
  __lowerCAmelCase				      =					42




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def    lowercase_  (							self					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Tuple		= 2
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)
    self.assertEqual(map_nested(snake_case_   ,    snake_case_   ,    num_proc=snake_case_					)   ,    snake_case_					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= {"""a""": 1, """b""": 2}
    __UpperCAmelCase:					Any		= {"""a""": 3, """b""": 4}
    __UpperCAmelCase:					int		= {"""a""": 5, """b""": 6}
    __UpperCAmelCase:					str		= sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]					)
    self.assertEqual(sorted(zip_dict(snake_case_   ,    snake_case_   ,    snake_case_					)					)   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    class    a :



      """simple docstring"""
      __lowerCAmelCase				      =					"""bar"""



    __UpperCAmelCase:					List[Any]		= Foo()
    self.assertEqual(foo.my_attr   ,    """bar"""					)
    with temporary_assignment(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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]   ,    )
def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				Tuple   ,    _lowercase    :				Tuple     )							->	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:
    __UpperCAmelCase:					Optional[int]		= {F'''{i}''': i for i in range(_lowercase     )}
    __UpperCAmelCase:					Any		= map_nested(lambda _lowercase     : x + 1_0   ,    _lowercase   ,    num_proc=_lowercase   ,    parallel_min_length=1_6     )
    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 (       __lowerCAmelCase				):



  """simple docstring"""







  @require_tf
  def    lowercase_  (							self					):

    '''simple docstring'''







    import tensorflow as tf
    from tensorflow.keras import layers

    __UpperCAmelCase:					Dict		= layers.Dense(2					)

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

    with temp_seed(42   ,    set_tensorflow=snake_case_					):
      __UpperCAmelCase:					Dict		= gen_random_output()
    with temp_seed(42   ,    set_tensorflow=snake_case_					):
      __UpperCAmelCase:					Optional[Any]		= gen_random_output()
    __UpperCAmelCase:					Any		= gen_random_output()

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







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    def gen_random_output():
      __UpperCAmelCase:					str		= torch.nn.Linear(3   ,    2					)
      __UpperCAmelCase:					List[Any]		= torch.rand(1   ,    3					)
      return model(snake_case_					).detach().numpy()

    with temp_seed(42   ,    set_pytorch=snake_case_					):
      __UpperCAmelCase:					str		= gen_random_output()
    with temp_seed(42   ,    set_pytorch=snake_case_					):
      __UpperCAmelCase:					Tuple		= gen_random_output()
    __UpperCAmelCase:					List[str]		= gen_random_output()

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







  def    lowercase_  (							self					):

    '''simple docstring'''







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

    with temp_seed(42					):
      __UpperCAmelCase:					Tuple		= gen_random_output()
    with temp_seed(42					):
      __UpperCAmelCase:					Optional[Any]		= gen_random_output()
    __UpperCAmelCase:					Any		= gen_random_output()

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






@pytest.mark.parametrize("""input_data"""   ,    [{}]     )
def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]     )							->	List[Any]:
  __UpperCAmelCase:					int		= NestedDataStructure(_lowercase     ).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  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				str     )							->	Any:
  __UpperCAmelCase:					Optional[Any]		= NestedDataStructure(_lowercase     ).flatten()
  assert output == expected_output






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

  __UpperCAmelCase:					int		= {"""a""": {"""b""": A(x=1_0   ,    y="""foo"""     )}, """c""": [A(x=2_0   ,    y="""bar"""     )]}
  __UpperCAmelCase:					Dict		= {"""a""": {"""b""": {"""x""": 1_0, """y""": """foo"""}}, """c""": [{"""x""": 2_0, """y""": """bar"""}]}
  assert asdict(_lowercase     ) == expected_output

  with pytest.raises(_lowercase     ):
    asdict([1, A(x=1_0   ,    y="""foo"""     )]     )






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Tuple:
  return text.split()






def  UpperCamelCase__					(					_lowercase    :				Any     )							->	Tuple:
  yield (time.time(), content)
  time.sleep(2     )
  yield (time.time(), content)







def  UpperCamelCase__					(					)							->	List[Any]:
  with Pool(2     ) as pool:
    __UpperCAmelCase:					List[str]		= list(iflatmap_unordered(_lowercase   ,    _split_text   ,    kwargs_iterable=[{"""text""": """hello there"""}] * 1_0     )     )
    assert out.count("""hello"""     ) == 1_0
    assert out.count("""there"""     ) == 1_0
    assert len(_lowercase     ) == 2_0

  # check multiprocess from pathos (uses dill for pickling)
  with multiprocess.Pool(2     ) as pool:
    __UpperCAmelCase:					Tuple		= list(iflatmap_unordered(_lowercase   ,    _split_text   ,    kwargs_iterable=[{"""text""": """hello there"""}] * 1_0     )     )
    assert out.count("""hello"""     ) == 1_0
    assert out.count("""there"""     ) == 1_0
    assert len(_lowercase     ) == 2_0

  # check that we get items as fast as possible
  with Pool(2     ) as pool:
    __UpperCAmelCase:					int		= []
    for yield_time, content in iflatmap_unordered(
        _lowercase   ,    _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(_lowercase     )
    assert out.count("""a"""     ) == 2
    assert out.count("""b"""     ) == 2
    assert len(_lowercase     ) == 4
'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					Optional[int]		= []
  for i, start_vector in enumerate(vectors[:-1]     ):
    __UpperCAmelCase:					Union[str, Any]		= vectors[i + 1]
    new_vectors.append(_lowercase     )
    __UpperCAmelCase:					Optional[int]		= end_vector - start_vector
    new_vectors.append(start_vector + difference_vector / 3     )
    new_vectors.append(
        start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,    6_0     )     )
    new_vectors.append(start_vector + difference_vector * 2 / 3     )
  new_vectors.append(vectors[-1]     )
  return new_vectors






def  UpperCamelCase__					(					_lowercase    :				numpy.ndarray   ,    _lowercase    :				float     )							->	numpy.ndarray:
  __UpperCAmelCase:					Tuple		= numpy.radians(_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= numpy.cos(_lowercase     ), numpy.sin(_lowercase     )
  __UpperCAmelCase:					Tuple		= numpy.array(((c, -s), (s, c))     )
  return numpy.dot(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, Any]		= plt.gca()
  axes.set_aspect("""equal"""     )

  # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
  # y-coordinates as inputs, which are constructed from the vector-list using
  # zip()
  __UpperCAmelCase, __UpperCAmelCase:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)