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      a__ (		__UpperCamelCase				):  # picklable for multiprocessing
      return x.sum()


def      a__ (		__UpperCamelCase				):  # picklable for multiprocessing
      return i + 1


@dataclass
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						42
  lowerCamelCase__     =						42



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       int   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  {}
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  [1, 2]
        SCREAMING_SNAKE_CASE_          =  {"a": 1, "b": 2}
        SCREAMING_SNAKE_CASE_          =  {"a": [1, 2], "b": [3, 4]}
        SCREAMING_SNAKE_CASE_          =  {"a": {"1": 1}, "b": 2}
        SCREAMING_SNAKE_CASE_          =  {"a": 1, "b": 2, "c": 3, "d": 4}
        SCREAMING_SNAKE_CASE_          =  {}
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  [2, 3]
        SCREAMING_SNAKE_CASE_          =  {"a": 2, "b": 3}
        SCREAMING_SNAKE_CASE_          =  {"a": [2, 3], "b": [4, 5]}
        SCREAMING_SNAKE_CASE_          =  {"a": {"1": 2}, "b": 3}
        SCREAMING_SNAKE_CASE_          =  {"a": 2, "b": 3, "c": 4, "d": 5}
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__   )  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  2
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )
        self.assertEqual(map_nested(__magic_name__  ,     __magic_name__  ,     num_proc=__magic_name__   )  ,     __magic_name__   )

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







  def    __A     (							self :       List[Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  {"a": 1, "b": 2}
        SCREAMING_SNAKE_CASE_          =  {"a": 3, "b": 4}
        SCREAMING_SNAKE_CASE_          =  {"a": 5, "b": 6}
        SCREAMING_SNAKE_CASE_          =  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]   )
        self.assertEqual(sorted(zip_dict(__magic_name__  ,     __magic_name__  ,     __magic_name__   )   )  ,     __magic_name__   )







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


        class   lowerCamelCase					:
          """simple docstring"""







          lowerCamelCase__     =						'bar'



        SCREAMING_SNAKE_CASE_          =  Foo()
        self.assertEqual(foo.my_attr  ,     "bar"   )
        with temporary_assignment(__magic_name__  ,     "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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      with patch("datasets.utils.py_utils._single_map_nested"				) as mock_single_map_nested, patch(
          "datasets.parallel.parallel.Pool"				) as mock_multiprocessing_pool:
            SCREAMING_SNAKE_CASE_          =  {F'''{i}''': i for i in range(_SCREAMING_SNAKE_CASE				)}
            SCREAMING_SNAKE_CASE_          =  map_nested(lambda __UpperCamelCase				: x + 1_0     ,      _SCREAMING_SNAKE_CASE     ,      num_proc=_SCREAMING_SNAKE_CASE     ,      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   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  @require_tf
  def    __A     (							self :       Tuple   )  ->	str:
        import tensorflow as tf
        from tensorflow.keras import layers

        SCREAMING_SNAKE_CASE_          =  layers.Dense(2   )

        def gen_random_output():
              SCREAMING_SNAKE_CASE_          =  tf.random.uniform((1, 3)   )
              return model(__magic_name__   ).numpy()

        with temp_seed(42  ,     set_tensorflow=__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        with temp_seed(42  ,     set_tensorflow=__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        SCREAMING_SNAKE_CASE_          =  gen_random_output()

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







  @require_torch
  def    __A     (							self :       Tuple   )  ->	Any:
        import torch

        def gen_random_output():
              SCREAMING_SNAKE_CASE_          =  torch.nn.Linear(3  ,     2   )
              SCREAMING_SNAKE_CASE_          =  torch.rand(1  ,     3   )
              return model(__magic_name__   ).detach().numpy()

        with temp_seed(42  ,     set_pytorch=__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        with temp_seed(42  ,     set_pytorch=__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        SCREAMING_SNAKE_CASE_          =  gen_random_output()

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







  def    __A     (							self :       List[str]   )  ->	Tuple:







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

        with temp_seed(42   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        with temp_seed(42   ):
              SCREAMING_SNAKE_CASE_          =  gen_random_output()
        SCREAMING_SNAKE_CASE_          =  gen_random_output()

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



@pytest.mark.parametrize("input_data"     ,      [{}]				)
def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  NestedDataStructure(_SCREAMING_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      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  NestedDataStructure(_SCREAMING_SNAKE_CASE				).flatten()
      assert output == expected_output


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  A(x=1     ,      y="foobar"				)
      SCREAMING_SNAKE_CASE_          =  {"x": 1, "y": "foobar"}
      assert asdict(_SCREAMING_SNAKE_CASE				) == expected_output

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

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


def      a__ (		__UpperCamelCase				):
      return text.split()


def      a__ (		__UpperCamelCase				):
      yield (time.time(), content)
      time.sleep(2				)
      yield (time.time(), content)





def      a__ (		):
      with Pool(2				) as pool:
            SCREAMING_SNAKE_CASE_          =  list(iflatmap_unordered(_SCREAMING_SNAKE_CASE     ,      _split_text     ,      kwargs_iterable=[{"text": "hello there"}] * 1_0				)				)
            assert out.count("hello"				) == 1_0
            assert out.count("there"				) == 1_0
            assert len(_SCREAMING_SNAKE_CASE				) == 2_0

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

      # check that we get items as fast as possible
      with Pool(2				) as pool:
            SCREAMING_SNAKE_CASE_          =  []
            for yield_time, content in iflatmap_unordered(
                _SCREAMING_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(_SCREAMING_SNAKE_CASE				)
            assert out.count("a"				) == 2
            assert out.count("b"				) == 2
            assert len(_SCREAMING_SNAKE_CASE				) == 4



from collections.abc import Callable


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  a
      SCREAMING_SNAKE_CASE_          =  b
      if function(__UpperCamelCase				) == 0:  # one of the a or b is a root for the function
            return a
      elif function(__UpperCamelCase				) == 0:
            return b
      elif (
          function(__UpperCamelCase				) * function(__UpperCamelCase				) > 0
      ):  # if none of these are root and they are both positive or negative,
            # then this algorithm can't find the root
            raise ValueError("could not find root in given interval."				)
      else:
            SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            while abs(start - mid				) > 1_0**-7:  # until precisely equals to 10^-7
                  if function(__UpperCamelCase				) == 0:
                        return mid
                  elif function(__UpperCamelCase				) * function(__UpperCamelCase				) < 0:
                        SCREAMING_SNAKE_CASE_          =  mid
                  else:
                        SCREAMING_SNAKE_CASE_          =  mid
                  SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            return mid


def      a__ (		__UpperCamelCase				):
      return x**3 - 2 * x - 5


if __name__ == "__main__":
      print(bisection(f, 1, 10_00))

      import doctest

      doctest.testmod()


