



"""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      __magic_name__				(							lowercase ):  # picklable for multiprocessing
   return x.sum()





def      __magic_name__				(							lowercase ):  # picklable for multiprocessing
   return i + 1
@dataclass
class 					a	:
  UpperCamelCase	:				int
  UpperCamelCase	:				str







class 					a	(  UpperCAmelCase__       ):



  def 	lowerCamelCase__       (						self :					int		)     ->					List[Any]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Tuple     			=2
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)
     self.assertEqual(map_nested(lowerCAmelCase       ,		lowerCAmelCase       ,		num_proc=lowerCAmelCase		)       ,		lowerCAmelCase		)

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



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


     '''simple docstring'''




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





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Tuple:


     '''simple docstring'''




     class 					a	:
       UpperCamelCase	:				int     						=					'bar'







     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Foo()
     self.assertEqual(foo.my_attr       ,		"""bar"""		)
     with temporary_assignment(lowerCAmelCase       ,		"""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      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   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_: Dict     			={f'''{i}''': i for i in range(lowercase )}
      SCREAMING_SNAKE_CASE_: List[str]     			=map_nested(lambda lowercase : x + 10							,			lowercase							,			num_proc=lowercase							,			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	(  UpperCAmelCase__       ):



  @require_tf
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					int:


     '''simple docstring'''




     import tensorflow as tf
     from tensorflow.keras import layers

     SCREAMING_SNAKE_CASE_: Dict     			=layers.Dense(2		)

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

     with temp_seed(42       ,		set_tensorflow=lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=gen_random_output()
     with temp_seed(42       ,		set_tensorflow=lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Any     			=gen_random_output()
     SCREAMING_SNAKE_CASE_: str     			=gen_random_output()

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



  @require_torch
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     import torch

     def gen_random_output():
        SCREAMING_SNAKE_CASE_: int     			=torch.nn.Linear(3       ,		2		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.rand(1       ,		3		)
        return model(lowerCAmelCase		).detach().numpy()

     with temp_seed(42       ,		set_pytorch=lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=gen_random_output()
     with temp_seed(42       ,		set_pytorch=lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Optional[int]     			=gen_random_output()
     SCREAMING_SNAKE_CASE_: str     			=gen_random_output()

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





  def 	lowerCamelCase__       (						self :					Tuple		)     ->					str:


     '''simple docstring'''




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

     with temp_seed(42		):
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=gen_random_output()
     with temp_seed(42		):
        SCREAMING_SNAKE_CASE_: Dict     			=gen_random_output()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=gen_random_output()

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







@pytest.mark.parametrize("""input_data"""							,			[{}] )
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Tuple     			=NestedDataStructure(lowercase ).flatten()
   assert output == expected_output





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: int     			=A(x=1							,			y="""foobar""" )
   SCREAMING_SNAKE_CASE_: List[str]     			={"""x""": 1, """y""": """foobar"""}
   assert asdict(lowercase ) == expected_output

   SCREAMING_SNAKE_CASE_: Optional[Any]     			={"""a""": {"""b""": A(x=10							,			y="""foo""" )}, """c""": [A(x=20							,			y="""bar""" )]}
   SCREAMING_SNAKE_CASE_: List[str]     			={"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
   assert asdict(lowercase ) == expected_output

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





def      __magic_name__				(							lowercase ):
   return text.split()





def      __magic_name__				(							lowercase ):
   yield (time.time(), content)
   time.sleep(2 )
   yield (time.time(), content)





def      __magic_name__				(							):
   with Pool(2 ) as pool:
      SCREAMING_SNAKE_CASE_: Any     			=list(iflatmap_unordered(lowercase							,			_split_text							,			kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
      assert out.count("""hello""" ) == 10
      assert out.count("""there""" ) == 10
      assert len(lowercase ) == 20

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

   # check that we get items as fast as possible
   with Pool(2 ) as pool:
      SCREAMING_SNAKE_CASE_: Optional[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





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			2 )
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



