





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_							(	_lowerCAmelCase	)		->					Tuple:  # picklable for multiprocessing
 return x.sum()



def 						A_							(	_lowerCAmelCase	)		->					Dict:  # picklable for multiprocessing
 return i + 1



@dataclass
class    A__  :
      _UpperCAmelCase      :int
      _UpperCAmelCase      :str



class    A__  (			__snake_case     ):





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   List[str]			      =  2
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )
       self.assertEqual(map_nested(A_			,				A_			,				num_proc=A_  )			,				A_  )

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  {"a": 1, "b": 2}
       UpperCamelCase      :   Tuple			      =  {"a": 3, "b": 4}
       UpperCamelCase      :   Optional[Any]			      =  {"a": 5, "b": 6}
       UpperCamelCase      :   Tuple			      =  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]  )
       self.assertEqual(sorted(zip_dict(A_			,				A_			,				A_  )  )			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       class    A__  :
             _UpperCAmelCase      :Union[str, Any]		       =  'bar'


       UpperCamelCase      :   str			      =  Foo()
       self.assertEqual(foo.my_attr			,				"bar"  )
       with temporary_assignment(A_			,				"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 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[int]:
 with patch("datasets.utils.py_utils._single_map_nested"	) as mock_single_map_nested, patch(
     "datasets.parallel.parallel.Pool"	) as mock_multiprocessing_pool:
  UpperCamelCase      :   str			      =  {F"""{i}""": i for i in range(_lowerCAmelCase	)}
  UpperCamelCase      :   Optional[Any]			      =  map_nested(lambda _lowerCAmelCase	: x + 10				,  _lowerCAmelCase				,  num_proc=_lowerCAmelCase				,  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__  (			__snake_case     ):





      @require_tf
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       import tensorflow as tf
       from tensorflow.keras import layers

       UpperCamelCase      :   int			      =  layers.Dense(2  )

       def gen_random_output():
        UpperCamelCase      :   Optional[Any]			      =  tf.random.uniform((1, 3)  )
        return model(A_  ).numpy()

       with temp_seed(42			,				set_tensorflow=A_  ):
        UpperCamelCase      :   Optional[int]			      =  gen_random_output()
       with temp_seed(42			,				set_tensorflow=A_  ):
        UpperCamelCase      :   Tuple			      =  gen_random_output()
       UpperCamelCase      :   Optional[int]			      =  gen_random_output()

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





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       import torch

       def gen_random_output():
        UpperCamelCase      :   Dict			      =  torch.nn.Linear(3			,				2  )
        UpperCamelCase      :   str			      =  torch.rand(1			,				3  )
        return model(A_  ).detach().numpy()

       with temp_seed(42			,				set_pytorch=A_  ):
        UpperCamelCase      :   Union[str, Any]			      =  gen_random_output()
       with temp_seed(42			,				set_pytorch=A_  ):
        UpperCamelCase      :   Any			      =  gen_random_output()
       UpperCamelCase      :   str			      =  gen_random_output()

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       with temp_seed(42  ):
        UpperCamelCase      :   List[Any]			      =  gen_random_output()
       with temp_seed(42  ):
        UpperCamelCase      :   Optional[int]			      =  gen_random_output()
       UpperCamelCase      :   Any			      =  gen_random_output()

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



@pytest.mark.parametrize("input_data"				,  [{}]	)
def 						A_							(	_lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   Optional[int]			      =  NestedDataStructure(_lowerCAmelCase	).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_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Union[str, Any]:
 UpperCamelCase      :   int			      =  NestedDataStructure(_lowerCAmelCase	).flatten()
 assert output == expected_output



def 						A_							(	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  A(x=1				,  y="foobar"	)
 UpperCamelCase      :   Optional[int]			      =  {"x": 1, "y": "foobar"}
 assert asdict(_lowerCAmelCase	) == expected_output

 UpperCamelCase      :   List[str]			      =  {"a": {"b": A(x=10				,  y="foo"	)}, "c": [A(x=20				,  y="bar"	)]}
 UpperCamelCase      :   Tuple			      =  {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
 assert asdict(_lowerCAmelCase	) == expected_output

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



def 						A_							(	_lowerCAmelCase	)		->					Optional[int]:
 return text.split()



def 						A_							(	_lowerCAmelCase	)		->					Tuple:
 yield (time.time(), content)
 time.sleep(2	)
 yield (time.time(), content)



def 						A_							(	)		->					Optional[int]:
 with Pool(2	) as pool:
  UpperCamelCase      :   Dict			      =  list(iflatmap_unordered(_lowerCAmelCase				,  _split_text				,  kwargs_iterable=[{"text": "hello there"}] * 10	)	)
  assert out.count("hello"	) == 10
  assert out.count("there"	) == 10
  assert len(_lowerCAmelCase	) == 20

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

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







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
         self.adj_list[destination_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [source_vertex]
       else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   List[str]			      =  []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )
