





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[str]:
 assert isinstance(_lowerCAmelCase							,     _lowerCAmelCase  )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"							,     [False, True]  )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      List[str] 			=     tmp_path / "cache"
 UpperCamelCase			:      Tuple 			=     {"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCamelCase			:      List[Any] 			=     TextDatasetReader(_lowerCAmelCase							,     cache_dir=_lowerCAmelCase							,     keep_in_memory=_lowerCAmelCase  ).read()
 _check_text_dataset(_lowerCAmelCase							,     _lowerCAmelCase  )



@pytest.mark.parametrize(
    "features"							,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,     )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Union[str, Any] 			=     tmp_path / "cache"
 UpperCamelCase			:      Tuple 			=     {"text": "string"}
 UpperCamelCase			:      List[Any] 			=     features.copy() if features else default_expected_features
 UpperCamelCase			:      Optional[int] 			=     (
     Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
 )
 UpperCamelCase			:      Union[str, Any] 			=     TextDatasetReader(_lowerCAmelCase							,     features=_lowerCAmelCase							,     cache_dir=_lowerCAmelCase  ).read()
 _check_text_dataset(_lowerCAmelCase							,     _lowerCAmelCase  )



@pytest.mark.parametrize("split"							,     [None, NamedSplit("train"  ), "train", "test"]  )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      Any 			=     tmp_path / "cache"
 UpperCamelCase			:      str 			=     {"text": "string"}
 UpperCamelCase			:      Union[str, Any] 			=     TextDatasetReader(_lowerCAmelCase							,     cache_dir=_lowerCAmelCase							,     split=_lowerCAmelCase  ).read()
 _check_text_dataset(_lowerCAmelCase							,     _lowerCAmelCase  )
 assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"							,     [str, list]  )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[str]:
 if issubclass(_lowerCAmelCase							,     _lowerCAmelCase  ):
  UpperCamelCase			:      List[str] 			=     text_path
 elif issubclass(_lowerCAmelCase							,     _lowerCAmelCase  ):
  UpperCamelCase			:      Optional[int] 			=     [text_path]
 UpperCamelCase			:      int 			=     tmp_path / "cache"
 UpperCamelCase			:      Tuple 			=     {"text": "string"}
 UpperCamelCase			:      Optional[int] 			=     TextDatasetReader(_lowerCAmelCase							,     cache_dir=_lowerCAmelCase  ).read()
 _check_text_dataset(_lowerCAmelCase							,     _lowerCAmelCase  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=("train",)  )		->				Optional[int]:
 assert isinstance(_lowerCAmelCase							,     _lowerCAmelCase  )
 for split in splits:
  UpperCamelCase			:      Union[str, Any] 			=     dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"							,     [False, True]  )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Dict 			=     tmp_path / "cache"
 UpperCamelCase			:      Tuple 			=     {"text": "string"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCamelCase			:      Any 			=     TextDatasetReader({"train": text_path}							,     cache_dir=_lowerCAmelCase							,     keep_in_memory=_lowerCAmelCase  ).read()
 _check_text_datasetdict(_lowerCAmelCase							,     _lowerCAmelCase  )



@pytest.mark.parametrize(
    "features"							,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,     )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      Any 			=     tmp_path / "cache"
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCamelCase			:      Dict 			=     {"text": "string"}
 UpperCamelCase			:      Any 			=     features.copy() if features else default_expected_features
 UpperCamelCase			:      int 			=     (
     Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
 )
 UpperCamelCase			:      str 			=     TextDatasetReader({"train": text_path}							,     features=_lowerCAmelCase							,     cache_dir=_lowerCAmelCase  ).read()
 _check_text_datasetdict(_lowerCAmelCase							,     _lowerCAmelCase  )



@pytest.mark.parametrize("split"							,     [None, NamedSplit("train"  ), "train", "test"]  )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Dict:
 if split:
  UpperCamelCase			:      Optional[int] 			=     {split: text_path}
 else:
  UpperCamelCase			:      str 			=     "train"
  UpperCamelCase			:      Dict 			=     {"train": text_path, "test": text_path}
 UpperCamelCase			:      Dict 			=     tmp_path / "cache"
 UpperCamelCase			:      List[str] 			=     {"text": "string"}
 UpperCamelCase			:      Optional[int] 			=     TextDatasetReader(_lowerCAmelCase							,     cache_dir=_lowerCAmelCase  ).read()
 _check_text_datasetdict(_lowerCAmelCase							,     _lowerCAmelCase							,     splits=list(path.keys()  )  )
 assert all(dataset[split].split == split for split in path.keys()  )







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  )		->				Union[str, Any]:  # picklable for multiprocessing
 return x.sum()



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:  # picklable for multiprocessing
 return i + 1



@dataclass
class       A__     :
      _UpperCAmelCase			:int
      _UpperCAmelCase			:str



class       A__     ( __snake_case    ):





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




       UpperCamelCase			:      Optional[int] 			=     {}
       UpperCamelCase			:      Optional[Any] 			=     []
       UpperCamelCase			:      List[Any] 			=     1
       UpperCamelCase			:      Tuple 			=     [1, 2]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      Optional[Any] 			=     {"a": [1, 2], "b": [3, 4]}
       UpperCamelCase			:      Any 			=     {"a": {"1": 1}, "b": 2}
       UpperCamelCase			:      List[str] 			=     {"a": 1, "b": 2, "c": 3, "d": 4}
       UpperCamelCase			:      Dict 			=     {}
       UpperCamelCase			:      Any 			=     []
       UpperCamelCase			:      Any 			=     2
       UpperCamelCase			:      Any 			=     [2, 3]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 2, "b": 3}
       UpperCamelCase			:      List[Any] 			=     {"a": [2, 3], "b": [4, 5]}
       UpperCamelCase			:      Tuple 			=     {"a": {"1": 2}, "b": 3}
       UpperCamelCase			:      Dict 			=     {"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			:      List[str] 			=     {"a": np.eye(2     ), "b": np.zeros(3     ), "c": np.ones(2     )}
       UpperCamelCase			:      int 			=     {"a": 2, "b": 0, "c": 2}
       UpperCamelCase			:      Union[str, Any] 			=     {
           "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			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      List[Any] 			=     {"a": 3, "b": 4}
       UpperCamelCase			:      Tuple 			=     {"a": 5, "b": 6}
       UpperCamelCase			:      Union[str, Any] 			=     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			:str							=     'bar'


       UpperCamelCase			:      List[Any] 			=     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[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			:      Union[str, Any] 			=     {F"""{i}""": i for i in range(_lowerCAmelCase  )}
  UpperCamelCase			:      List[str] 			=     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			:      List[Any] 			=     gen_random_output()
       with temp_seed(42 ,							set_tensorflow=A_     ):
        UpperCamelCase			:      Dict 			=     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			:      Optional[Any] 			=     torch.nn.Linear(3 ,							2     )
        UpperCamelCase			:      Dict 			=     torch.rand(1 ,							3     )
        return model(A_     ).detach().numpy()

       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Dict 			=     gen_random_output()
       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Optional[int] 			=     gen_random_output()
       UpperCamelCase			:      List[Any] 			=     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			:      Optional[Any] 			=     gen_random_output()
       with temp_seed(42     ):
        UpperCamelCase			:      Optional[Any] 			=     gen_random_output()
       UpperCamelCase			:      Optional[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  )		->				List[Any]:
 UpperCamelCase			:      Optional[Any] 			=     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  )		->				Tuple:
 UpperCamelCase			:      Dict 			=     NestedDataStructure(_lowerCAmelCase  ).flatten()
 assert output == expected_output



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      str 			=     A(x=1							,     y="foobar"  )
 UpperCamelCase			:      Tuple 			=     {"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  )		->				Tuple:
 return text.split()



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



def 	A_       (  )		->				str:
 with Pool(2  ) as pool:
  UpperCamelCase			:      List[str] 			=     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			:      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 that we get items as fast as possible
 with Pool(2  ) as pool:
  UpperCamelCase			:      Any 			=     []
  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
