

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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:  # picklable for multiprocessing

     """simple docstring"""







     return x.sum()







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      str:  # picklable for multiprocessing

     """simple docstring"""







     return i + 1





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					int
 _lowercase     :					str





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    {}
      _lowerCAmelCase   :					Any        =    []
      _lowerCAmelCase   :					Dict        =    1
      _lowerCAmelCase   :					List[str]        =    [1, 2]
      _lowerCAmelCase   :					Union[str, Any]        =    {"a": 1, "b": 2}
      _lowerCAmelCase   :					List[str]        =    {"a": [1, 2], "b": [3, 4]}
      _lowerCAmelCase   :					List[Any]        =    {"a": {"1": 1}, "b": 2}
      _lowerCAmelCase   :					Any        =    {"a": 1, "b": 2, "c": 3, "d": 4}
      _lowerCAmelCase   :					int        =    {}
      _lowerCAmelCase   :					Tuple        =    []
      _lowerCAmelCase   :					Any        =    2
      _lowerCAmelCase   :					Optional[Any]        =    [2, 3]
      _lowerCAmelCase   :					Optional[int]        =    {"a": 2, "b": 3}
      _lowerCAmelCase   :					int        =    {"a": [2, 3], "b": [4, 5]}
      _lowerCAmelCase   :					int        =    {"a": {"1": 2}, "b": 3}
      _lowerCAmelCase   :					List[str]        =    {"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_       )

      _lowerCAmelCase   :					Optional[Any]        =    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_       )

      _lowerCAmelCase   :					Any        =    {"a": np.eye(2       ), "b": np.zeros(3       ), "c": np.ones(2       )}
      _lowerCAmelCase   :					List[str]        =    {"a": 2, "b": 0, "c": 2}
      _lowerCAmelCase   :					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    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      class A__    :



       """simple docstring"""







       _lowercase     :					str      =						'''bar'''

      _lowerCAmelCase   :					Dict        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







     with patch("datasets.utils.py_utils._single_map_nested"				) as mock_single_map_nested, patch(
         "datasets.parallel.parallel.Pool"				) as mock_multiprocessing_pool:
          _lowerCAmelCase   :					List[str]        =    {f'''{i}''': i for i in range(SCREAMING_SNAKE_CASE				)}
          _lowerCAmelCase   :					Optional[int]        =    map_nested(lambda SCREAMING_SNAKE_CASE				: x + 10		,							SCREAMING_SNAKE_CASE		,							num_proc=SCREAMING_SNAKE_CASE		,							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__    ( A ):



 """simple docstring"""







 @require_tf
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      import tensorflow as tf
      from tensorflow.keras import layers

      _lowerCAmelCase   :					Optional[Any]        =    layers.Dense(2       )

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

      with temp_seed(4_2     ,						set_tensorflow=A_       ):
           _lowerCAmelCase   :					Optional[Any]        =    gen_random_output()
      with temp_seed(4_2     ,						set_tensorflow=A_       ):
           _lowerCAmelCase   :					Union[str, Any]        =    gen_random_output()
      _lowerCAmelCase   :					Union[str, Any]        =    gen_random_output()

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

 @require_torch
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      import torch

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

      with temp_seed(4_2     ,						set_pytorch=A_       ):
           _lowerCAmelCase   :					str        =    gen_random_output()
      with temp_seed(4_2     ,						set_pytorch=A_       ):
           _lowerCAmelCase   :					List[str]        =    gen_random_output()
      _lowerCAmelCase   :					Optional[int]        =    gen_random_output()

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


 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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

      with temp_seed(4_2       ):
           _lowerCAmelCase   :					List[Any]        =    gen_random_output()
      with temp_seed(4_2       ):
           _lowerCAmelCase   :					Optional[int]        =    gen_random_output()
      _lowerCAmelCase   :					Optional[int]        =    gen_random_output()

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







@pytest.mark.parametrize("input_data"		,							[{}]				)
def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    NestedDataStructure(SCREAMING_SNAKE_CASE				).flatten()
     assert output == expected_output







def 	_snake_case		(    )       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    A(x=1		,							y="foobar"				)
     _lowerCAmelCase   :					List[Any]        =    {"x": 1, "y": "foobar"}
     assert asdict(SCREAMING_SNAKE_CASE				) == expected_output

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

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     return text.split()







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     yield (time.time(), content)
     time.sleep(2				)
     yield (time.time(), content)







def 	_snake_case		(    )       ->      List[str]:

     """simple docstring"""







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

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

     # check that we get items as fast as possible
     with Pool(2				) as pool:
          _lowerCAmelCase   :					Optional[Any]        =    []
          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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__UpperCAmelCase    				=     {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






