
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__       (			A_   ):  # picklable for multiprocessing






  '''simple docstring'''





  return x.sum()

def a__       (			A_   ):  # picklable for multiprocessing






  '''simple docstring'''





  return i + 1




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42







class 			UpperCAmelCase_     (					_UpperCamelCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Dict       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			{}
    __magic_name__								=			[]
    __magic_name__								=			1
    __magic_name__								=			[1, 2]
    __magic_name__								=			{'a': 1, 'b': 2}
    __magic_name__								=			{'a': [1, 2], 'b': [3, 4]}
    __magic_name__								=			{'a': {'1': 1}, 'b': 2}
    __magic_name__								=			{'a': 1, 'b': 2, 'c': 3, 'd': 4}
    __magic_name__								=			{}
    __magic_name__								=			[]
    __magic_name__								=			2
    __magic_name__								=			[2, 3]
    __magic_name__								=			{'a': 2, 'b': 3}
    __magic_name__								=			{'a': [2, 3], 'b': [4, 5]}
    __magic_name__								=			{'a': {'1': 2}, 'b': 3}
    __magic_name__								=			{'a': 2, 'b': 3, 'c': 4, 'd': 5}
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    self.assertEqual(map_nested(_UpperCAmelCase     ,				_UpperCAmelCase       )     ,				_UpperCAmelCase       )

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

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







    """simple docstring"""






    __magic_name__								=			{'a': 1, 'b': 2}
    __magic_name__								=			{'a': 3, 'b': 4}
    __magic_name__								=			{'a': 5, 'b': 6}
    __magic_name__								=			sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]       )
    self.assertEqual(sorted(zip_dict(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )       )     ,				_UpperCAmelCase       )





  def 				_lowercase				(					self     :					str       )     ->      str:







    """simple docstring"""






    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						'bar'







    __magic_name__								=			Foo()
    self.assertEqual(foo.my_attr     ,				"""bar"""       )
    with temporary_assignment(_UpperCAmelCase     ,				"""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__       (			A_,	A_,	A_   ):






  '''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:
    __magic_name__								=			{f'''{i}''': i for i in range(lowerCAmelCase_   )}
    __magic_name__								=			map_nested(lambda A_   : 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 			UpperCAmelCase_     (					_UpperCamelCase	):


  '''simple docstring'''







  @require_tf
  def 				_lowercase				(					self     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    import tensorflow as tf
    from tensorflow.keras import layers

    __magic_name__								=			layers.Dense(2       )

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

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

    np.testing.assert_equal(_UpperCAmelCase     ,				_UpperCAmelCase       )
    self.assertGreater(np.abs(outa - outa       ).sum()     ,				0       )
  @require_torch
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[Any]:







    """simple docstring"""






    import torch

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

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

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





  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Union[str, Any]:







    """simple docstring"""






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

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

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







@pytest.mark.parametrize("""input_data""",	[{}]   )
def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			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__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			NestedDataStructure(lowerCAmelCase_   ).flatten()
  assert output == expected_output

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			A(x=1,	y="""foobar"""   )
  __magic_name__								=			{'x': 1, 'y': 'foobar'}
  assert asdict(lowerCAmelCase_   ) == expected_output

  __magic_name__								=			{'a': {'b': A(x=10,	y="""foo"""   )}, 'c': [A(x=20,	y="""bar"""   )]}
  __magic_name__								=			{'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__       (			A_   ):






  '''simple docstring'''





  return text.split()

def a__       (			A_   ):






  '''simple docstring'''





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





def a__       (			):






  '''simple docstring'''





  with Pool(2   ) as pool:
    __magic_name__								=			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:
    __magic_name__								=			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:
    __magic_name__								=			[]
    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






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Optional[int]      ,*_UpperCAmelCase   :			List[Any]      ,**_UpperCAmelCase   :			Union[str, Any]						):
									warnings.warn(
									    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use GLPNImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




