

"""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 	A       (     snake_case__    ):  # picklable for multiprocessing




			'''simple docstring'''


			return x.sum()

def 	A       (     snake_case__    ):  # picklable for multiprocessing




			'''simple docstring'''


			return i + 1





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		int
			lowerCamelCase__   :		str





class    lowerCamelCase						(A__			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      {}
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      [1, 2]
						SCREAMING_SNAKE_CASE__								=      {"""a""": 1, """b""": 2}
						SCREAMING_SNAKE_CASE__								=      {"""a""": [1, 2], """b""": [3, 4]}
						SCREAMING_SNAKE_CASE__								=      {"""a""": {"""1""": 1}, """b""": 2}
						SCREAMING_SNAKE_CASE__								=      {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
						SCREAMING_SNAKE_CASE__								=      {}
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      2
						SCREAMING_SNAKE_CASE__								=      [2, 3]
						SCREAMING_SNAKE_CASE__								=      {"""a""": 2, """b""": 3}
						SCREAMING_SNAKE_CASE__								=      {"""a""": [2, 3], """b""": [4, 5]}
						SCREAMING_SNAKE_CASE__								=      {"""a""": {"""1""": 2}, """b""": 3}
						SCREAMING_SNAKE_CASE__								=      {"""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       )

						SCREAMING_SNAKE_CASE__								=      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       )

						SCREAMING_SNAKE_CASE__								=      {"""a""": np.eye(2       ), """b""": np.zeros(3       ), """c""": np.ones(2       )}
						SCREAMING_SNAKE_CASE__								=      {"""a""": 2, """b""": 0, """c""": 2}
						SCREAMING_SNAKE_CASE__								=      {
						    """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 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      {"""a""": 1, """b""": 2}
						SCREAMING_SNAKE_CASE__								=      {"""a""": 3, """b""": 4}
						SCREAMING_SNAKE_CASE__								=      {"""a""": 5, """b""": 6}
						SCREAMING_SNAKE_CASE__								=      sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]       )
						self.assertEqual(sorted(zip_dict(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )       )    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Any:





						class    lowerCamelCase						:
									lowerCamelCase__   :		Dict							=							'bar'
						SCREAMING_SNAKE_CASE__								=      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       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''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:
						SCREAMING_SNAKE_CASE__								=      {f"""{i}""": i for i in range(snake_case__    )}
						SCREAMING_SNAKE_CASE__								=      map_nested(lambda snake_case__    : x + 10		,				snake_case__		,				num_proc=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    lowerCamelCase						(A__			):

			@require_tf
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						import tensorflow as tf
						from tensorflow.keras import layers

						SCREAMING_SNAKE_CASE__								=      layers.Dense(2       )

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

						with temp_seed(4_2    ,				set_tensorflow=__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						with temp_seed(4_2    ,				set_tensorflow=__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						SCREAMING_SNAKE_CASE__								=      gen_random_output()

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

			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
						import torch

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

						with temp_seed(4_2    ,				set_pytorch=__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						with temp_seed(4_2    ,				set_pytorch=__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						SCREAMING_SNAKE_CASE__								=      gen_random_output()

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						def gen_random_output():
									return np.random.rand(1    ,				3       )

						with temp_seed(4_2       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						with temp_seed(4_2       ):
									SCREAMING_SNAKE_CASE__								=      gen_random_output()
						SCREAMING_SNAKE_CASE__								=      gen_random_output()

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

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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      NestedDataStructure(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 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      NestedDataStructure(snake_case__    ).flatten()
			assert output == expected_output

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      A(x=1		,				y="""foobar"""    )
			SCREAMING_SNAKE_CASE__								=      {"""x""": 1, """y""": """foobar"""}
			assert asdict(snake_case__    ) == expected_output

			SCREAMING_SNAKE_CASE__								=      {"""a""": {"""b""": A(x=10		,				y="""foo"""    )}, """c""": [A(x=20		,				y="""bar"""    )]}
			SCREAMING_SNAKE_CASE__								=      {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
			assert asdict(snake_case__    ) == expected_output

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

def 	A       (     snake_case__    ):




			'''simple docstring'''


			return text.split()

def 	A       (     snake_case__    ):




			'''simple docstring'''


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




def 	A       (     ):




			'''simple docstring'''


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

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

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





"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler


