





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













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       (   __magic_name__	):  # picklable for multiprocessing
   return x.sum()


def _A       (   __magic_name__	):  # picklable for multiprocessing
   return i + 1





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42





class  lowerCAmelCase							(       lowercase_ ):







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  {}
     lowercase__			       =  []
     lowercase__			       =  1
     lowercase__			       =  [1, 2]
     lowercase__			       =  {"a": 1, "b": 2}
     lowercase__			       =  {"a": [1, 2], "b": [3, 4]}
     lowercase__			       =  {"a": {"1": 1}, "b": 2}
     lowercase__			       =  {"a": 1, "b": 2, "c": 3, "d": 4}
     lowercase__			       =  {}
     lowercase__			       =  []
     lowercase__			       =  2
     lowercase__			       =  [2, 3]
     lowercase__			       =  {"a": 2, "b": 3}
     lowercase__			       =  {"a": [2, 3], "b": [4, 5]}
     lowercase__			       =  {"a": {"1": 2}, "b": 3}
     lowercase__			       =  {"a": 2, "b": 3, "c": 4, "d": 5}
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase )   ,							_lowercase )

     lowercase__			       =  2
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )

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







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





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







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






     '''simple docstring'''





     class  lowerCAmelCase							:
       __lowerCamelCase		=      'bar'
     lowercase__			       =  Foo()
     self.assertEqual(foo.my_attr   ,							"bar" )
     with temporary_assignment(_lowercase   ,							"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       (   __magic_name__  , __magic_name__  , __magic_name__	):
   with patch("datasets.utils.py_utils._single_map_nested"	) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"	) as mock_multiprocessing_pool:
      lowercase__			       =  {f'''{i}''': i for i in range(__magic_name__	)}
      lowercase__			       =  map_nested(lambda __magic_name__	: x + 10  , __magic_name__  , num_proc=__magic_name__  , 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							(       lowercase_ ):







  @require_tf
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     import tensorflow as tf
     from tensorflow.keras import layers

     lowercase__			       =  layers.Dense(2 )

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

     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  @require_torch
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     import torch

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

     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





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

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

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


@pytest.mark.parametrize("input_data"  , [{}]	)
def _A       (   __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).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       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).flatten()
   assert output == expected_output


def _A       (   ):
   lowercase__			       =  A(x=1  , y="foobar"	)
   lowercase__			       =  {"x": 1, "y": "foobar"}
   assert asdict(__magic_name__	) == expected_output

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

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


def _A       (   __magic_name__	):
   return text.split()


def _A       (   __magic_name__	):
   yield (time.time(), content)
   time.sleep(2	)
   yield (time.time(), content)







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

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

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






