



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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any]					):  # picklable for multiprocessing






 '''simple docstring'''

 return x.sum()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any]					):  # picklable for multiprocessing






 '''simple docstring'''

 return i + 1



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		(       UpperCAmelCase__	):

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


    """simple docstring"""






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

    a						=		2
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)
    self.assertEqual(map_nested(__lowerCAmelCase							,   __lowerCAmelCase							,   num_proc=__lowerCAmelCase							)							,   __lowerCAmelCase							)

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

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


    """simple docstring"""






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




   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    class 		_lowercase		:
       _UpperCAmelCase     	=			'''bar'''

    a						=		Foo()
    self.assertEqual(foo.my_attr							,   "bar"							)
    with temporary_assignment(__lowerCAmelCase							,   "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 	UpperCAmelCase__      (      UpperCAmelCase__ :Any ,					UpperCAmelCase__ :str ,					UpperCAmelCase__ :str					):






 '''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:
  a						=		{F"""{i}""": i for i in range(UpperCAmelCase__					)}
  a						=		map_nested(lambda UpperCAmelCase__					: x + 10 ,					UpperCAmelCase__ ,					num_proc=UpperCAmelCase__ ,					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 		_lowercase		(       UpperCAmelCase__	):

   @require_tf
   def 		A							(      self			:       Dict							)       ->				Optional[Any]:


    """simple docstring"""






    import tensorflow as tf
    from tensorflow.keras import layers

    a						=		layers.Dense(2							)

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

    with temp_seed(42							,   set_tensorflow=__lowerCAmelCase							):
     a						=		gen_random_output()
    with temp_seed(42							,   set_tensorflow=__lowerCAmelCase							):
     a						=		gen_random_output()
    a						=		gen_random_output()

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

   @require_torch
   def 		A							(      self			:       Dict							)       ->				Tuple:


    """simple docstring"""






    import torch

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

    with temp_seed(42							,   set_pytorch=__lowerCAmelCase							):
     a						=		gen_random_output()
    with temp_seed(42							,   set_pytorch=__lowerCAmelCase							):
     a						=		gen_random_output()
    a						=		gen_random_output()

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




   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






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

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

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


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






 '''simple docstring'''

 a						=		NestedDataStructure(UpperCAmelCase__					).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 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		NestedDataStructure(UpperCAmelCase__					).flatten()
 assert output == expected_output


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

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

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

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 return text.split()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

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




def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

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

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

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(UniPCMultistepScheduler,)
   _UpperCAmelCase     	=			(('''num_inference_steps''', 25),)

   def 		A							(      self			:       List[Any]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "solver_order": 2,
        "solver_type": "bh2",
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]=0							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)
     # copy over dummy past residuals
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      new_scheduler.set_timesteps(__lowerCAmelCase							)
      # copy over dummy past residuals
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a		,  a						=		sample, sample
     for t in range(__lowerCAmelCase							,   time_step + scheduler.config.solver_order + 1							):
      a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
      a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

      assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]=0							,   **__lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)

     # copy over dummy past residuals (must be after setting timesteps)
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      # copy over dummy past residuals
      new_scheduler.set_timesteps(__lowerCAmelCase							)

      # copy over dummy past residual (must be after setting timesteps)
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any=None							,   **__lowerCAmelCase			:       List[str]							)       ->				Any:


    """simple docstring"""






    if scheduler is None:
     a						=		self.scheduler_classes[0]
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(**__lowerCAmelCase							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    return sample

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)

    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)

     a						=		self.dummy_sample
     a						=		0.1 * sample

     if num_inference_steps is not None and hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      scheduler.set_timesteps(__lowerCAmelCase							)
     elif num_inference_steps is not None and not hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      a						=		num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     a						=		scheduler.timesteps[5]
     a						=		scheduler.timesteps[6]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     self.assertEqual(output_a.shape							,   sample.shape							)
     self.assertEqual(output_a.shape							,   output_a.shape							)

   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		UniPCMultistepScheduler(**self.get_scheduler_config()							)
    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

    a						=		DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    a						=		DEISMultistepScheduler.from_config(scheduler.config							)
    a						=		DPMSolverMultistepScheduler.from_config(scheduler.config							)
    a						=		UniPCMultistepScheduler.from_config(scheduler.config							)

    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """simple docstring"""






    for timesteps in [25, 50, 100, 999, 1000]:
     self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    self.check_over_configs(thresholding=__lowerCAmelCase							)
    for order in [1, 2, 3]:
     for solver_type in ["bh1", "bh2"]:
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            thresholding=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   sample_max_value=__lowerCAmelCase							,   solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   )

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for solver_type in ["bh1", "bh2"]:
     for order in [1, 2, 3]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       a						=		self.full_loop(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       assert not torch.isnan(__lowerCAmelCase							).any(), "Samples have nan numbers"

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    self.check_over_configs(lower_order_final=__lowerCAmelCase							)
    self.check_over_configs(lower_order_final=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
     self.check_over_forward(num_inference_steps=__lowerCAmelCase							,   time_step=0							)

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop()
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(thresholding=__lowerCAmelCase							,   dynamic_thresholding_ratio=0							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    assert sample.dtype == torch.floataa




   def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

     scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
     assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps

