
"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			(CMStochasticIterativeScheduler,)
     A__      =			10
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = {
           "num_train_timesteps": 201,
           "sigma_min": 0.002,
           "sigma_max": 80.0,
       }

       config.update(**snake_case__				)
       return config
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 10

       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				int							    = self.scheduler_classes[0](**snake_case__				)

       scheduler.set_timesteps(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = scheduler.timesteps[0]
       _SCREAMING_SNAKE_CASE   :				int							    = scheduler.timesteps[1]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.dummy_sample
       _SCREAMING_SNAKE_CASE   :				Any							    = 0.1 * sample

       _SCREAMING_SNAKE_CASE   :				Any							    = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__				).prev_sample
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__				).prev_sample

       self.assertEqual(output_a.shape  ,     sample.shape				)
       self.assertEqual(output_a.shape  ,     output_a.shape				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				Tuple							    = scheduler_class(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = 1
       scheduler.set_timesteps(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = scheduler.timesteps

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = torch.manual_seed(0				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.dummy_model()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(snake_case__				):
         # 1. scale model input
         _SCREAMING_SNAKE_CASE   :				str							    = scheduler.scale_model_input(snake_case__  ,     snake_case__				)

         # 2. predict noise residual
         _SCREAMING_SNAKE_CASE   :				int							    = model(snake_case__  ,     snake_case__				)

         # 3. predict previous sample x_t-1
         _SCREAMING_SNAKE_CASE   :				str							    = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

         _SCREAMING_SNAKE_CASE   :				Tuple							    = pred_prev_sample

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = torch.sum(torch.abs(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = torch.mean(torch.abs(snake_case__				)				)

       assert abs(result_sum.item() - 192.7_614				) < 1E-2
       assert abs(result_mean.item() - 0.2_510				) < 1E-3
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				int							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = scheduler_class(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Any							    = [106, 0]
       scheduler.set_timesteps(timesteps=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = scheduler.timesteps

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.manual_seed(0				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.dummy_model()
       _SCREAMING_SNAKE_CASE   :				Any							    = self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         _SCREAMING_SNAKE_CASE   :				str							    = scheduler.scale_model_input(snake_case__  ,     snake_case__				)

         # 2. predict noise residual
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = model(snake_case__  ,     snake_case__				)

         # 3. predict previous sample x_t-1
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pred_prev_sample

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.sum(torch.abs(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.mean(torch.abs(snake_case__				)				)

       assert abs(result_sum.item() - 347.6_357				) < 1E-2
       assert abs(result_mean.item() - 0.4_527				) < 1E-3
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = scheduler_class(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [39, 30, 12, 15, 0]

       with self.assertRaises(snake_case__  ,     msg="`timesteps` must be in descending order."				):
         scheduler.set_timesteps(timesteps=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				Tuple							    = scheduler_class(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = [39, 30, 12, 1, 0]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = len(snake_case__				)

       with self.assertRaises(snake_case__  ,     msg="Can only pass one of `num_inference_steps` or `timesteps`."				):
         scheduler.set_timesteps(num_inference_steps=snake_case__  ,     timesteps=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = scheduler_class(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           snake_case__  ,     msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"  ,     ):
         scheduler.set_timesteps(timesteps=snake_case__				)





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



