
"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   (DDIMParallelScheduler,)
							_lowerCamelCase					:			List[str]          =   (("""eta""", 0.0), ("""num_inference_steps""", 50))
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							Any:




										'''simple docstring'''
										__lowercase       =       {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "clip_sample": True,
										}

										config.update(**_lowerCamelCase       )
										return config
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config(**_lowerCamelCase       )
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase   ,					__lowercase       =       10, 0.0

										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter

										scheduler.set_timesteps(_lowerCamelCase       )

										for t in scheduler.timesteps:
													__lowercase       =       model(_lowerCamelCase  ,    _lowerCamelCase       )
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       ).prev_sample

										return sample
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										for timesteps in [100, 500, 1_000]:
													self.check_over_configs(num_train_timesteps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_lowerCamelCase       )

										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config(steps_offset=1       )
										__lowercase       =       scheduler_class(**_lowerCamelCase       )
										scheduler.set_timesteps(5       )
										assert torch.equal(scheduler.timesteps  ,    torch.LongTensor([801, 601, 401, 201, 1]       )       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,    [0.002, 0.02, 0.2, 2]       ):
													self.check_over_configs(beta_start=_lowerCamelCase  ,    beta_end=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										self.check_over_configs(thresholding=_lowerCamelCase       )
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=_lowerCamelCase  ,    prediction_type=_lowerCamelCase  ,    sample_max_value=_lowerCamelCase  ,    )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[int]:




										'''simple docstring'''
										for t in [1, 10, 49]:
													self.check_over_forward(time_step=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										for t, num_inference_steps in zip([1, 10, 50]  ,    [10, 50, 500]       ):
													self.check_over_forward(time_step=_lowerCamelCase  ,    num_inference_steps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for t, eta in zip([1, 10, 49]  ,    [0.0, 0.5, 1.0]       ):
													self.check_over_forward(time_step=_lowerCamelCase  ,    eta=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										assert torch.sum(torch.abs(scheduler._get_variance(0  ,    0       ) - 0.0       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(420  ,    400       ) - 0.1_4771       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(980  ,    960       ) - 0.3_2460       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(0  ,    0       ) - 0.0       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487  ,    486       ) - 0.0_0979       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999  ,    998       ) - 0.02       )       ) < 1e-5
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase   ,					__lowercase       =       10, 0.0
										scheduler.set_timesteps(_lowerCamelCase       )

										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter
										__lowercase       =       self.dummy_sample_deter + 0.1
										__lowercase       =       self.dummy_sample_deter - 0.1

										__lowercase       =       samplea.shape[0]
										__lowercase       =       torch.stack([samplea, samplea, samplea]  ,    dim=0       )
										__lowercase       =       torch.arange(_lowerCamelCase       )[0:3, None].repeat(1  ,    _lowerCamelCase       )

										__lowercase       =       model(samples.flatten(0  ,    1       )  ,    timesteps.flatten(0  ,    1       )       )
										__lowercase       =       scheduler.batch_step_no_noise(_lowerCamelCase  ,    timesteps.flatten(0  ,    1       )  ,    samples.flatten(0  ,    1       )  ,    _lowerCamelCase       )

										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 1147.7904       ) < 1e-2
										assert abs(result_mean.item() - 0.4982       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       self.full_loop()

										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 172.0067       ) < 1e-2
										assert abs(result_mean.item() - 0.22_3967       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.full_loop(prediction_type="v_prediction"       )

										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 52.5302       ) < 1e-2
										assert abs(result_mean.item() - 0.0684       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										# We specify different beta, so that the first alpha is 0.99
										__lowercase       =       self.full_loop(set_alpha_to_one=_lowerCamelCase  ,    beta_start=0.01       )
										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 149.8295       ) < 1e-2
										assert abs(result_mean.item() - 0.1951       ) < 1e-3





							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										# We specify different beta, so that the first alpha is 0.99
										__lowercase       =       self.full_loop(set_alpha_to_one=_lowerCamelCase  ,    beta_start=0.01       )
										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 149.0784       ) < 1e-2
										assert abs(result_mean.item() - 0.1941       ) < 1e-3







"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase							=				[
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]



def      lowerCAmelCase__			(		__magic_name__=True      )						->int:
			if with_config:
						return [
						    {
						        "testcase_name": d["dataset"] + "/" + d["config_name"],
						        "dataset": d["dataset"],
						        "config_name": d["config_name"],
						    }
						    for d in DATASETS_ON_HF_GCP
						]
			else:
						return [
						    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
						]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a       )       )
class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   None
							_lowerCamelCase					:			int          =   None
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										with TemporaryDirectory() as tmp_dir:
													__lowercase       =       dataset_module_factory(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )

													__lowercase       =       import_main_class(dataset_module.module_path  ,    dataset=_lowerCamelCase       )

													__lowercase       =       builder_cls(
													    cache_dir=_lowerCamelCase  ,    config_name=_lowerCamelCase  ,    hash=dataset_module.hash  ,    )

													__lowercase       =       "/".join(
													    [
													        HF_GCP_BASE_URL,
													        builder_instance._relative_data_dir(with_hash=_lowerCamelCase       ).replace(os.sep  ,    "/"       ),
													        config.DATASET_INFO_FILENAME,
													    ]       )
													__lowercase       =       cached_path(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )
													self.assertTrue(os.path.exists(_lowerCamelCase       )       )





@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->str:
			__lowercase       =       tmp_path_factory.mktemp("test_hf_gcp"      ) / "test_wikipedia_simple"
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			__lowercase       =       None
			builder_instance.download_and_prepare()
			__lowercase       =       builder_instance.as_dataset()
			assert ds







@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->Any:
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path			,   dataset=__magic_name__      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			__lowercase       =       builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__magic_name__			,   __magic_name__      )
			assert "train" in ds
			assert isinstance(ds["train"]			,   __magic_name__      )
			assert next(iter(ds["train"]      )      )





