




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		:				int      		=[
    {"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_					(  _lowercase    :       List[Any]=True)							->	str:







			"""simple docstring"""
			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        snake_case__    (a__		):







						"""simple docstring"""




						__lowerCAmelCase					:int      =					None
						__lowerCAmelCase					:Dict      =					None

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									with TemporaryDirectory() as tmp_dir:
												a__       :						Dict							=  dataset_module_factory(SCREAMING_SNAKE_CASE_ ,       cache_dir=SCREAMING_SNAKE_CASE_  )

												a__       :						Tuple							=  import_main_class(dataset_module.module_path ,       dataset=SCREAMING_SNAKE_CASE_  )

												a__       :						DatasetBuilder							=  builder_cls(
												    cache_dir=SCREAMING_SNAKE_CASE_ ,       config_name=SCREAMING_SNAKE_CASE_ ,       hash=dataset_module.hash ,       )

												a__       :						Optional[int]							=  '/'.join(
												    [
												        HF_GCP_BASE_URL,
												        builder_instance._relative_data_dir(with_hash=SCREAMING_SNAKE_CASE_  ).replace(os.sep ,       """/"""  ),
												        config.DATASET_INFO_FILENAME,
												    ]  )
												a__       :						List[str]							=  cached_path(SCREAMING_SNAKE_CASE_ ,       cache_dir=SCREAMING_SNAKE_CASE_  )
												self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE_  )  )




@pytest.mark.integration
def      lowerCAmelCase_					(  _lowercase    :       Any)							->	List[str]:







			"""simple docstring"""
			a__       :						str							=  tmp_path_factory.mktemp("""test_hf_gcp""") / 'test_wikipedia_simple'
			a__       :						Tuple							=  dataset_module_factory("""wikipedia"""						,       cache_dir=snake_case__)
			a__       :						Dict							=  import_main_class(dataset_module.module_path)
			a__       :						DatasetBuilder							=  builder_cls(
			    cache_dir=snake_case__						,       config_name="""20220301.frr"""						,       hash=dataset_module.hash						,       )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			a__       :						List[Any]							=  None
			builder_instance.download_and_prepare()
			a__       :						Optional[int]							=  builder_instance.as_dataset()
			assert ds





@pytest.mark.integration
def      lowerCAmelCase_					(  _lowercase    :       Optional[Any])							->	Optional[Any]:







			"""simple docstring"""
			a__       :						List[str]							=  dataset_module_factory("""wikipedia"""						,       cache_dir=snake_case__)
			a__       :						int							=  import_main_class(dataset_module.module_path						,       dataset=snake_case__)
			a__       :						DatasetBuilder							=  builder_cls(
			    cache_dir=snake_case__						,       config_name="""20220301.frr"""						,       hash=dataset_module.hash						,       )
			a__       :						Dict							=  builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(snake_case__						,       snake_case__)
			assert "train" in ds
			assert isinstance(ds["""train"""]						,       snake_case__)
			assert next(iter(ds["""train"""]))









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




