





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     _UpperCamelCase     (a__   :Any   ,			a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			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}}"""       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@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=42   ,			),
    ]   ,			)
def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :DatasetInfo       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_info.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfo.from_directory(a__       )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(a__   ,			"""dataset_info.json"""       )       )





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			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""": 42}]   ,			download_checksums={}   ,			download_size=1337   ,			post_processing_size=442   ,			dataset_size=1234   ,			size_in_bytes=1337 + 442 + 1234   ,			)
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert sorted(a__       ) == 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)       )
							UpperCamelCase__				     =			yaml.safe_dump(a__       )
							UpperCamelCase__				     =			yaml.safe_load(a__       )
							assert dataset_info_yaml_dict == reloaded





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			DatasetInfo()
							UpperCamelCase__				     =			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=42   ,			)
            }       ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42       ),
                """v2""": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]   ,			)
def     _UpperCamelCase     (a__   :int   ,			a__   :DatasetInfosDict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_infos_dict.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			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(a__   ,			"""README.md"""       )       )











import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()

					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

					parser.add_argument(
					    "--txt2img_unclip",
					    default="kakaobrain/karlo-v1-alpha",
					    type=str,
					    required=False,
					    help="The pretrained txt2img unclip.",
					)

					UpperCamelCase__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					UpperCamelCase__											= CLIPImageProcessor()
					UpperCamelCase__											= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

					UpperCamelCase__											= UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)




