






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	a_							=			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.''',
	)

	a_							=			parser.parse_args()

	a_							=			UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	a_							=			CLIPImageProcessor()
	a_							=			CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	a_							=			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)









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	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    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}}'      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )
							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    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   ,							)
							snake_case_							=    dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__      ) == 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)      )
							snake_case_							=    yaml.safe_dump(UpperCamelCase__      )
							snake_case_							=    yaml.safe_load(UpperCamelCase__      )
							assert dataset_info_yaml_dict == reloaded





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo()
							snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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




