






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A     :      str   					=logging.get_logger(__name__)


_A     :      Tuple   					=TypeVar('''DatasetType''', Dataset, IterableDataset)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = "first_exhausted"    ,      )				->	int:
							from .arrow_dataset import Dataset
							from .iterable_dataset import IterableDataset

							if not datasets:
														raise ValueError("""Unable to interleave an empty list of datasets."""				)
							for i, dataset in enumerate(a_				):
														if not isinstance(a_    ,      (Dataset, IterableDataset)				):
																					if isinstance(a_    ,      (DatasetDict, IterableDatasetDict)				):
																												if not dataset:
																																			raise ValueError(
																																			    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
																																			    """is an empty dataset dictionary."""				)
																												raise ValueError(
																												    f'''Dataset at position {i} has at least one split: {list(a_				)}\n'''
																												    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(a_				)				)}\']'''				)
																					raise ValueError(
																					    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_				).__name__}.'''				)
														if i == 0:
																					lowerCamelCase__ :     Union[str, Any]	  =	(
																					    (Dataset, IterableDataset) if isinstance(a_    ,      a_				) else (IterableDataset, Dataset)
																					)
														elif not isinstance(a_    ,      a_				):
																					raise ValueError(
																					    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''				)
							if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
														raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''				)
							if dataset_type is Dataset:
														return _interleave_map_style_datasets(
														    a_    ,      a_    ,      a_    ,      info=a_    ,      split=a_    ,      stopping_strategy=a_				)
							else:
														return _interleave_iterable_datasets(
														    a_    ,      a_    ,      a_    ,      info=a_    ,      split=a_    ,      stopping_strategy=a_				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = 0    ,      )				->	Optional[int]:

							if not dsets:
														raise ValueError("""Unable to concatenate an empty list of datasets."""				)
							for i, dataset in enumerate(a_				):
														if not isinstance(a_    ,      (Dataset, IterableDataset)				):
																					if isinstance(a_    ,      (DatasetDict, IterableDatasetDict)				):
																												if not dataset:
																																			raise ValueError(
																																			    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
																																			    """is an empty dataset dictionary."""				)
																												raise ValueError(
																												    f'''Dataset at position {i} has at least one split: {list(a_				)}\n'''
																												    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(a_				)				)}\']'''				)
																					raise ValueError(
																					    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_				).__name__}.'''				)
														if i == 0:
																					lowerCamelCase__ :     Optional[int]	  =	(
																					    (Dataset, IterableDataset) if isinstance(a_    ,      a_				) else (IterableDataset, Dataset)
																					)
														elif not isinstance(a_    ,      a_				):
																					raise ValueError(
																					    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''				)
							if dataset_type is Dataset:
														return _concatenate_map_style_datasets(a_    ,      info=a_    ,      split=a_    ,      axis=a_				)
							else:
														return _concatenate_iterable_datasets(a_    ,      info=a_    ,      split=a_    ,      axis=a_				)









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=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     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=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)

