

'''simple docstring'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 	_a       (				__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		assert isinstance(__lowerCAmelCase   ,							__lowerCAmelCase     )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('''keep_in_memory'''   ,							[False, True]     )
def 	_a       (				__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  tmp_path / '''cache'''
		snake_case__						:       List[Any]       						=  {'''text''': '''string'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				snake_case__						:       Tuple       						=  TextDatasetReader(__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase   ,							keep_in_memory=__lowerCAmelCase     ).read()
		_check_text_dataset(__lowerCAmelCase   ,							__lowerCAmelCase     )




@pytest.mark.parametrize(
    '''features'''   ,							[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]   ,							)
def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  tmp_path / '''cache'''
		snake_case__						:       List[Any]       						=  {'''text''': '''string'''}
		snake_case__						:       int       						=  features.copy() if features else default_expected_features
		snake_case__						:       Dict       						=  (
		    Features({feature: Value(__lowerCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		snake_case__						:       List[str]       						=  TextDatasetReader(__lowerCAmelCase   ,							features=__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase     ).read()
		_check_text_dataset(__lowerCAmelCase   ,							__lowerCAmelCase     )




@pytest.mark.parametrize('''split'''   ,							[None, NamedSplit('''train'''     ), '''train''', '''test''']     )
def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Union[str, Any]     ):

		"""simple docstring"""

		snake_case__						:       Tuple       						=  tmp_path / '''cache'''
		snake_case__						:       int       						=  {'''text''': '''string'''}
		snake_case__						:       str       						=  TextDatasetReader(__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase   ,							split=__lowerCAmelCase     ).read()
		_check_text_dataset(__lowerCAmelCase   ,							__lowerCAmelCase     )
		assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''   ,							[str, list]     )
def 	_a       (				__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      str     ):

		"""simple docstring"""

		if issubclass(__lowerCAmelCase   ,							__lowerCAmelCase     ):
				snake_case__						:       Dict       						=  text_path
		elif issubclass(__lowerCAmelCase   ,							__lowerCAmelCase     ):
				snake_case__						:       Union[str, Any]       						=  [text_path]
		snake_case__						:       int       						=  tmp_path / '''cache'''
		snake_case__						:       Any       						=  {'''text''': '''string'''}
		snake_case__						:       Optional[Any]       						=  TextDatasetReader(__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase     ).read()
		_check_text_dataset(__lowerCAmelCase   ,							__lowerCAmelCase     )




def 	_a       (				__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Dict=("train",)     ):

		"""simple docstring"""

		assert isinstance(__lowerCAmelCase   ,							__lowerCAmelCase     )
		for split in splits:
				snake_case__						:       List[str]       						=  dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('''keep_in_memory'''   ,							[False, True]     )
def 	_a       (				__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       int       						=  tmp_path / '''cache'''
		snake_case__						:       Tuple       						=  {'''text''': '''string'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				snake_case__						:       Union[str, Any]       						=  TextDatasetReader({'''train''': text_path}   ,							cache_dir=__lowerCAmelCase   ,							keep_in_memory=__lowerCAmelCase     ).read()
		_check_text_datasetdict(__lowerCAmelCase   ,							__lowerCAmelCase     )




@pytest.mark.parametrize(
    '''features'''   ,							[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]   ,							)
def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Union[str, Any]     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  tmp_path / '''cache'''
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		snake_case__						:       str       						=  {'''text''': '''string'''}
		snake_case__						:       List[str]       						=  features.copy() if features else default_expected_features
		snake_case__						:       Optional[Any]       						=  (
		    Features({feature: Value(__lowerCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		snake_case__						:       Union[str, Any]       						=  TextDatasetReader({'''train''': text_path}   ,							features=__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase     ).read()
		_check_text_datasetdict(__lowerCAmelCase   ,							__lowerCAmelCase     )





@pytest.mark.parametrize('''split'''   ,							[None, NamedSplit('''train'''     ), '''train''', '''test''']     )
def 	_a       (				__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if split:
				snake_case__						:       Optional[Any]       						=  {split: text_path}
		else:
				snake_case__						:       List[str]       						=  '''train'''
				snake_case__						:       Dict       						=  {'''train''': text_path, '''test''': text_path}
		snake_case__						:       Union[str, Any]       						=  tmp_path / '''cache'''
		snake_case__						:       int       						=  {'''text''': '''string'''}
		snake_case__						:       List[str]       						=  TextDatasetReader(__lowerCAmelCase   ,							cache_dir=__lowerCAmelCase     ).read()
		_check_text_datasetdict(__lowerCAmelCase   ,							__lowerCAmelCase   ,							splits=list(path.keys()     )     )
		assert all(dataset[split].split == split for split in path.keys()     )






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						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.""",
					)

					lowerCAmelCase__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						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)



