


"""simple docstring"""







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


lowerCAmelCase__ :    Optional[Any]				    =       [
    {'''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  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]=True    ):


  '''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 				SCREAMING_SNAKE_CASE__    (		A_    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =None

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         with TemporaryDirectory() as tmp_dir:
           lowerCAmelCase					:					str								=							dataset_module_factory(snake_case__		,					cache_dir=snake_case__						)

           lowerCAmelCase					:					Tuple								=							import_main_class(dataset_module.module_path		,					dataset=snake_case__						)

           lowerCAmelCase					:					DatasetBuilder								=							builder_cls(
               cache_dir=snake_case__		,					config_name=snake_case__		,					hash=dataset_module.hash		,					)

           lowerCAmelCase					:					Union[str, Any]								=							"/".join(
               [
                   HF_GCP_BASE_URL,
                   builder_instance._relative_data_dir(with_hash=snake_case__						).replace(os.sep		,					"/"						),
                   config.DATASET_INFO_FILENAME,
               ]						)
           lowerCAmelCase					:					Optional[Any]								=							cached_path(snake_case__		,					cache_dir=snake_case__						)
           self.assertTrue(os.path.exists(snake_case__						)						)

@pytest.mark.integration
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







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





@pytest.mark.integration
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







  lowerCAmelCase					:					Union[str, Any]								=							dataset_module_factory("wikipedia"    ,				cache_dir=__lowerCamelCase    )
  lowerCAmelCase					:					List[Any]								=							import_main_class(dataset_module.module_path    ,				dataset=__lowerCamelCase    )
  lowerCAmelCase					:					DatasetBuilder								=							builder_cls(
      cache_dir=__lowerCamelCase    ,				config_name="20220301.frr"    ,				hash=dataset_module.hash    ,				)
  lowerCAmelCase					:					Optional[Any]								=							builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(__lowerCamelCase    ,				__lowerCamelCase    )
  assert "train" in ds
  assert isinstance(ds["train"]    ,				__lowerCamelCase    )
  assert next(iter(ds["train"]    )    )









"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
     def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''simple docstring'''







       if string == "True":
         return True
       elif string == "False":
         return False
       else:
         raise ValueError(f"""could not parse string as bool {string}"""    )

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





