
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


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  TypeVar('''DatasetType''', Dataset, IterableDataset)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = "first_exhausted"    ,		):
    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(__SCREAMING_SNAKE_CASE							):
        if not isinstance(__SCREAMING_SNAKE_CASE    ,		(Dataset, IterableDataset)							):
            if isinstance(__SCREAMING_SNAKE_CASE    ,		(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(__SCREAMING_SNAKE_CASE							)}\n"""
                    F"""Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__SCREAMING_SNAKE_CASE							)							)}\']"""							)
            raise ValueError(
                F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__SCREAMING_SNAKE_CASE							).__name__}."""							)
        if i == 0:
            snake_case__						=				(
                (Dataset, IterableDataset) if isinstance(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE							) else (IterableDataset, Dataset)
            )
        elif not isinstance(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE							):
            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(
            __SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE    ,		info=__SCREAMING_SNAKE_CASE    ,		split=__SCREAMING_SNAKE_CASE    ,		stopping_strategy=__SCREAMING_SNAKE_CASE							)
    else:
        return _interleave_iterable_datasets(
            __SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE    ,		info=__SCREAMING_SNAKE_CASE    ,		split=__SCREAMING_SNAKE_CASE    ,		stopping_strategy=__SCREAMING_SNAKE_CASE							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = 0    ,		):






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





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         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)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  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)



