import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
	)
	# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
	parser.add_argument(
	    """--original_config_file""",
	    default=None,
	    type=str,
	    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(
	    """--scheduler_type""",
	    default="""pndm""",
	    type=str,
	    help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
	)
	parser.add_argument(
	    """--pipeline_type""",
	    default=None,
	    type=str,
	    help=(
	        """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
	        """. If `None` pipeline will be automatically inferred."""
	    ),
	)
	parser.add_argument(
	    """--image_size""",
	    default=None,
	    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(
	    """--prediction_type""",
	    default=None,
	    type=str,
	    help=(
	        """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
	        """ Diffusion v2 Base. Use 'v_prediction' 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.)""")
	parser.add_argument(
	    """--stable_unclip""",
	    type=str,
	    default=None,
	    required=False,
	    help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
	)
	parser.add_argument(
	    """--stable_unclip_prior""",
	    type=str,
	    default=None,
	    required=False,
	    help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
	)
	parser.add_argument(
	    """--clip_stats_path""",
	    type=str,
	    help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
	    required=False,
	)
	parser.add_argument(
	    """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
	)
	parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
	parser.add_argument(
	    """--vae_path""",
	    type=str,
	    default=None,
	    required=False,
	    help="""Set to a path, hub id to an already converted vae to not convert it again.""",
	)
	_UpperCAmelCase            =		parser.parse_args()

	_UpperCAmelCase            =		download_from_original_stable_diffusion_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    prediction_type=args.prediction_type,
	    model_type=args.pipeline_type,
	    extract_ema=args.extract_ema,
	    scheduler_type=args.scheduler_type,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    stable_unclip=args.stable_unclip,
	    stable_unclip_prior=args.stable_unclip_prior,
	    clip_stats_path=args.clip_stats_path,
	    controlnet=args.controlnet,
	    vae_path=args.vae_path,
	)

	if args.half:
		pipe.to(torch_dtype=torch.floataa)

	if args.controlnet:
		# only save the controlnet model
		pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
	else:
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






