



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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.""",
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()

							__SCREAMING_SNAKE_CASE           =  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 unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class 	lowerCamelCase_    :






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									return self.get_dummy_input()







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									if self.block_type == "down":
												return (4, 32, 16, 16)
									elif self.block_type == "mid":
												return (4, 32, 32, 32)
									elif self.block_type == "up":
												return (4, 32, 64, 64)

									raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Optional[int]=False	,	)      ->				Dict:
									A							:					Optional[Any]              =						4
									A							:					List[str]              =						32
									A							:					Any              =						(32, 32)

									A							:					str              =						torch.manual_seed(0 )
									A							:					int              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						(batch_size, num_channels) + sizes
									A							:					Dict              =						randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )
									A							:					int              =						{"hidden_states": hidden_states}

									if include_temb:
												A							:					Any              =						1_28
												A							:					List[str]              =						randn_tensor((batch_size, temb_channels)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									if include_res_hidden_states_tuple:
												A							:					str              =						torch.manual_seed(1 )
												A							:					Tuple              =						(randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase ),)

									if include_encoder_hidden_states:
												A							:					Dict              =						floats_tensor((batch_size, 32, 32) ).to(__lowerCamelCase )

									if include_skip_sample:
												A							:					Optional[int]              =						randn_tensor(((batch_size, 3) + sizes)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									return dummy_input







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						{
									    "in_channels": 32,
									    "out_channels": 32,
									    "temb_channels": 1_28,
									}
									if self.block_type == "up":
												A							:					Dict              =						32

									if self.block_type == "mid":
												init_dict.pop("out_channels" )

									A							:					str              =						self.dummy_input
									return init_dict, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Union[str, Any]:
									A       , A							:					str              =						self.prepare_init_args_and_inputs_for_common()
									A							:					List[Any]              =						self.block_class(**__lowerCamelCase )
									unet_block.to(__lowerCamelCase )
									unet_block.eval()

									with torch.no_grad():
												A							:					int              =						unet_block(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Union[str, Any]              =						output[0]

									self.assertEqual(output.shape	,	self.output_shape )

									A							:					Any              =						output[0, -1, -3:, -3:]
									A							:					Union[str, Any]              =						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
									assert torch_all_close(output_slice.flatten()	,	__lowerCamelCase	,	atol=5e-3 )







						@unittest.skipIf(torch_device == "mps"	,	"Training is not supported in mps" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.prepare_init_args_and_inputs_for_common()
									A							:					str              =						self.block_class(**__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.train()
									A							:					Optional[int]              =						model(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Optional[Any]              =						output[0]

									A							:					List[str]              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						randn_tensor(output.shape	,	device=__lowerCamelCase )
									A							:					Dict              =						torch.nn.functional.mse_loss(__lowerCamelCase	,	__lowerCamelCase )
									loss.backward()