

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
	A_       : Dict         =			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.',
	)
	A_       : str         =			parser.parse_args()

	A_       : Optional[int]         =			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 argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



