



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							__snake_case    :		Tuple  =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=5_1_2,
							    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      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





											'''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)

							__snake_case    :		int  =parser.parse_args()

							__snake_case    :		Dict  =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)






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

