






'''simple docstring'''


import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						lowercase_      :				List[Any]   					=    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.''',
						)
						lowercase_      :				Dict   					=    parser.parse_args()

						lowercase_      :				Union[str, Any]   					=    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)







'''simple docstring'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

						with open(os.path.join(lowercase_ ,  """git_log.json"""						) ,  """w"""						) as f:
												json.dump(lowercase_ ,  lowercase_ ,  indent=4						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if params.n_gpu <= 0:
												lowercase        =							0
												lowercase        =							-1
												lowercase        =							True
												lowercase        =							False
												return

						assert torch.cuda.is_available()

						logger.info("""Initializing GPUs"""						)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase        =							int(os.environ["""WORLD_SIZE"""]						)
												lowercase        =							int(os.environ["""N_GPU_NODE"""]						)
												lowercase        =							int(os.environ["""RANK"""]						)

												# number of nodes / node ID
												lowercase        =							params.world_size // params.n_gpu_per_node
												lowercase        =							params.global_rank // params.n_gpu_per_node
												lowercase        =							True

												assert params.n_nodes == int(os.environ["""N_NODES"""]						)
												assert params.node_id == int(os.environ["""NODE_RANK"""]						)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase        =							1
												lowercase        =							0
												lowercase        =							0
												lowercase        =							0
												lowercase        =							1
												lowercase        =							1
												lowercase        =							False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase        =							params.node_id == 0 and params.local_rank == 0
						lowercase        =							params.n_nodes > 1

						# summary
						lowercase        =							F"""--- Global rank: {params.global_rank} - """
						logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
						logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
						logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
						logger.info(PREFIX + """World size     : %i""" % params.world_size						)
						logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
						logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
						logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
						logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
						logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

						# set GPU device
						torch.cuda.set_device(params.local_rank						)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info("""Initializing PyTorch distributed"""						)
												torch.distributed.init_process_group(
												    init_method="""env://""" ,  backend="""nccl""" ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


