





'''simple docstring'''






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

					lowerCAmelCase       		=      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 importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def      __A						(							a_						:  int	):
		return EnvironmentCommand()






def      __A						(							a_						:  Tuple	):
		return EnvironmentCommand(args.accelerate_config_file	)

class 		lowerCamelCase  (      _A       ):



	@staticmethod
	def   _lowerCamelCase    (   a_	):
			lowerCAmelCase     :    int						=   parser.add_parser("env"	)
			download_parser.set_defaults(func=a_	)
			download_parser.add_argument(
			    "--accelerate-config_file"		,      default=a_		,      help="The accelerate config file to use for the default values in the launching script."		,      )
			download_parser.set_defaults(func=a_	)



	def __init__(   self		,      a_		,      *a_	):
			lowerCAmelCase     :    List[str]						=   accelerate_config_file



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "not installed"
			if is_safetensors_available():
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   safetensors.__version__
			elif importlib.util.find_spec("safetensors"	) is not None:
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    List[str]						=   "not found"
			if is_accelerate_available():
					import accelerate
					from accelerate.commands.config import default_config_file, load_config_from_file

					lowerCAmelCase     :    int						=   accelerate.__version__
					# Get the default from the config file.
					if self._accelerate_config_file is not None or os.path.isfile(a_	):
							lowerCAmelCase     :    int						=   load_config_from_file(self._accelerate_config_file	).to_dict()

					lowerCAmelCase     :    Optional[int]						=   (
					    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
					    if isinstance(a_		,      a_	)
					    else F'''\t{accelerate_config}'''
					)

			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Union[str, Any]						=   "NA"
			if is_torch_available():
					import torch

					lowerCAmelCase     :    Optional[Any]						=   torch.__version__
					lowerCAmelCase     :    List[Any]						=   torch.cuda.is_available()

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_tf_available():
					import tensorflow as tf

					lowerCAmelCase     :    Optional[int]						=   tf.__version__
					try:
							# deprecated in v2.1
							lowerCAmelCase     :    List[str]						=   tf.test.is_gpu_available()
					except AttributeError:
							# returns list of devices, convert to bool
							lowerCAmelCase     :    List[Any]						=   bool(tf.config.list_physical_devices("GPU"	)	)

			lowerCAmelCase     :    Union[str, Any]						=   "not installed"
			lowerCAmelCase     :    Any						=   "not installed"
			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_flax_available():
					import flax
					import jax
					import jaxlib

					lowerCAmelCase     :    Union[str, Any]						=   flax.__version__
					lowerCAmelCase     :    int						=   jax.__version__
					lowerCAmelCase     :    Optional[Any]						=   jaxlib.__version__
					lowerCAmelCase     :    int						=   jax.lib.xla_bridge.get_backend().platform

			lowerCAmelCase     :    Optional[Any]						=   {
			    "`transformers` version": version,
			    "Platform": platform.platform(),
			    "Python version": platform.python_version(),
			    "Huggingface_hub version": huggingface_hub.__version__,
			    "Safetensors version": F'''{safetensors_version}''',
			    "Accelerate version": F'''{accelerate_version}''',
			    "Accelerate config": F'''{accelerate_config_str}''',
			    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
			    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
			    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
			    "Jax version": F'''{jax_version}''',
			    "JaxLib version": F'''{jaxlib_version}''',
			    "Using GPU in script?": "<fill in>",
			    "Using distributed or parallel set-up in script?": "<fill in>",
			}

			print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"	)
			print(self.format_dict(a_	)	)

			return info





	@staticmethod
	def   _lowerCamelCase    (   a_	):
			return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"



