



"""simple docstring"""







import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							A    =			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    =			parser.parse_args()

							A    =			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"""







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def   lowerCAmelCase__			(     ) ->							Optional[Any]:
							import os as original_os
							from os import path as original_path
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

							A         =					'__test_patch_submodule_mock__'
							with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
														# Every way to access os.path.join must be patched, and the rest must stay untouched

														# check os.path.join
														assert isinstance(_test_patching.os				,      _PatchedModuleObj			)
														assert isinstance(_test_patching.os.path				,      _PatchedModuleObj			)
														assert _test_patching.os.path.join is mock

														# check path.join
														assert isinstance(_test_patching.path				,      _PatchedModuleObj			)
														assert _test_patching.path.join is mock

														# check join
														assert _test_patching.join is mock

														# check that the other attributes are untouched
														assert _test_patching.os.rename is original_rename
														assert _test_patching.path.dirname is original_dirname
														assert _test_patching.os.path.dirname is original_dirname

														# Even renamed modules or objects must be patched

														# check renamed_os.path.join
														assert isinstance(_test_patching.renamed_os				,      _PatchedModuleObj			)
														assert isinstance(_test_patching.renamed_os.path				,      _PatchedModuleObj			)
														assert _test_patching.renamed_os.path.join is mock

														# check renamed_path.join
														assert isinstance(_test_patching.renamed_path				,      _PatchedModuleObj			)
														assert _test_patching.renamed_path.join is mock

														# check renamed_join
														assert _test_patching.renamed_join is mock

														# check that the other attributes are untouched
														assert _test_patching.renamed_os.rename is original_rename
														assert _test_patching.renamed_path.dirname is original_dirname
														assert _test_patching.renamed_os.path.dirname is original_dirname

							# check that everthing is back to normal when the patch is over

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

def   lowerCAmelCase__			(     ) ->							str:
							assert _test_patching.open is open

							A         =					'__test_patch_submodule_builtin_mock__'
							# _test_patching has "open" in its globals
							assert _test_patching.open is open
							with patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			):
														assert _test_patching.open is mock

							# check that everthing is back to normal when the patch is over

							assert _test_patching.open is open

def   lowerCAmelCase__			(     ) ->							List[Any]:
							# pandas.read_csv is not present in _test_patching
							A         =					'__test_patch_submodule_missing_mock__'
							with patch_submodule(_test_patching				,      'pandas.read_csv'				,      lowerCamelCase__			):
														pass

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							# builtin should always be mocked even if they're not in the globals
							# in case they're loaded at one point
							A         =					'__test_patch_submodule_missing_builtin_mock__'
							# _test_patching doesn't have "len" in its globals
							assert getattr(_test_patching				,      'len'				,      lowerCamelCase__			) is None
							with patch_submodule(_test_patching				,      'len'				,      lowerCamelCase__			):
														assert _test_patching.len is mock
							assert _test_patching.len is len

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							A         =					'__test_patch_submodule_start_and_stop_mock__'
							A         =					patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			)
							assert _test_patching.open is open
							patch.start()
							assert _test_patching.open is mock
							patch.stop()
							assert _test_patching.open is open

def   lowerCAmelCase__			(     ) ->							int:
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							A         =					'__test_patch_submodule_successive_join__'
							A         =					'__test_patch_submodule_successive_dirname__'
							A         =					'__test_patch_submodule_successive_rename__'
							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

							with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.rename'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

    # try another order
							with patch_submodule(_test_patching				,      'os.rename'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

def   lowerCAmelCase__			(     ) ->							Optional[Any]:
							A         =					'__test_patch_submodule_doesnt_exist_mock__'
							with patch_submodule(_test_patching				,      '__module_that_doesn_exist__.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass
							with patch_submodule(_test_patching				,      'os.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass



