
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


UpperCAmelCase       =					{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase       =					[
	    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ErnieForCausalLM''',
	    '''ErnieForMaskedLM''',
	    '''ErnieForMultipleChoice''',
	    '''ErnieForNextSentencePrediction''',
	    '''ErnieForPreTraining''',
	    '''ErnieForQuestionAnswering''',
	    '''ErnieForSequenceClassification''',
	    '''ErnieForTokenClassification''',
	    '''ErnieModel''',
	    '''ErniePreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase       =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''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    :							List[str]										=    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    :							List[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)

