


"""simple docstring"""
import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      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.',
				)
				__SCREAMING_SNAKE_CASE							:      int						=      parser.parse_args()

				__SCREAMING_SNAKE_CASE							:      Optional[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"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """mobilenet_v1"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: List[Any]=224						,    UpperCAmelCase_							: List[Any]=1.0						,    UpperCAmelCase_							: Any=8						,    UpperCAmelCase_							: int="relu6"						,    UpperCAmelCase_							: str=True						,    UpperCAmelCase_							: List[str]=0.999						,    UpperCAmelCase_							: Optional[Any]=0.02						,    UpperCAmelCase_							: List[Any]=0.001						,    **UpperCAmelCase_							: Any						,    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														if depth_multiplier <= 0:
																					raise ValueError("""depth_multiplier must be greater than zero."""    )

														snake_case_          =						num_channels
														snake_case_          =						image_size
														snake_case_          =						depth_multiplier
														snake_case_          =						min_depth
														snake_case_          =						hidden_act
														snake_case_          =						tf_padding
														snake_case_          =						classifier_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						layer_norm_eps





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						int							 =      version.parse("""1.11""")



							@property
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														return OrderedDict([("""pixel_values""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task == "image-classification":
																					return OrderedDict([("""logits""", {0: """batch"""})]    )
														else:
																					return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->float:



														"""simple docstring"""



														return 1E-4



