



"""simple docstring"""
import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

   _UpperCAmelCase     =	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 typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



