"""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__: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.""",
   )
   SCREAMING_SNAKE_CASE__:str			=	parser.parse_args()

   SCREAMING_SNAKE_CASE__: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 typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
