






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  __a   :							Dict						= 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   :							Any						= 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)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__a   :							Dict						= {
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"realm"




     def __init__(					self							:      str			,						UpperCamelCase_							:      List[Any]=30_522			,						UpperCamelCase_							:      Dict=768			,						UpperCamelCase_							:      Union[str, Any]=128			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      Optional[Any]=12			,						UpperCamelCase_							:      Optional[int]=8			,						UpperCamelCase_							:      str=3_072			,						UpperCamelCase_							:      List[str]="gelu_new"			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      str=512			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      str=0.02			,						UpperCamelCase_							:      List[Any]=1e-12			,						UpperCamelCase_							:      str=256			,						UpperCamelCase_							:      List[str]=10			,						UpperCamelCase_							:      int=1e-3			,						UpperCamelCase_							:      List[str]=5			,						UpperCamelCase_							:      Tuple=320			,						UpperCamelCase_							:      List[str]=13_353_718			,						UpperCamelCase_							:      Tuple=5_000			,						UpperCamelCase_							:      Optional[int]=1			,						UpperCamelCase_							:      List[str]=0			,						UpperCamelCase_							:      Union[str, Any]=2			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         # Common config
         __A       =						vocab_size
         __A       =						max_position_embeddings
         __A       =						hidden_size
         __A       =						retriever_proj_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						num_candidates
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						initializer_range
         __A       =						type_vocab_size
         __A       =						layer_norm_eps

         # Reader config
         __A       =						span_hidden_size
         __A       =						max_span_width
         __A       =						reader_layer_norm_eps
         __A       =						reader_beam_size
         __A       =						reader_seq_len

         # Retrieval config
         __A       =						num_block_records
         __A       =						searcher_beam_size




