



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
   parser.add_argument("--model_name", default="roberta-large", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   _lowercase						  =		parser.parse_args()

   if args.model_type == "roberta":
      _lowercase						  =		RobertaForMaskedLM.from_pretrained(args.model_name)
      _lowercase						  =		"roberta"
   elif args.model_type == "gpt2":
      _lowercase						  =		GPTaLMHeadModel.from_pretrained(args.model_name)
      _lowercase						  =		"transformer"

   _lowercase						  =		model.state_dict()
   _lowercase						  =		{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         _lowercase						  =		state_dict[F"{prefix}.{param_name}"]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         _lowercase						  =		F"{prefix}.embeddings.{w}.weight"
         _lowercase						  =		state_dict[param_name]
      for w in ["weight", "bias"]:
         _lowercase						  =		F"{prefix}.embeddings.LayerNorm.{w}"
         _lowercase						  =		state_dict[param_name]

    # Transformer Blocks #
   _lowercase						  =		0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               _lowercase						  =		state_dict[
                   F"{prefix}.h.{teacher_idx}.{layer}.{w}"
               ]
         _lowercase						  =		state_dict[F"{prefix}.h.{teacher_idx}.attn.bias"]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               _lowercase						  =		state_dict[
                   F"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         _lowercase						  =		state_dict[F"{layer}"]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            _lowercase						  =		state_dict[F"lm_head.dense.{w}"]
            _lowercase						  =		state_dict[F"lm_head.layer_norm.{w}"]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         _lowercase						  =		state_dict[F"{prefix}.ln_f.{w}"]
      _lowercase						  =		state_dict["lm_head.weight"]

   print(F"N layers selected for distillation: {std_idx}")
   print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

   print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
   torch.save(compressed_sd, args.dump_checkpoint)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument(
       "--original_config_file",
       type=str,
       required=True,
       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(
       "--image_size",
       default=512,
       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(
       "--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.)")



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       if string == "True":
           return True
       elif string == "False":
           return False
       else:
           raise ValueError(f"could not parse string as bool {string}"	)

   parser.add_argument(
       "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
   )

   parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		download_controlnet_from_original_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       extract_ema=args.extract_ema,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       use_linear_projection=args.use_linear_projection,
       cross_attention_dim=args.cross_attention_dim,
   )



   controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
