





"""simple docstring"""


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      if "model" in orig_key:
            _UpperCamelCase   :						Any    =							orig_key.replace("model."		,""      )
      if "norm1" in orig_key:
            _UpperCamelCase   :						int    =							orig_key.replace("norm1"		,"attention.output.LayerNorm"      )
      if "norm2" in orig_key:
            _UpperCamelCase   :						Tuple    =							orig_key.replace("norm2"		,"output.LayerNorm"      )
      if "norm" in orig_key:
            _UpperCamelCase   :						List[str]    =							orig_key.replace("norm"		,"LayerNorm"      )
      if "transformer" in orig_key:
            _UpperCamelCase   :						Union[str, Any]    =							orig_key.split("."      )[0].split("_"      )[-1]
            _UpperCamelCase   :						List[Any]    =							orig_key.replace(F'''transformer_{layer_num}'''		,F'''encoder.layer.{layer_num}'''      )
      if "mha.attn" in orig_key:
            _UpperCamelCase   :						Optional[Any]    =							orig_key.replace("mha.attn"		,"attention.self"      )
      if "mha" in orig_key:
            _UpperCamelCase   :						Optional[Any]    =							orig_key.replace("mha"		,"attention"      )
      if "W_q" in orig_key:
            _UpperCamelCase   :						int    =							orig_key.replace("W_q"		,"self.query"      )
      if "W_k" in orig_key:
            _UpperCamelCase   :						str    =							orig_key.replace("W_k"		,"self.key"      )
      if "W_v" in orig_key:
            _UpperCamelCase   :						List[Any]    =							orig_key.replace("W_v"		,"self.value"      )
      if "ff1" in orig_key:
            _UpperCamelCase   :						Any    =							orig_key.replace("ff1"		,"intermediate.dense"      )
      if "ff2" in orig_key:
            _UpperCamelCase   :						Optional[Any]    =							orig_key.replace("ff2"		,"output.dense"      )
      if "ff" in orig_key:
            _UpperCamelCase   :						Any    =							orig_key.replace("ff"		,"output.dense"      )
      if "mlm_class" in orig_key:
            _UpperCamelCase   :						Tuple    =							orig_key.replace("mlm.mlm_class"		,"cls.predictions.decoder"      )
      if "mlm" in orig_key:
            _UpperCamelCase   :						Optional[int]    =							orig_key.replace("mlm"		,"cls.predictions.transform"      )
      if "cls" not in orig_key:
            _UpperCamelCase   :						Optional[Any]    =							"yoso." + orig_key

      return orig_key




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Tuple:




      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            _UpperCamelCase   :						Optional[int]    =							orig_state_dict.pop(lowercase_      )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  _UpperCamelCase   :						Tuple    =							val

      _UpperCamelCase   :						List[str]    =							orig_state_dict["cls.predictions.decoder.bias"]
      _UpperCamelCase   :						Any    =							torch.arange(lowercase_      ).expand((1, -1)      ) + 2

      return orig_state_dict




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							torch.load(lowercase_		,map_location="cpu"      )["model_state_dict"]
      _UpperCamelCase   :						Union[str, Any]    =							YosoConfig.from_json_file(lowercase_      )
      _UpperCamelCase   :						Any    =							YosoForMaskedLM(lowercase_      )

      _UpperCamelCase   :						Optional[Any]    =							convert_checkpoint_helper(config.max_position_embeddings		,lowercase_      )

      print(model.load_state_dict(lowercase_      )      )
      model.eval()
      model.save_pretrained(lowercase_      )

      print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''      )


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for YOSO model config.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__						= parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__						= False
lowerCamelCase__						= True
lowerCamelCase__						= False


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

  parser.add_argument(
      "--repo_path",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  lowerCamelCase__						= parser.parse_args()

  lowerCamelCase__						= {
      "image_size": "sample_size",
      "num_res_blocks": "layers_per_block",
      "block_channels": "block_out_channels",
      "down_blocks": "down_block_types",
      "up_blocks": "up_block_types",
      "downscale_freq_shift": "freq_shift",
      "resnet_num_groups": "norm_num_groups",
      "resnet_act_fn": "act_fn",
      "resnet_eps": "norm_eps",
      "num_head_channels": "attention_head_dim",
  }

  lowerCamelCase__						= {
      "time_steps": "time_proj",
      "mid": "mid_block",
      "downsample_blocks": "down_blocks",
      "upsample_blocks": "up_blocks",
  }

  lowerCamelCase__						= "" if has_file(args.repo_path, "config.json") else "unet"

  with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
    lowerCamelCase__						= reader.read()
    lowerCamelCase__						= json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, "config.json"):
    lowerCamelCase__						= UNetaDModel(**config)
  else:
    lowerCamelCase__						= UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
    lowerCamelCase__						= class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowerCamelCase__						= dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowerCamelCase__						= config[key]
        del config[key]

    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["down_block_types"]]
    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["up_block_types"]]

  if do_only_weights:
    lowerCamelCase__						= torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

    lowerCamelCase__						= {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
        continue
      lowerCamelCase__						= False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(".")[0] == key:
          lowerCamelCase__						= param_value
          lowerCamelCase__						= True
      if not has_changed:
        lowerCamelCase__						= param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))
