





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase :			Any     		=    logging.get_logger(__name__)



def A_      (						A__							)    ->						int:
       a__		:      Union[str, Any]					=							torch.load(lowercase_						, map_location='cpu'							)
       if "model" in sd.keys():
              a__		:      List[str]					=							torch.load(lowercase_						, map_location='cpu'							)["model"]

       # pop unnecessary weights
       a__		:      Optional[int]					=							[
           "decoder.version",
           "decoder.output_projection.weight",
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(lowercase_							)

       a__		:      Any					=							{
           "decoder.project_in_dim.weight": "decoder.project_in.weight",
           "decoder.project_out_dim.weight": "decoder.project_out.weight",
           "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
           "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__		:      List[Any]					=							sd.pop(lowercase_							)

       a__		:      int					=							list(sd.keys()							)
       for key in keys:
              if ".qkv_proj." in key:
                     a__		:      Dict					=							sd[key]
                     # We split QKV in separate Q,K,V

                     a__		:      Tuple					=							key.replace('.qkv_proj.'						, '.q_proj.'							)
                     a__		:      Dict					=							key.replace('.qkv_proj.'						, '.k_proj.'							)
                     a__		:      Tuple					=							key.replace('.qkv_proj.'						, '.v_proj.'							)

                     a__		:      List[Any]					=							value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__		:      Union[str, Any]					=							torch.split(lowercase_						, depth // 3						, dim=0							)

                     a__		:      Optional[int]					=							q
                     a__		:      Tuple					=							k
                     a__		:      Optional[int]					=							v
                     del sd[key]

       return sd



@torch.no_grad()
def A_      (						A__						, A__						, A__=None							)    ->						int:
       a__		:      Any					=							load_checkpoint(lowercase_							)

       if config is not None:
              a__		:      str					=							OPTConfig.from_pretrained(lowercase_							)
       else:
              a__		:      Tuple					=							OPTConfig()

       a__		:      Any					=							OPTModel(lowercase_							).half().eval()
       model.load_state_dict(lowercase_							)

       # Check results
       Path(lowercase_							).mkdir(exist_ok=lowercase_							)
       model.save_pretrained(lowercase_							)


if __name__ == "__main__":
    lowercase :			Optional[Any]     		=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase :			Optional[int]     		=    parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









"""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))
