


'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__A :  Union[str, Any]    							=				False
__A :  Union[str, Any]    							=				True
__A :  List[str]    							=				False


if __name__ == "__main__":
  __A :  Tuple    							=				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.")

  __A :  Any    							=				parser.parse_args()

  __A :  Any    							=				{
      "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",
  }

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

  __A :  Optional[Any]    							=				"" 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:
    __A :  Tuple    							=				reader.read()
    __A :  List[Any]    							=				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"):
    __A :  Union[str, Any]    							=				UNetaDModel(**config)
  else:
    __A :  Any    							=				UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
    __A :  str    							=				class_name(**config)

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

  __A :  str    							=				dict(model.config)

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

    __A :  List[str]    							=				[k.replace("UNetRes", "") for k in config["down_block_types"]]
    __A :  Union[str, Any]    							=				[k.replace("UNetRes", "") for k in config["up_block_types"]]

  if do_only_weights:
    __A :  List[Any]    							=				torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

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







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



