
"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCamelCase			:       Any									=				False
UpperCamelCase			:       Union[str, Any]									=				True
UpperCamelCase			:       int									=				False


if __name__ == "__main__":
      UpperCamelCase			:       int									=				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.")

      UpperCamelCase			:       str									=				parser.parse_args()

      UpperCamelCase			:       str									=				{
          "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",
      }

      UpperCamelCase			:       Union[str, Any]									=				{
          "time_steps": "time_proj",
          "mid": "mid_block",
          "downsample_blocks": "down_blocks",
          "upsample_blocks": "up_blocks",
      }

      UpperCamelCase			:       Dict									=				"" 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:
            UpperCamelCase			:       str									=				reader.read()
            UpperCamelCase			:       List[str]									=				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"):
            UpperCamelCase			:       int									=				UNetaDModel(**config)
      else:
            UpperCamelCase			:       Dict									=				UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
            UpperCamelCase			:       List[str]									=				class_name(**config)

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

      UpperCamelCase			:       List[Any]									=				dict(model.config)

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

            UpperCamelCase			:       int									=				[k.replace("UNetRes", "") for k in config["down_block_types"]]
            UpperCamelCase			:       str									=				[k.replace("UNetRes", "") for k in config["up_block_types"]]

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

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

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





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



