"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__     						=  False
UpperCAmelCase__     						=  True
UpperCAmelCase__     						=  False


if __name__ == "__main__":
    UpperCAmelCase__     						=  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__     						=  parser.parse_args()

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

    UpperCAmelCase__     						=  """""" 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__     						=  reader.read()
        UpperCAmelCase__     						=  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__     						=  UNetaDModel(**config)
    else:
        UpperCAmelCase__     						=  UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
        UpperCAmelCase__     						=  class_name(**config)

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

    UpperCAmelCase__     						=  dict(model.config)

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

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

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

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

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


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ):
 __a :  str						=  len(lowerCAmelCase__  )
 __a :  Optional[int]						=  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a :  str						=  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a :  Tuple						=  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
