





'''simple docstring'''

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowerCAmelCase							:     Union[str, Any]		     =False
__lowerCAmelCase							:     Any		     =True
__lowerCAmelCase							:     Optional[Any]		     =False


if __name__ == "__main__":
  __lowerCAmelCase							:     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.")

  __lowerCAmelCase							:     str		     =parser.parse_args()

  __lowerCAmelCase							:     List[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",
  }

  __lowerCAmelCase							:     List[str]		     ={
      "time_steps": "time_proj",
      "mid": "mid_block",
      "downsample_blocks": "down_blocks",
      "upsample_blocks": "up_blocks",
  }

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

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

  __lowerCAmelCase							:     Union[str, Any]		     =dict(model.config)

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

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

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

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

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










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


if __name__ == "__main__":
  print(f"""{solution() = }""")



