
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase    :   int		       =				False
lowercase    :   Optional[Any]		       =				True
lowercase    :   List[Any]		       =				False


if __name__ == "__main__":
  lowercase    :   Dict		       =				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.""")

  lowercase    :   List[Any]		       =				parser.parse_args()

  lowercase    :   Optional[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""",
  }

  lowercase    :   str		       =				{
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

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

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

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

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

    lowercase    :   Tuple		       =				[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    lowercase    :   Optional[int]		       =				[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

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

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





'''simple docstring'''





import string

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Union[str, Any]       			=					''
  for i in sequence:
    __lowerCamelCase			:			Tuple       			=					ord(_lowerCAmelCase			)
    if 65 <= extract <= 90:
      output += chr(155 - extract			)
    elif 97 <= extract <= 122:
      output += chr(219 - extract			)
    else:
      output += i
  return output

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					string.ascii_letters
  __lowerCamelCase			:			str       			=					string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(_lowerCAmelCase			)] if c in letters else c for c in sequence			)

def 			a_	(				) -> None:
  from timeit import timeit

  print('Running performance benchmarks...'			)
  __lowerCamelCase			:			Tuple       			=					'from string import printable ; from __main__ import atbash, atbash_slow'
  print(F'> atbash_slow(): {timeit("atbash_slow(printable)"			,setup=_lowerCAmelCase			)} seconds'			)
  print(F'>      atbash(): {timeit("atbash(printable)"			,setup=_lowerCAmelCase			)} seconds'			)


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f'''{example} encrypted in atbash: {atbash(example)}''')
   benchmark()






