

def 					__lowercase      (     _A						,    _A			)     ->						Dict:

      '''simple docstring'''





      _enforce_args(__A						,    __A			)
      if n == 0:
            return 0
      SCREAMING_SNAKE_CASE :      Tuple       =   float("""-inf"""			)
      for i in range(1						,    n + 1			):
            SCREAMING_SNAKE_CASE :      Any       =   max(
                __A						,    prices[i - 1] + naive_cut_rod_recursive(n - i						,    __A			)			)

      return max_revue






def 					__lowercase      (     _A						,    _A			)     ->						Dict:

      '''simple docstring'''





      _enforce_args(__A						,    __A			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   [float("""-inf"""			) for _ in range(n + 1			)]
      return _top_down_cut_rod_recursive(__A						,    __A						,    __A			)






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Tuple:

      '''simple docstring'''





      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            SCREAMING_SNAKE_CASE :      int       =   float("""-inf"""			)
            for i in range(1						,    n + 1			):
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   max(
                      __A						,    prices[i - 1] + _top_down_cut_rod_recursive(n - i						,    __A						,    __A			)						,    )

            SCREAMING_SNAKE_CASE :      Optional[Any]       =   max_revenue

      return max_rev[n]






def 					__lowercase      (     _A						,    _A			)     ->						str:

      '''simple docstring'''





      _enforce_args(__A						,    __A			)

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      SCREAMING_SNAKE_CASE :      List[Any]       =   [float("""-inf"""			) for _ in range(n + 1			)]
      SCREAMING_SNAKE_CASE :      Any       =   0

      for i in range(1						,    n + 1			):
            SCREAMING_SNAKE_CASE :      Any       =   max_rev[i]
            for j in range(1						,    i + 1			):
                  SCREAMING_SNAKE_CASE :      List[Any]       =   max(__A						,    prices[j - 1] + max_rev[i - j]			)

            SCREAMING_SNAKE_CASE :      int       =   max_revenue_i

      return max_rev[n]






def 					__lowercase      (     _A						,    _A			)     ->						List[Any]:

      '''simple docstring'''





      if n < 0:
            SCREAMING_SNAKE_CASE :      Any       =   F"n must be greater than or equal to 0. Got n = {n}"
            raise ValueError(__A			)

      if n > len(__A			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   (
                """Each integral piece of rod must have a corresponding price. """
                F"Got n = {n} but length of prices = {len(__A			)}"
            )
            raise ValueError(__A			)






def 					__lowercase      (     )     ->						Union[str, Any]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      str       =   [6, 10, 12, 15, 20, 23]
      SCREAMING_SNAKE_CASE :      List[str]       =   len(__A			)

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      SCREAMING_SNAKE_CASE :      Tuple       =   36

      SCREAMING_SNAKE_CASE :      Any       =   top_down_cut_rod(__A						,    __A			)
      SCREAMING_SNAKE_CASE :      Dict       =   bottom_up_cut_rod(__A						,    __A			)
      SCREAMING_SNAKE_CASE :      List[str]       =   naive_cut_rod_recursive(__A						,    __A			)

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    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__ :     List[str]				    =    parser.parse_args()

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

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

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

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

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

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

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

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

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




