
from __future__ import annotations

lowerCamelCase      =[-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
lowerCamelCase      =[-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   str					=				[]
      UpperCamelCase__    :   Optional[Any]					=				len(UpperCamelCase__		)

      for i in range(UpperCamelCase__		):
            UpperCamelCase__    :   float					=				-1
            for j in range(i + 1 ,		UpperCamelCase__		):
                  if arr[i] < arr[j]:
                        UpperCamelCase__    :   Optional[int]					=				arr[j]
                        break
            result.append(UpperCamelCase__		)
      return result


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Optional[Any]					=				[]
      for i, outer in enumerate(UpperCamelCase__		):
            UpperCamelCase__    :   float					=				-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        UpperCamelCase__    :   str					=				inner
                        break
            result.append(UpperCamelCase__		)
      return result


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   int					=				len(UpperCamelCase__		)
      UpperCamelCase__    :   list[float]					=				[]
      UpperCamelCase__    :   list[float]					=				[-1] * arr_size

      for index in reversed(range(UpperCamelCase__		)		):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  UpperCamelCase__    :   int					=				stack[-1]
            stack.append(arr[index]		)
      return result


if __name__ == "__main__":
    from doctest import testmod
    from timeit import timeit

    testmod()
    print(next_greatest_element_slow(arr))
    print(next_greatest_element_fast(arr))
    print(next_greatest_element(arr))

    lowerCamelCase      =(
        "from __main__ import arr, next_greatest_element_slow, "
        "next_greatest_element_fast, next_greatest_element"
    )
    print(
        "next_greatest_element_slow():",
        timeit("next_greatest_element_slow(arr)", setup=setup),
    )
    print(
        "next_greatest_element_fast():",
        timeit("next_greatest_element_fast(arr)", setup=setup),
    )
    print(
        "     next_greatest_element():",
        timeit("next_greatest_element(arr)", setup=setup),
    )




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


if __name__ == "__main__":
    lowerCamelCase      =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      =parser.parse_args()

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

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

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

    lowerCamelCase      =dict(model.config)

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

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

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

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

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


