
import math


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ = 0 ,		UpperCamelCase__ = 0		):
      UpperCamelCase__    :   List[Any]					=				end or len(UpperCamelCase__		)
      for i in range(UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[str]					=				i
            UpperCamelCase__    :   Any					=				array[i]
            while temp_index != start and temp_index_value < array[temp_index - 1]:
                  UpperCamelCase__    :   Union[str, Any]					=				array[temp_index - 1]
                  temp_index -= 1
            UpperCamelCase__    :   Tuple					=				temp_index_value
      return array


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):  # Max Heap
      UpperCamelCase__    :   Optional[Any]					=				index
      UpperCamelCase__    :   int					=				2 * index + 1  # Left Node
      UpperCamelCase__    :   List[Any]					=				2 * index + 2  # Right Node

      if left_index < heap_size and array[largest] < array[left_index]:
            UpperCamelCase__    :   int					=				left_index

      if right_index < heap_size and array[largest] < array[right_index]:
            UpperCamelCase__    :   Tuple					=				right_index

      if largest != index:
            UpperCamelCase__				,UpperCamelCase__    :   Any					=				array[largest], array[index]
            heapify(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				len(UpperCamelCase__		)

      for i in range(n // 2 ,		-1 ,		-1		):
            heapify(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      for i in range(n - 1 ,		0 ,		-1		):
            UpperCamelCase__				,UpperCamelCase__    :   Any					=				array[0], array[i]
            heapify(UpperCamelCase__ ,		0 ,		UpperCamelCase__		)

      return array


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      if (array[first_index] > array[middle_index]) != (
          array[first_index] > array[last_index]
      ):
            return array[first_index]
      elif (array[middle_index] > array[first_index]) != (
          array[middle_index] > array[last_index]
      ):
            return array[middle_index]
      else:
            return array[last_index]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				low
      UpperCamelCase__    :   int					=				high
      while True:
            while array[i] < pivot:
                  i += 1
            j -= 1
            while pivot < array[j]:
                  j -= 1
            if i >= j:
                  return i
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				array[j], array[i]
            i += 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if len(UpperCamelCase__		) == 0:
            return array
      UpperCamelCase__    :   Tuple					=				2 * math.ceil(math.loga(len(UpperCamelCase__		)		)		)
      UpperCamelCase__    :   int					=				1_6
      return intro_sort(UpperCamelCase__ ,		0 ,		len(UpperCamelCase__		) ,		UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      while end - start > size_threshold:
            if max_depth == 0:
                  return heap_sort(UpperCamelCase__		)
            max_depth -= 1
            UpperCamelCase__    :   Tuple					=				median_of_a(UpperCamelCase__ ,		UpperCamelCase__ ,		start + ((end - start) // 2) + 1 ,		end - 1		)
            UpperCamelCase__    :   List[str]					=				partition(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
            intro_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
            UpperCamelCase__    :   List[Any]					=				p
      return insertion_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCamelCase      =input("Enter numbers separated by a comma : ").strip()
    lowerCamelCase      =[float(item) for item in user_input.split(",")]
    print(sort(unsorted))




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))


