
from __future__ import annotations


class 		__magic_name__ :





		def __init__(				self     :						Dict    ,	lowerCamelCase__     :						int					):
							lowerCAmelCase		:       Dict							=  order

							# a_{0} ... a_{k}
							lowerCAmelCase		:       Any							=  [1.0] + [0.0] * order
							# b_{0} ... b_{k}
							lowerCAmelCase		:       List[Any]							=  [1.0] + [0.0] * order

							# x[n-1] ... x[n-k]
							lowerCAmelCase		:       Dict							=  [0.0] * self.order
							# y[n-1] ... y[n-k]
							lowerCAmelCase		:       Union[str, Any]							=  [0.0] * self.order





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						list[float]    ,	lowerCamelCase__     :						list[float]					):
							if len(lowerCAmelCase__					) < self.order:
												lowerCAmelCase		:       List[str]							=  [1.0, *a_coeffs]

							if len(lowerCAmelCase__					) != self.order + 1:
												lowerCAmelCase		:       List[Any]							=  (
												    f'''Expected a_coeffs to have {self.order + 1} elements '''
												    f'''for {self.order}-order filter, got {len(lowerCAmelCase__					)}'''
												)
												raise ValueError(lowerCAmelCase__					)

							if len(lowerCAmelCase__					) != self.order + 1:
												lowerCAmelCase		:       int							=  (
												    f'''Expected b_coeffs to have {self.order + 1} elements '''
												    f'''for {self.order}-order filter, got {len(lowerCAmelCase__					)}'''
												)
												raise ValueError(lowerCAmelCase__					)

							lowerCAmelCase		:       Optional[Any]							=  a_coeffs
							lowerCAmelCase		:       Optional[Any]							=  b_coeffs





		def    _A				(				self     :						Any    ,	lowerCamelCase__     :						float					):
							lowerCAmelCase		:       str							=  0.0

							# Start at index 1 and do index 0 at the end.
							for i in range(1    ,	self.order + 1					):
												result += (
												    self.b_coeffs[i] * self.input_history[i - 1]
												    - self.a_coeffs[i] * self.output_history[i - 1]
												)

							lowerCAmelCase		:       str							=  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

							lowerCAmelCase		:       Dict							=  self.input_history[:-1]
							lowerCAmelCase		:       str							=  self.output_history[:-1]

							lowerCAmelCase		:       Any							=  sample
							lowerCAmelCase		:       Optional[Any]							=  result

							return result


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


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

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							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",
						}

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

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

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

						_snake_case      :							Any     	=    dict(model.config)

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

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

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

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