




'''simple docstring'''





import json
import sys


def      UpperCamelCase_(	snake_case							:       List[Any]							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							with open(snake_case							,				encoding="utf-8"     ) as f:
														snake_case_						=      json.load(snake_case     )

							snake_case_						=      ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

							for benchmark_name in sorted(snake_case     ):
														snake_case_						=      results[benchmark_name]

														snake_case_						=      benchmark_name.split("/"     )[-1]
														output_md.append(f'### Benchmark: {benchmark_file_name}'     )

														snake_case_						=      "| metric |"
														snake_case_						=      "|--------|"
														snake_case_						=      "| new / old (diff) |"
														for metric_name in sorted(snake_case     ):
																					snake_case_						=      benchmark_res[metric_name]
																					snake_case_						=      metric_vals["new"]
																					snake_case_						=      metric_vals.get("old"							,				snake_case     )
																					snake_case_						=      metric_vals.get("diff"							,				snake_case     )

																					snake_case_						=      f' {new_val:f}' if isinstance(snake_case							,				(int, float)     ) else "None"

																					if old_val is not None:
																												val_str += f' / {old_val:f}' if isinstance(snake_case							,				(int, float)     ) else "None"
																					if dif_val is not None:
																												val_str += f' ({dif_val:f})' if isinstance(snake_case							,				(int, float)     ) else "None"

																					title += " " + metric_name + " |"
																					lines += "---|"
																					value += val_str + " |"

														output_md += [title, lines, value, " "]

							output_md.append("</details>"     )

							with open(snake_case							,				"w"							,				encoding="utf-8"     ) as f:
														f.writelines("\n".join(snake_case     )     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Tuple      		= sys.argv[1]
		_SCREAMING_SNAKE_CASE							:   Optional[int]      		= sys.argv[2]

		format_json_to_md(input_json_file, output_md_file)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


