



"""simple docstring"""
import argparse
import json

from tqdm import tqdm





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Tuple											=    argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        '''--src_path'''     ,       type=UpperCamelCase__     ,       default='''biencoder-nq-dev.json'''     ,       help='''Path to raw DPR training data'''     ,       )
    parser.add_argument(
        '''--evaluation_set'''     ,       type=UpperCamelCase__     ,       help='''where to store parsed evaluation_set file'''     ,       )
    parser.add_argument(
        '''--gold_data_path'''     ,       type=UpperCamelCase__     ,       help='''where to store parsed gold_data_path file'''     ,       )
    _UpperCAmelCase							:  List[str]											=    parser.parse_args()

    with open(args.src_path     ,       '''r'''							) as src_file, open(args.evaluation_set     ,       '''w'''							) as eval_file, open(
        args.gold_data_path     ,       '''w'''							) as gold_file:
        _UpperCAmelCase							:  List[str]											=    json.load(UpperCamelCase__							)
        for dpr_record in tqdm(UpperCamelCase__							):
            _UpperCAmelCase							:  List[str]											=    dpr_record['''question''']
            _UpperCAmelCase							:  str											=    [context['''title'''] for context in dpr_record['''positive_ctxs''']]
            eval_file.write(question + '''\n'''							)
            gold_file.write('''\t'''.join(UpperCamelCase__							) + '''\n'''							)


if __name__ == "__main__":
  main()







"""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)


