



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


a_    :  Any						  =		False
a_    :  List[Any]						  =		True
a_    :  Tuple						  =		False


if __name__ == "__main__":
     a_    :  Optional[int]						  =		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.')

     a_    :  Optional[int]						  =		parser.parse_args()

     a_    :  Dict						  =		{
         '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',
     }

     a_    :  int						  =		{
         'time_steps': 'time_proj',
         'mid': 'mid_block',
         'downsample_blocks': 'down_blocks',
         'upsample_blocks': 'up_blocks',
     }

     a_    :  Optional[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:
          a_    :  Dict						  =		reader.read()
          a_    :  Optional[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'):
          a_    :  Dict						  =		UNetaDModel(**config)
     else:
          a_    :  str						  =		UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
          a_    :  List[str]						  =		class_name(**config)

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

     a_    :  int						  =		dict(model.config)

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

          a_    :  List[str]						  =		[k.replace('UNetRes', '') for k in config['down_block_types']]
          a_    :  Optional[int]						  =		[k.replace('UNetRes', '') for k in config['up_block_types']]

     if do_only_weights:
          a_    :  int						  =		torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

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

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




a_    :  Dict						  =		8.314462  # Unit - J mol-1 K-1

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


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

     testmod()