




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__					:  Union[str, Any]      							=				False
lowerCamelCase__					:  List[str]      							=				True
lowerCamelCase__					:  Optional[Any]      							=				False


if __name__ == "__main__":
   lowerCamelCase__					:  Optional[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.')

   lowerCamelCase__					:  Optional[Any]      							=				parser.parse_args()

   lowerCamelCase__					:  List[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',
   }

   lowerCamelCase__					:  str      							=				{
       'time_steps': 'time_proj',
       'mid': 'mid_block',
       'downsample_blocks': 'down_blocks',
       'upsample_blocks': 'up_blocks',
   }

   lowerCamelCase__					:  str      							=				'' 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__					:  Optional[int]      							=				reader.read()
      lowerCamelCase__					:  Optional[int]      							=				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__					:  Any      							=				UNetaDModel(**config)
   else:
      lowerCamelCase__					:  Optional[Any]      							=				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
      lowerCamelCase__					:  Optional[Any]      							=				class_name(**config)

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

   lowerCamelCase__					:  Optional[Any]      							=				dict(model.config)

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

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

   if do_only_weights:
      lowerCamelCase__					:  Optional[Any]      							=				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

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

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


'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> str:
							if a < 0 or b < 0:
														raise ValueError('the value of both inputs must be positive'   )
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]  # remove the leading "0b"
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]
							SCREAMING_SNAKE_CASE_				=				max(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
							return "0b" + "".join(
							    str(int('1' in (char_a, char_b)   )   )
							    for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase__   )		,b_binary.zfill(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





