


'''simple docstring'''
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__SCREAMING_SNAKE_CASE					:				Dict									=		False
__SCREAMING_SNAKE_CASE					:				str									=		True
__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		False


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				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.''')

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		parser.parse_args()

							__SCREAMING_SNAKE_CASE					:				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""",
							}

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

							__SCREAMING_SNAKE_CASE					:				Tuple									=		"""""" 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:
														__SCREAMING_SNAKE_CASE					:				str									=		reader.read()
														__SCREAMING_SNAKE_CASE					:				Union[str, 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'''):
														__SCREAMING_SNAKE_CASE					:				Optional[int]									=		UNetaDModel(**config)
							else:
														__SCREAMING_SNAKE_CASE					:				Dict									=		UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
														__SCREAMING_SNAKE_CASE					:				Tuple									=		class_name(**config)

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

							__SCREAMING_SNAKE_CASE					:				Dict									=		dict(model.config)

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

														__SCREAMING_SNAKE_CASE					:				int									=		[k.replace('''UNetRes''', '''''') for k in config["""down_block_types"""]]
														__SCREAMING_SNAKE_CASE					:				str									=		[k.replace('''UNetRes''', '''''') for k in config["""up_block_types"""]]

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

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

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












"""simple docstring"""


import os

def      UpperCamelCase							():
						UpperCamelCase				:		Union[str, Any]				   =			os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE     )							,     """num.txt"""     )
						with open(SCREAMING_SNAKE_CASE     ) as file_hand:
												return str(sum(int(SCREAMING_SNAKE_CASE     ) for line in file_hand     )     )[:10]


if __name__ == "__main__":
		print(solution())



