






"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase    			=       False
lowercase    			=       True
lowercase    			=       False


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

			lowercase    			=       parser.parse_args()

			lowercase    			=       {
			    """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""",
			}

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

			lowercase    			=       """""" 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:
						lowercase    			=       reader.read()
						lowercase    			=       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'''):
						lowercase    			=       UNetaDModel(**config)
			else:
						lowercase    			=       UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
						lowercase    			=       class_name(**config)

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

			lowercase    			=       dict(model.config)

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

						lowercase    			=       [k.replace('''UNetRes''', '''''') for k in config["""down_block_types"""]]
						lowercase    			=       [k.replace('''UNetRes''', '''''') for k in config["""up_block_types"""]]

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

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

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











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
			print(f'''{solution() = }''')



