import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__     :   Optional[Any]		=   False
lowerCAmelCase__     :   Any		=   True
lowerCAmelCase__     :   Any		=   False


if __name__ == "__main__":
				lowerCAmelCase__     :   Tuple		=   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__     :   Tuple		=   parser.parse_args()

				lowerCAmelCase__     :   str		=   {
				    '''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__     :   Any		=   {
				    '''time_steps''': '''time_proj''',
				    '''mid''': '''mid_block''',
				    '''downsample_blocks''': '''down_blocks''',
				    '''upsample_blocks''': '''up_blocks''',
				}

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

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

				lowerCAmelCase__     :   List[Any]		=   dict(model.config)

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

								lowerCAmelCase__     :   Union[str, Any]		=   [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
								lowerCAmelCase__     :   Tuple		=   [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

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

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







'''simple docstring'''

import math



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bool:
		_a     :      Optional[int]        =							math.loga(math.sqrt(4 * positive_integer + 1  ) / 2 + 1 / 2  )

		return exponent == int(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_ = 1 / 12345  )			->      int:
		_a     :      int        =							0
		_a     :      Optional[Any]        =							0

		_a     :      int        =							3
		while True:
				_a     :      Tuple        =							(integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							int(lowerCAmelCase_  )
						total_partitions += 1
						if check_partition_perfect(lowerCAmelCase_  ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(lowerCAmelCase_  )
				integer += 1


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




