



'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Tuple     =					s.rsplit(lowerCAmelCase__						,				lowerCAmelCase__   )
			return new.join(lowerCAmelCase__   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					{}

			A_     :						int     =					["""group_1""", """group_2""", """group_3""", """group_4"""]
			for key, value in state_dict.items():
						for group_key in group_keys:
									if group_key in key:
												A_     :						int     =					key.replace(f'{group_key}.'						,				f'{group_key}.group.'   )

						if "res_path" in key:
									A_     :						Optional[int]     =					key.replace("""res_path."""						,				"""res_path.path."""   )

						if key.endswith(""".w"""   ):
									A_     :						Union[str, Any]     =					rreplace(lowerCAmelCase__						,				""".w"""						,				""".weight"""						,				1   )
						if key.endswith(""".b"""   ):
									A_     :						Any     =					rreplace(lowerCAmelCase__						,				""".b"""						,				""".bias"""						,				1   )

						A_     :						Dict     =					value.float()

			return upgrade



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				lowerCamelCase__=True   ):
			'''simple docstring'''





			from dall_e import Encoder

			A_     :						List[Any]     =					Encoder()
			if os.path.exists(lowerCAmelCase__   ):
						A_     :						int     =					torch.load(lowerCAmelCase__   )
			else:
						A_     :						int     =					torch.hub.load_state_dict_from_url(lowerCAmelCase__   )

			if isinstance(lowerCAmelCase__						,				lowerCAmelCase__   ):
						A_     :						Dict     =					ckpt.state_dict()
			encoder.load_state_dict(lowerCAmelCase__   )

			if config_path is not None:
						A_     :						Optional[Any]     =					FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase__   )
			else:
						A_     :						Optional[int]     =					FlavaImageCodebookConfig()

			A_     :						Any     =					FlavaImageCodebook(lowerCAmelCase__   ).eval()
			A_     :						Optional[Any]     =					encoder.state_dict()

			A_     :						Optional[int]     =					upgrade_state_dict(lowerCAmelCase__   )
			hf_model.load_state_dict(lowerCAmelCase__   )
			A_     :						Optional[int]     =					hf_model.state_dict()
			A_     :						str     =					count_parameters(lowerCAmelCase__   )
			A_     :						Union[str, Any]     =					count_parameters(lowerCAmelCase__   )

			assert torch.allclose(lowerCAmelCase__						,				lowerCAmelCase__						,				atol=1E-3   )

			if save_checkpoint:
						hf_model.save_pretrained(lowerCAmelCase__   )
			else:
						return hf_state_dict


if __name__ == "__main__":
			lowerCamelCase							:Tuple       = argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			lowerCamelCase							:Any       = parser.parse_args()

			convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




'''simple docstring'''




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase							:List[str]       = 1_0_0

lowerCamelCase							:Dict       = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase							:int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
						if prime not in primes:
												continue
						primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00   )
def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			A_     :						set[int]     =					set()
			A_     :						int
			A_     :						int

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime   ):
									ret.add(sub * prime   )

			return ret



def      a      (			lowerCamelCase__ = 50_00   ):
			'''simple docstring'''





			for number_to_partition in range(1						,				lowerCamelCase__   ):
						if len(partition(lowerCamelCase__   )   ) > number_unique_partitions:
									return number_to_partition
			return None


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