






'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Optional[int]						):
						lowerCamelCase__         =  s.rsplit(__lowerCAmelCase   ,    __lowerCAmelCase						)
						return new.join(__lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Optional[Any]						):
						# encoder.embeddings are double copied in original FLAVA
						return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()						)
def 						A__			(				__lowerCAmelCase		:			Dict						):
						lowerCamelCase__         =  {}

						lowerCamelCase__         =  ["""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:
																								lowerCamelCase__         =  key.replace(F'''{group_key}.'''   ,    F'''{group_key}.group.'''						)

												if "res_path" in key:
																		lowerCamelCase__         =  key.replace("""res_path."""   ,    """res_path.path."""						)

												if key.endswith(""".w"""						):
																		lowerCamelCase__         =  rreplace(__lowerCAmelCase   ,    """.w"""   ,    """.weight"""   ,    1						)
												if key.endswith(""".b"""						):
																		lowerCamelCase__         =  rreplace(__lowerCAmelCase   ,    """.b"""   ,    """.bias"""   ,    1						)

												lowerCamelCase__         =  value.float()

						return upgrade
@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]=None   ,    __lowerCAmelCase		:			Optional[Any]=True						):
						from dall_e import Encoder

						lowerCamelCase__         =  Encoder()
						if os.path.exists(__lowerCAmelCase						):
												lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						else:
												lowerCamelCase__         =  torch.hub.load_state_dict_from_url(__lowerCAmelCase						)

						if isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												lowerCamelCase__         =  ckpt.state_dict()
						encoder.load_state_dict(__lowerCAmelCase						)

						if config_path is not None:
												lowerCamelCase__         =  FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase						)
						else:
												lowerCamelCase__         =  FlavaImageCodebookConfig()

						lowerCamelCase__         =  FlavaImageCodebook(__lowerCAmelCase						).eval()
						lowerCamelCase__         =  encoder.state_dict()

						lowerCamelCase__         =  upgrade_state_dict(__lowerCAmelCase						)
						hf_model.load_state_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  hf_model.state_dict()
						lowerCamelCase__         =  count_parameters(__lowerCAmelCase						)
						lowerCamelCase__         =  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__":
							UpperCamelCase    :      Optional[int]  			=		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')
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

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









'''simple docstring'''







from __future__ import annotations

import math
def 						A__			(				__lowerCAmelCase		:			int						):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,    int(math.sqrt(__lowerCAmelCase						) + 1						)   ,    6						):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


UpperCamelCase    :      str  			=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise ValueError("""n must be an integer"""						)
						if n <= 0:
												raise ValueError("""n must be >= 0"""						)

						lowerCamelCase__         =  []
						for num in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  0
												while 2 * i * i <= odd_composites[num]:
																		lowerCamelCase__         =  odd_composites[num] - 2 * i * i
																		if is_prime(__lowerCAmelCase						):
																								break
																		i += 1
												else:
																		list_nums.append(odd_composites[num]						)
																		if len(__lowerCAmelCase						) == n:
																								return list_nums

						return []
def 						A__			(				):
						return compute_nums(1						)[0]


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

