



from itertools import permutations






def 					a(   A :					int = 5 )	-> int:
					"""simple docstring"""





					if gon_side < 3 or gon_side > 5:
										raise ValueError("gon_side must be in the range [3, 5]" )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					a        =     list(range(gon_side + 1				,  0				,  -1 ) )
					a        =     list(range(gon_side + 2				,  gon_side * 2 + 1 ) )

					for perm in permutations(small_numbers + big_numbers ):
										a        =     generate_gon_ring(A				,  list(A ) )
										if is_magic_gon(A ):
															return int("".join(str(A ) for n in numbers ) )

					a        =     f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(A )






def 					a(   A :					int				,  A :					list[int] )	-> list[int]:
					"""simple docstring"""





					a        =     [0] * (gon_side * 3)
					a        =     perm[0:3]
					perm.append(perm[1] )

					a        =     1 if gon_side < 5 else 2

					for i in range(1				,  len(A ) // 3 + magic_number ):
										a        =     perm[2 * i + 1]
										a        =     result[3 * i - 1]
										a        =     perm[2 * i + 2]

					return result






def 					a(   A :					list[int] )	-> bool:
					"""simple docstring"""





					if len(A ) % 3 != 0:
										raise ValueError("a gon ring should have a length that is a multiple of 3" )

					if min(numbers[::3] ) != numbers[0]:
										return False

					a        =     sum(numbers[:3] )

					return all(sum(numbers[i : i + 3] ) == total for i in range(3				,  len(A )				,  3 ) )


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





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 					a(   A :					Union[str, Any]				,  A :					List[str]				,  A :					Any )	-> Tuple:
					"""simple docstring"""





					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3				,  dim=0 )
					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3				,  dim=0 )

					a        =     pt_weights[f'''{prefix}.out_proj.weight''']
					a        =     pt_weights[f'''{prefix}.out_proj.bias''']

					a        =     q_proj
					a        =     q_proj_bias

					a        =     k_proj
					a        =     k_proj_bias

					a        =     v_proj
					a        =     v_proj_bias

					a        =     out_proj_weights
					a        =     out_proj_bias






def 					a(   A :					List[str]				,  A :					List[Any]				,  A :					int )	-> List[Any]:
					"""simple docstring"""





					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_fc''' )
					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_proj''' )






def 					a(   A :					Optional[Any]				,  A :					List[Any]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					a        =     pt_weights[f'''{prefix}.weight'''].data
					a        =     pt_weights[f'''{prefix}.bias'''].data






def 					a(   A :					int				,  A :					Optional[Any]				,  A :					List[Any] )	-> Union[str, Any]:
					"""simple docstring"""





					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_1''' )
					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_2''' )

					# copy MLP
					copy_mlp(hf_layer.mlp				,  A				,  f'''{prefix}.mlp''' )

					# copy attn
					copy_attn_layer(hf_layer.self_attn				,  A				,  f'''{prefix}.attn''' )






def 					a(   A :					str				,  A :					Optional[int]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					for layer_id, hf_layer in enumerate(A ):
										copy_layer(A				,  A				,  f'''{prefix}.{layer_id}''' )






def 					a(   A :					Dict				,  A :					Union[str, Any] )	-> Optional[Any]:
					"""simple docstring"""





					a        =     pt_weights["text_projection"].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										a        =     pt_weights[f'''bert.{name}'''].data






def 					a(   A :					Optional[Any]				,  A :					Optional[Any] )	-> List[str]:
					"""simple docstring"""





					a        =     pt_weights["visual.proj"].data.T

					# copy layer norms
					copy_linear(hf_model.vision_model.pre_layrnorm				,  A				,  "visual.ln_pre" )
					copy_linear(hf_model.vision_model.post_layernorm				,  A				,  "visual.ln_post" )

					# copy embeddings
					a        =     pt_weights["visual.conv1.weight"].data
					a        =     pt_weights["visual.class_embedding"].data
					a        =     pt_weights["visual.positional_embedding"].data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers				,  A				,  "visual.transformer.resblocks" )






@torch.no_grad()
def 					a(   A :					str				,  A :					List[str]				,  A :					Union[str, Any]=None )	-> Tuple:
					"""simple docstring"""





					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					a        =     ChineseCLIPConfig.from_pretrained(A )

					a        =     ChineseCLIPModel(A ).eval()

					a        =     torch.load(A				,  map_location="cpu" )["state_dict"]
					a        =     {(name[7:] if name.startswith("module." ) else name): value for name, value in pt_weights.items()}

					copy_text_model_and_projection(A				,  A )
					copy_vision_model_and_projection(A				,  A )
					a        =     pt_weights["logit_scale"].data

					hf_model.save_pretrained(A )


if __name__ == "__main__":
				_lowercase:				List[Any]       =	argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    help="Path to the output folder storing converted hf PyTorch model.",
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
				)
				parser.add_argument(
				    "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
				)
				_lowercase:				int       =	parser.parse_args()

				convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
				print("The conversion is finished!")
