



from __future__ import annotations






def 					a(   A :					list[list[int]]				,  A :					int				,  A :					int				,  A :					int | float )	-> None:
					"""simple docstring"""





					a  ,       a        =     m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}''' )
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found''' )






def 					a(   )	-> None:
					"""simple docstring"""





					a        =     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					a        =     int(input("Enter the element to be searched:" ) )
					print(A )
					search_in_a_sorted_matrix(A				,  len(A )				,  len(mat[0] )				,  A )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





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!")
