




from __future__ import annotations

import sys




def      __magic_name__							(					A						:   int  ):






			'''simple docstring'''







			if n < 0:
						raise ValueError("Negative arguments are not supported"  )
			return _fib(A  )[0]




def      __magic_name__							(					A						:   int  ):






			'''simple docstring'''







			if n == 0:  # (F(0), F(1))
						return (0, 1)

			# F(2n) = F(n)[2F(n+1) − F(n)]
			# F(2n+1) = F(n+1)^2+F(n)^2
			a       ,						a       =     _fib(n // 2  )
			a       =     a * (b * 2 - a)
			a       =     a * a + b * b
			return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__lowerCAmelCase						:							Optional[Any]			     =						int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')












import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def      __magic_name__							(					A						:   str,    A						:   List[str],    A						:   int  ):






			'''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      __magic_name__							(					A						:   Optional[int],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_fc"""  )
			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_proj"""  )




def      __magic_name__							(					A						:   int,    A						:   Tuple,    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     pt_weights[F"""{prefix}.weight"""].data
			a       =     pt_weights[F"""{prefix}.bias"""].data




def      __magic_name__							(					A						:   List[Any],    A						:   List[str],    A						:   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      __magic_name__							(					A						:   int,    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			for layer_id, hf_layer in enumerate(A  ):
						copy_layer(A,    A,    F"""{prefix}.{layer_id}"""  )




def      __magic_name__							(					A						:   Any,    A						:   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      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any]  ):






			'''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      __magic_name__							(					A						:   Optional[Any],    A						:   List[str],    A						:   Dict=None  ):






			'''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__":
		__lowerCAmelCase						:							Dict			     =						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.'
		)
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

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






