
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Tuple:
		'''simple docstring'''

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

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

		UpperCamelCase            =	q_proj
		UpperCamelCase            =	q_proj_bias

		UpperCamelCase            =	k_proj
		UpperCamelCase            =	k_proj_bias

		UpperCamelCase            =	v_proj
		UpperCamelCase            =	v_proj_bias

		UpperCamelCase            =	out_proj_weights
		UpperCamelCase            =	out_proj_bias


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Dict:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Dict:
		'''simple docstring'''

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

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

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


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    str:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	pt_weights['text_projection'].data.T

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


def  A			(   lowercase       ,			lowercase					)  ->    str:
		'''simple docstring'''

		UpperCamelCase            =	pt_weights['visual.proj'].data.T

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

		# copy embeddings
		UpperCamelCase            =	pt_weights['visual.conv1.weight'].data
		UpperCamelCase            =	pt_weights['visual.class_embedding'].data
		UpperCamelCase            =	pt_weights['visual.positional_embedding'].data

		# copy encoder
		copy_layers(hf_model.vision_model.encoder.layers       ,			lowercase       ,			'visual.transformer.resblocks'					)


@torch.no_grad()
def  A			(   lowercase       ,			lowercase       ,			lowercase=None					)  ->    str:
		'''simple docstring'''

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

		UpperCamelCase            =	ChineseCLIPModel(lowercase					).eval()

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

		copy_text_model_and_projection(lowercase       ,			lowercase					)
		copy_vision_model_and_projection(lowercase       ,			lowercase					)
		UpperCamelCase            =	pt_weights['logit_scale'].data

		hf_model.save_pretrained(lowercase					)


if __name__ == "__main__":
					_UpperCAmelCase   :					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."
					)
					_UpperCAmelCase   :					Dict    =			parser.parse_args()

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






class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	int				)   ->	None:
							a       =     data
							a       =     42
							a       =     42




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     0
			a       =     x




def      __magic_name__							(					A						:   Node,    A						:   Node  ):






			'''simple docstring'''







			a       ,						a       =     find_set(A  ), find_set(A  )
			if x == y:
						return

			elif x.rank > y.rank:
						a       =     x
			else:
						a       =     y
						if x.rank == y.rank:
									y.rank += 1




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			if x != x.parent:
						a       =     find_set(x.parent  )
			return x.parent




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     ({0, 1, 2}, {3, 4, 5})
			for s in sets:
						if node.data in s:
									return s
			a       =     F"""{node.data} is not in {sets}"""
			raise ValueError(A  )




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     [Node(A  ) for i in range(6  )]
			for v in vertex:
						make_set(A  )

			union_set(vertex[0],    vertex[1]  )
			union_set(vertex[1],    vertex[2]  )
			union_set(vertex[3],    vertex[4]  )
			union_set(vertex[3],    vertex[5]  )

			for nodea in vertex:
						for nodea in vertex:
									if find_python_set(A  ).isdisjoint(find_python_set(A  )  ):
												assert find_set(A  ) != find_set(A  )
									else:
												assert find_set(A  ) == find_set(A  )


if __name__ == "__main__":
		test_disjoint_set()






