



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






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





					a        =     [
					    "encoder.version",
					    "decoder.version",
					    "model.encoder.version",
					    "model.decoder.version",
					    "_float_tensor",
					    "decoder.output_projection.weight",
					]
					for k in ignore_keys:
										state_dict.pop(UpperCamelCase__				,  UpperCamelCase__ )






def 					a(   A :					Dict )	-> str:
					"""simple docstring"""





					a        =     emb.weight.shape
					a        =     nn.Linear(UpperCamelCase__				,  UpperCamelCase__				,  bias=UpperCamelCase__ )
					a        =     emb.weight.data
					return lin_layer






def 					a(   A :					List[Any]				,  A :					List[str]="uclanlp/plbart-base"				,  A :					List[Any]=False				,  A :					Dict=False )	-> Optional[Any]:
					"""simple docstring"""





					a        =     torch.load(UpperCamelCase__				,  map_location="cpu" )["model"]
					remove_ignore_keys_(UpperCamelCase__ )
					a        =     state_dict["encoder.embed_tokens.weight"].shape[0]

					a        =     PLBartConfig.from_pretrained(UpperCamelCase__				,  vocab_size=UpperCamelCase__ )

					a        =     state_dict["decoder.embed_tokens.weight"]
					if not classification:
										a        =     PLBartForConditionalGeneration(UpperCamelCase__ )
										model.model.load_state_dict(UpperCamelCase__ )
										if finetuned:
															a        =     make_linear_from_emb(model.model.shared )

					else:
										a        =     {}
										for key, value in state_dict.copy().items():
															if key.startswith("classification_heads.sentence_classification_head" ):
																				a        =     value
																				state_dict.pop(UpperCamelCase__ )
										a        =     PLBartForSequenceClassification(UpperCamelCase__ )
										model.model.load_state_dict(UpperCamelCase__ )
										model.classification_head.load_state_dict(UpperCamelCase__ )

					return model


if __name__ == "__main__":
		_lowercase:				List[Any]       =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config",
		    default="uclanlp/plbart-base",
		    type=str,
		    help="Which huggingface architecture to use: plbart-base",
		)
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		parser.add_argument(
		    "--classification", action="store_true", help="whether the model is a classification checkpoint"
		)
		_lowercase:				str       =	parser.parse_args()
		_lowercase:				str       =	convert_fairseq_plbart_checkpoint_from_disk(
		    args.fairseq_path,
		    hf_config_path=args.hf_config,
		    finetuned=args.finetuned,
		    classification=args.classification,
		)
		model.save_pretrained(args.pytorch_dump_folder_path)





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!")
