"""simple docstring"""




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def SCREAMING_SNAKE_CASE			(       lowercase_:		str						,  lowercase_:		Tuple						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    str					  =					pt_weights[F"{prefix}.in_proj_weight"].chunk(3						,  dim=0			)
       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					pt_weights[F"{prefix}.in_proj_bias"].chunk(3						,  dim=0			)

       _UpperCamelCase       :    int					  =					pt_weights[F"{prefix}.out_proj.weight"]
       _UpperCamelCase       :    Any					  =					pt_weights[F"{prefix}.out_proj.bias"]

       _UpperCamelCase       :    Optional[Any]					  =					q_proj
       _UpperCamelCase       :    List[Any]					  =					q_proj_bias

       _UpperCamelCase       :    Tuple					  =					k_proj
       _UpperCamelCase       :    Optional[Any]					  =					k_proj_bias

       _UpperCamelCase       :    Optional[Any]					  =					v_proj
       _UpperCamelCase       :    Union[str, Any]					  =					v_proj_bias

       _UpperCamelCase       :    List[Any]					  =					out_proj_weights
       _UpperCamelCase       :    int					  =					out_proj_bias


def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		Optional[Any]						,  lowercase_:		List[str]			):
       copy_linear(hf_mlp.fca						,  snake_case__						,  F"{prefix}.c_fc"			)
       copy_linear(hf_mlp.fca						,  snake_case__						,  F"{prefix}.c_proj"			)


def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		List[str]						,  lowercase_:		Any			):
       _UpperCamelCase       :    str					  =					pt_weights[F"{prefix}.weight"].data
       _UpperCamelCase       :    Any					  =					pt_weights[F"{prefix}.bias"].data


def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		Optional[Any]						,  lowercase_:		Optional[Any]			):
       # copy layer norms
       copy_linear(hf_layer.layer_norma						,  snake_case__						,  F"{prefix}.ln_1"			)
       copy_linear(hf_layer.layer_norma						,  snake_case__						,  F"{prefix}.ln_2"			)

       # copy MLP
       copy_mlp(hf_layer.mlp						,  snake_case__						,  F"{prefix}.mlp"			)

       # copy attn
       copy_attn_layer(hf_layer.self_attn						,  snake_case__						,  F"{prefix}.attn"			)


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Optional[int]			):
       for layer_id, hf_layer in enumerate(snake_case__			):
              copy_layer(snake_case__						,  snake_case__						,  F"{prefix}.{layer_id}"			)


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		List[str]			):
       # copy projection
       _UpperCamelCase       :    int					  =					pt_weights['text_projection'].data.T

       # copy text encoder
       for name, param in hf_model.text_model.named_parameters():
              _UpperCamelCase       :    Optional[Any]					  =					pt_weights[F"bert.{name}"].data


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		Optional[Any]			):
       # copy projection
       _UpperCamelCase       :    int					  =					pt_weights['visual.proj'].data.T

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

       # copy embeddings
       _UpperCamelCase       :    int					  =					pt_weights['visual.conv1.weight'].data
       _UpperCamelCase       :    Union[str, Any]					  =					pt_weights['visual.class_embedding'].data
       _UpperCamelCase       :    Any					  =					pt_weights['visual.positional_embedding'].data

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


@torch.no_grad()
def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]=None			):
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       _UpperCamelCase       :    Tuple					  =					ChineseCLIPConfig.from_pretrained(snake_case__			)

       _UpperCamelCase       :    Any					  =					ChineseCLIPModel(snake_case__			).eval()

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

       copy_text_model_and_projection(snake_case__						,  snake_case__			)
       copy_vision_model_and_projection(snake_case__						,  snake_case__			)
       _UpperCamelCase       :    Tuple					  =					pt_weights['logit_scale'].data

       hf_model.save_pretrained(snake_case__			)


if __name__ == "__main__":
 UpperCAmelCase     						=			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     						=			parser.parse_args()

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




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



