"""simple docstring"""


import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput






@dataclass
class        __UpperCAmelCase						(     snake_case__     ):
				A__				:       Union[List[PIL.Image.Image], np.ndarray]






class        __UpperCAmelCase						(     unittest.TestCase     ):

				def 		_a     (				self				):
										lowerCamelCase__ =CustomOutput(images=np.random.rand(1     ,			3     ,			4     ,			4				)				)

										# check every way of getting the attribute
										assert isinstance(outputs.images     ,			np.ndarray				)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs["images"]     ,			np.ndarray				)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0]     ,			np.ndarray				)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										lowerCamelCase__ =CustomOutput(images=[PIL.Image.new("RGB"     ,			(4, 4)				)]				)

										# check every way of getting the attribute
										assert isinstance(outputs.images     ,			_lowerCamelCase				)
										assert isinstance(outputs.images[0]     ,			PIL.Image.Image				)
										assert isinstance(outputs["images"]     ,			_lowerCamelCase				)
										assert isinstance(outputs["images"][0]     ,			PIL.Image.Image				)
										assert isinstance(outputs[0]     ,			_lowerCamelCase				)
										assert isinstance(outputs[0][0]     ,			PIL.Image.Image				)






				def 		_a     (				self				):
										# test output reinitialization with a `dict` for compatibility with `accelerate`
										lowerCamelCase__ =CustomOutput({"images": np.random.rand(1     ,			3     ,			4     ,			4				)}				)

										# check every way of getting the attribute
										assert isinstance(outputs.images     ,			np.ndarray				)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs["images"]     ,			np.ndarray				)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0]     ,			np.ndarray				)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										lowerCamelCase__ =CustomOutput({"images": [PIL.Image.new("RGB"     ,			(4, 4)				)]}				)

										# check every way of getting the attribute
										assert isinstance(outputs.images     ,			_lowerCamelCase				)
										assert isinstance(outputs.images[0]     ,			PIL.Image.Image				)
										assert isinstance(outputs["images"]     ,			_lowerCamelCase				)
										assert isinstance(outputs["images"][0]     ,			PIL.Image.Image				)
										assert isinstance(outputs[0]     ,			_lowerCamelCase				)
										assert isinstance(outputs[0][0]     ,			PIL.Image.Image				)


"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class        __UpperCAmelCase						(     utils.Parser     ):
				A__				:       str  							=	"halfcheetah-medium-expert-v2"
				A__				:       str  							=	"config.offline"





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()    )
						lowerCamelCase__ =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__":
	a							=Parser().parse_args('plan')
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)


