"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor






class        __UpperCAmelCase						(     unittest.TestCase     ):

				def 		_a     (				self     ,			_lowerCamelCase = False				):
										lowerCamelCase__ =10

										if only_cross_attention:
																lowerCamelCase__ =12
										else:
																# when only cross attention is not set, the cross attention dim must be the same as the query dim
																lowerCamelCase__ =query_dim

										return {
										    "query_dim": query_dim,
										    "cross_attention_dim": cross_attention_dim,
										    "heads": 2,
										    "dim_head": 4,
										    "added_kv_proj_dim": 6,
										    "norm_num_groups": 1,
										    "only_cross_attention": only_cross_attention,
										    "processor": AttnAddedKVProcessor(),
										}

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =2

										lowerCamelCase__ =torch.rand(_lowerCamelCase     ,			_lowerCamelCase     ,			3     ,			2				)
										lowerCamelCase__ =torch.rand(_lowerCamelCase     ,			4     ,			_lowerCamelCase				)
										lowerCamelCase__ =None

										return {
										    "hidden_states": hidden_states,
										    "encoder_hidden_states": encoder_hidden_states,
										    "attention_mask": attention_mask,
										}






				def 		_a     (				self				):
										# self and cross attention

										torch.manual_seed(0				)

										lowerCamelCase__ =self.get_constructor_arguments(only_cross_attention=_lowerCamelCase				)
										lowerCamelCase__ =Attention(**_lowerCamelCase				)

										self.assertTrue(attn.to_k is not None				)
										self.assertTrue(attn.to_v is not None				)

										lowerCamelCase__ =self.get_forward_arguments(
										    query_dim=constructor_args["query_dim"]     ,			added_kv_proj_dim=constructor_args["added_kv_proj_dim"]				)

										lowerCamelCase__ =attn(**_lowerCamelCase				)

										# only self attention

										torch.manual_seed(0				)

										lowerCamelCase__ =self.get_constructor_arguments(only_cross_attention=_lowerCamelCase				)
										lowerCamelCase__ =Attention(**_lowerCamelCase				)

										self.assertTrue(attn.to_k is None				)
										self.assertTrue(attn.to_v is None				)

										lowerCamelCase__ =self.get_forward_arguments(
										    query_dim=constructor_args["query_dim"]     ,			added_kv_proj_dim=constructor_args["added_kv_proj_dim"]				)

										lowerCamelCase__ =attn(**_lowerCamelCase				)

										self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()				)






class        __UpperCAmelCase						(     unittest.TestCase     ):

				def 		_a     (				self				):
										lowerCamelCase__ =DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"     ,			safety_checker=_lowerCamelCase				)

										lowerCamelCase__ =pipe(
										    "foo"     ,			num_inference_steps=2     ,			generator=torch.Generator("cpu"				).manual_seed(0				)     ,			output_type="np"     ,			).images

										# the initial conversion succeeds
										lowerCamelCase__ =DiffusionPipeline.from_pretrained(
										    "hf-internal-testing/tiny-stable-diffusion-pipe"     ,			device_map="sequential"     ,			safety_checker=_lowerCamelCase				)

										lowerCamelCase__ =pipe(
										    "foo"     ,			num_inference_steps=2     ,			generator=torch.Generator("cpu"				).manual_seed(0				)     ,			output_type="np"     ,			).images

										with tempfile.TemporaryDirectory() as tmpdir:
																# save the converted model
																pipe.save_pretrained(_lowerCamelCase				)

																# can also load the converted weights
																lowerCamelCase__ =DiffusionPipeline.from_pretrained(_lowerCamelCase     ,			device_map="sequential"     ,			safety_checker=_lowerCamelCase				)

										lowerCamelCase__ =pipe(
										    "foo"     ,			num_inference_steps=2     ,			generator=torch.Generator("cpu"				).manual_seed(0				)     ,			output_type="np"     ,			).images

										self.assertTrue(np.allclose(_lowerCamelCase     ,			_lowerCamelCase     ,			atol=1E-5				)				)
										self.assertTrue(np.allclose(_lowerCamelCase     ,			_lowerCamelCase     ,			atol=1E-5				)				)



"""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
	)


