






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    ):



							'''simple docstring'''




							def 							_UpperCamelCase      (	self	,      a = False )	->   Tuple:
														snake_case_           =							10

														if only_cross_attention:
																					snake_case_           =							12
														else:
																					# when only cross attention is not set, the cross attention dim must be the same as the query dim
																					snake_case_           =							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 							_UpperCamelCase      (	self	,      a	,      a )	->   Dict:
														snake_case_           =							2

														snake_case_           =							torch.rand(_UpperCAmelCase	,      _UpperCAmelCase	,      3	,      2 )
														snake_case_           =							torch.rand(_UpperCAmelCase	,      4	,      _UpperCAmelCase )
														snake_case_           =							None

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






							def 							_UpperCamelCase      (	self )	->   List[str]:
														torch.manual_seed(0 )

														snake_case_           =							self.get_constructor_arguments(only_cross_attention=_UpperCAmelCase )
														snake_case_           =							Attention(**_UpperCAmelCase )

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

														snake_case_           =							self.get_forward_arguments(
														    query_dim=constructor_args['query_dim']	,      added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

														snake_case_           =							attn(**_UpperCAmelCase )

														# only self attention

														torch.manual_seed(0 )

														snake_case_           =							self.get_constructor_arguments(only_cross_attention=_UpperCAmelCase )
														snake_case_           =							Attention(**_UpperCAmelCase )

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

														snake_case_           =							self.get_forward_arguments(
														    query_dim=constructor_args['query_dim']	,      added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

														snake_case_           =							attn(**_UpperCAmelCase )

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






class 			UpperCamelCase_				(      unittest.TestCase    ):



							'''simple docstring'''




							def 							_UpperCamelCase      (	self )	->   List[str]:
														snake_case_           =							DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'	,      safety_checker=_UpperCAmelCase )

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

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

														snake_case_           =							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(_UpperCAmelCase )

																					# can also load the converted weights
																					snake_case_           =							DiffusionPipeline.from_pretrained(_UpperCAmelCase	,      device_map='sequential'	,      safety_checker=_UpperCAmelCase )

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

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













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

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





