code: 
  
  
  """simple docstring"""
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			bool = True      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		{}
     for name in model.attn_processors.keys():
        _lowerCamelCase  :				Union[str, Any]      =		name.endswith("""attn2.processor"""      ) and not (
            name.startswith("""transformer_in"""      ) or """temp_attentions""" in name.split("""."""      )
        )
        _lowerCamelCase  :				int      =		model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith("""mid_block"""      ):
           _lowerCamelCase  :				int      =		model.config.block_out_channels[-1]
        elif name.startswith("""up_blocks"""      ):
           _lowerCamelCase  :				Optional[int]      =		int(name[len("""up_blocks."""      )]      )
           _lowerCamelCase  :				str      =		list(reversed(model.config.block_out_channels      )      )[block_id]
        elif name.startswith("""down_blocks"""      ):
           _lowerCamelCase  :				Dict      =		int(name[len("""down_blocks."""      )]      )
           _lowerCamelCase  :				Tuple      =		model.config.block_out_channels[block_id]
        elif name.startswith("""transformer_in"""      ):
           # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
           _lowerCamelCase  :				Tuple      =		8 * model.config.attention_head_dim
  
        _lowerCamelCase  :				Tuple      =		LoRAAttnProcessor(hidden_size=__snake_case	,   cross_attention_dim=__snake_case      )
        _lowerCamelCase  :				Union[str, Any]      =		lora_attn_procs[name].to(model.device      )
  
        if mock_weights:
           # add 1 to weights to mock trained weights
           with torch.no_grad():
              lora_attn_procs[name].to_q_lora.up.weight += 1
              lora_attn_procs[name].to_k_lora.up.weight += 1
              lora_attn_procs[name].to_v_lora.up.weight += 1
              lora_attn_procs[name].to_out_lora.up.weight += 1
  
     return lora_attn_procs
  
  
  
  
  
  @skip_mps
  class   lowercase__							(				A_   ,A_   ,unittest.TestCase							):
     __UpperCAmelCase       					=				UNetaDConditionModel
     __UpperCAmelCase       					=				'''sample'''
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				Optional[Any]      =		4
        _lowerCamelCase  :				Dict      =		4
        _lowerCamelCase  :				Union[str, Any]      =		4
        _lowerCamelCase  :				List[Any]      =		(32, 32)
  
        _lowerCamelCase  :				Optional[int]      =		floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)
  
        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        return (4, 4, 32, 32)
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        return (4, 4, 32, 32)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				List[str]      =		{
            """block_out_channels""": (32, 64),
            """down_block_types""": (
                """CrossAttnDownBlock3D""",
                """DownBlock3D""",
            ),
            """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
            """cross_attention_dim""": 32,
            """attention_head_dim""": 8,
            """out_channels""": 4,
            """in_channels""": 4,
            """layers_per_block""": 1,
            """sample_size""": 32,
        }
        _lowerCamelCase  :				List[Any]      =		self.dummy_input
        return init_dict, inputs_dict
  
  
  
  
  
     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()			,      reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,      )
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Tuple      =		self.prepare_init_args_and_inputs_for_common()
        _lowerCamelCase  :				List[str]      =		self.model_class(**SCREAMING_SNAKE_CASE)
  
        model.enable_xformers_memory_efficient_attention()
  
        assert (
            model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
            == "XFormersAttnProcessor"
        ), "xformers is not enabled"
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				str      =		32
  
        _lowerCamelCase  :				int      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
  
        with torch.no_grad():
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)
  
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Tuple      =		output.sample
  
        self.assertIsNotNone(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				int      =		inputs_dict["""sample"""].shape
        self.assertEqual(output.shape			,      SCREAMING_SNAKE_CASE			,      """Input and output shapes do not match""")
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
        _lowerCamelCase  :				Any      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
  
        with torch.no_grad():
           # Warmup pass when using mps (see #372)
           if torch_device == "mps" and isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              model(**self.dummy_input)
  
           _lowerCamelCase  :				str      =		model(**SCREAMING_SNAKE_CASE)
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				List[str]      =		first.sample
  
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				int      =		second.sample
  
        _lowerCamelCase  :				Optional[Any]      =		first.cpu().numpy()
        _lowerCamelCase  :				Tuple      =		second.cpu().numpy()
        _lowerCamelCase  :				str      =		out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
        _lowerCamelCase  :				List[str]      =		out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
        _lowerCamelCase  :				Optional[Any]      =		np.amax(np.abs(out_a - out_a))
        self.assertLessEqual(SCREAMING_SNAKE_CASE			,      1e-5)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase  :				List[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				str      =		8
  
        _lowerCamelCase  :				Optional[Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
  
        model.set_attention_slice("""auto""")
        with torch.no_grad():
           _lowerCamelCase  :				str      =		model(**SCREAMING_SNAKE_CASE)
        assert output is not None
  
        model.set_attention_slice("""max""")
        with torch.no_grad():
           _lowerCamelCase  :				Any      =		model(**SCREAMING_SNAKE_CASE)
        assert output is not None
  
        model.set_attention_slice(2)
        with torch.no_grad():
           _lowerCamelCase  :				Dict      =		model(**SCREAMING_SNAKE_CASE)
        assert output is not None
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				Optional[int]      =		8
  
        _lowerCamelCase  :				List[Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				List[Any]      =		model(**SCREAMING_SNAKE_CASE).sample
  
        _lowerCamelCase  :				Tuple      =		create_lora_layers(SCREAMING_SNAKE_CASE)
  
        # make sure we can set a list of attention processors
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        # test that attn processors can be set to itself
        model.set_attn_processor(model.attn_processors)
  
        with torch.no_grad():
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.0}).sample
           _lowerCamelCase  :				Any      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
           _lowerCamelCase  :				Union[str, Any]      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
  
        assert (samplea - samplea).abs().max() < 3e-3
        assert (samplea - samplea).abs().max() < 3e-3
  
        # sample 2 and sample 3 should be different
        assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				Optional[Any]      =		8
  
        torch.manual_seed(0)
        _lowerCamelCase  :				Tuple      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				Optional[Any]      =		model(**SCREAMING_SNAKE_CASE).sample
  
        _lowerCamelCase  :				Any      =		create_lora_layers(SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				Dict      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_attn_procs(SCREAMING_SNAKE_CASE)
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           torch.manual_seed(0)
           _lowerCamelCase  :				Union[str, Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
           new_model.to(SCREAMING_SNAKE_CASE)
           new_model.load_attn_procs(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				List[str]      =		new_model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
  
        assert (sample - new_sample).abs().max() < 1e-3
  
        # LoRA and no LoRA should NOT be the same
        assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				List[str]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				List[Any]      =		8
  
        torch.manual_seed(0)
        _lowerCamelCase  :				Any      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				Optional[int]      =		model(**SCREAMING_SNAKE_CASE).sample
  
        _lowerCamelCase  :				Dict      =		create_lora_layers(SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				List[str]      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_attn_procs(SCREAMING_SNAKE_CASE			,      safe_serialization=SCREAMING_SNAKE_CASE)
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.safetensors""")))
           torch.manual_seed(0)
           _lowerCamelCase  :				Union[str, Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
           new_model.to(SCREAMING_SNAKE_CASE)
           new_model.load_attn_procs(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				str      =		new_model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).sample
  
        assert (sample - new_sample).abs().max() < 3e-3
  
        # LoRA and no LoRA should NOT be the same
        assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        # enable deterministic behavior for gradient checkpointing
        _lowerCamelCase  :				List[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				str      =		8
  
        torch.manual_seed(0)
        _lowerCamelCase  :				Dict      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[Any]      =		create_lora_layers(SCREAMING_SNAKE_CASE			,      mock_weights=SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
        # Saving as torch, properly reloads with directly filename
        with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_attn_procs(SCREAMING_SNAKE_CASE)
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           torch.manual_seed(0)
           _lowerCamelCase  :				List[Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
           new_model.to(SCREAMING_SNAKE_CASE)
           new_model.load_attn_procs(SCREAMING_SNAKE_CASE			,      weight_name="""pytorch_lora_weights.bin""")
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Dict      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				Optional[Any]      =		8
  
        torch.manual_seed(0)
        _lowerCamelCase  :				str      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Tuple      =		create_lora_layers(SCREAMING_SNAKE_CASE			,      mock_weights=SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
        # Saving as torch, properly reloads with directly filename
        with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_attn_procs(SCREAMING_SNAKE_CASE)
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           torch.manual_seed(0)
           _lowerCamelCase  :				Optional[Any]      =		self.model_class(**SCREAMING_SNAKE_CASE)
           new_model.to(SCREAMING_SNAKE_CASE)
           with self.assertRaises(SCREAMING_SNAKE_CASE) as e:
              new_model.load_attn_procs(SCREAMING_SNAKE_CASE			,      use_safetensors=SCREAMING_SNAKE_CASE)
           self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""			,      str(e.exception))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				List[str]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				Tuple      =		8
  
        torch.manual_seed(0)
        _lowerCamelCase  :				Optional[int]      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				str      =		model(**SCREAMING_SNAKE_CASE).sample
  
        _lowerCamelCase  :				Optional[int]      =		create_lora_layers(SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
  
        with torch.no_grad():
           _lowerCamelCase  :				Dict      =		model(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.0}).sample
  
        model.set_attn_processor(AttnProcessor())
  
        with torch.no_grad():
           _lowerCamelCase  :				Any      =		model(**SCREAMING_SNAKE_CASE).sample
  
        assert (sample - new_sample).abs().max() < 1e-4
        assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  
  
     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()			,      reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,      )
     def 		UpperCamelCase_     (       self)     ->     str:
        # enable deterministic behavior for gradient checkpointing
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_init_args_and_inputs_for_common()
  
        _lowerCamelCase  :				Any      =		4
  
        torch.manual_seed(0)
        _lowerCamelCase  :				List[str]      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		create_lora_layers(SCREAMING_SNAKE_CASE)
        model.set_attn_processor(SCREAMING_SNAKE_CASE)
  
        # default
        with torch.no_grad():
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE).sample
  
           model.enable_xformers_memory_efficient_attention()
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE).sample
  
           model.disable_xformers_memory_efficient_attention()
           _lowerCamelCase  :				Union[str, Any]      =		model(**SCREAMING_SNAKE_CASE).sample
  
        assert (sample - on_sample).abs().max() < 1e-4
        assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase  :				Any      =		self.prepare_init_args_and_inputs_for_common()
        _lowerCamelCase  :				Tuple      =		32
  
        _lowerCamelCase  :				Tuple      =		self.model_class(**SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
  
        with torch.no_grad():
           _lowerCamelCase  :				Union[str, Any]      =		model(**SCREAMING_SNAKE_CASE)[0]
  
        model.enable_forward_chunking()
        with torch.no_grad():
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)[0]
  
        self.assertEqual(output.shape			,      output_a.shape			,      """Shape doesn't match""")
        assert np.abs(output.cpu() - output_a.cpu()).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		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
   )
  
  