code: 
  
  
  """simple docstring"""
  import copy
  import inspect
  
  from transformers.testing_utils import require_torch, torch_device
  from transformers.utils.backbone_utils import BackboneType
  
  
  
  
  @require_torch
  class   lowercase__							:
     __UpperCAmelCase       					=				()
     __UpperCAmelCase       					=				True
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Any      =		self.config_class
  
        # test default config
        _lowerCamelCase  :				Optional[Any]      =		config_class()
        self.assertIsNotNone(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		["""stem"""] + [F'stage{idx}' for idx in range(1			,      len(config.depths) + 1)]
        self.assertEqual(config.stage_names			,      SCREAMING_SNAKE_CASE)
        self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))
  
        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        _lowerCamelCase  :				Optional[Any]      =		config_class(out_features=SCREAMING_SNAKE_CASE			,      out_indices=SCREAMING_SNAKE_CASE)
        self.assertEqual(config.out_features			,      [config.stage_names[-1]])
        self.assertEqual(config.out_indices			,      [len(config.stage_names) - 1])
  
        # out_features and out_indices both set
        _lowerCamelCase  :				int      =		config_class(out_features=["""stem""", """stage1"""]			,      out_indices=[0, 1])
        self.assertEqual(config.out_features			,      ["""stem""", """stage1"""])
        self.assertEqual(config.out_indices			,      [0, 1])
  
        # Only out_features set
        _lowerCamelCase  :				Union[str, Any]      =		config_class(out_features=["""stage1""", """stage3"""])
        self.assertEqual(config.out_features			,      ["""stage1""", """stage3"""])
        self.assertEqual(config.out_indices			,      [1, 3])
  
        # Only out_indices set
        _lowerCamelCase  :				Tuple      =		config_class(out_indices=[0, 2])
        self.assertEqual(config.out_features			,      [config.stage_names[0], config.stage_names[2]])
        self.assertEqual(config.out_indices			,      [0, 2])
  
        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				List[str]      =		config_class(out_features=["""stage1""", """stage2"""]			,      out_indices=[0, 2])
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase      ,						_lowerCamelCase  :				Dict      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				str      =		model_class(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Tuple      =		inspect.signature(model.forward)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowerCamelCase  :				str      =		[*signature.parameters.keys()]
           _lowerCamelCase  :				List[str]      =		["""pixel_values"""]
           self.assertListEqual(arg_names[:1]			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				Optional[Any]      =		model_class(SCREAMING_SNAKE_CASE)
           self.assertEqual(len(model.channels)			,      len(config.out_features))
           _lowerCamelCase  :				Any      =		model.num_features
           _lowerCamelCase  :				str      =		[config.stage_names.index(SCREAMING_SNAKE_CASE) for feat in config.out_features]
           _lowerCamelCase  :				Tuple      =		[num_features[idx] for idx in out_indices]
           self.assertListEqual(model.channels			,      SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase  :				Tuple      =		copy.deepcopy(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		None
           _lowerCamelCase  :				str      =		model_class(SCREAMING_SNAKE_CASE)
           self.assertEqual(len(model.channels)			,      1)
           self.assertListEqual(model.channels			,      [num_features[-1]])
  
           _lowerCamelCase  :				Tuple      =		copy.deepcopy(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[Any]      =		None
           _lowerCamelCase  :				Dict      =		model_class(SCREAMING_SNAKE_CASE)
           self.assertEqual(len(model.channels)			,      1)
           self.assertListEqual(model.channels			,      [num_features[-1]])
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase      ,						_lowerCamelCase  :				Dict      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				Optional[Any]      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)
  
           self.assertEqual(len(result.feature_maps)			,      len(config.out_features))
           self.assertEqual(len(model.channels)			,      len(config.out_features))
           self.assertEqual(len(result.feature_maps)			,      len(config.out_indices))
           self.assertEqual(len(model.channels)			,      len(config.out_indices))
  
           # Check output of last stage is taken if out_features=None, out_indices=None
           _lowerCamelCase  :				Optional[int]      =		copy.deepcopy(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				str      =		None
           _lowerCamelCase  :				Any      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)
  
           self.assertEqual(len(result.feature_maps)			,      1)
           self.assertEqual(len(model.channels)			,      1)
  
           _lowerCamelCase  :				Optional[Any]      =		copy.deepcopy(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		None
           _lowerCamelCase  :				int      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _lowerCamelCase  :				Optional[Any]      =		model(**SCREAMING_SNAKE_CASE)
  
           self.assertEqual(len(result.feature_maps)			,      1)
           self.assertEqual(len(model.channels)			,      1)
  
           # Check backbone can be initialized with fresh weights
           _lowerCamelCase  :				Tuple      =		copy.deepcopy(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				int      =		False
           _lowerCamelCase  :				str      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for backbone_class in self.all_model_classes:
           _lowerCamelCase  :				List[str]      =		backbone_class(SCREAMING_SNAKE_CASE)
  
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """backbone_type"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """stage_names"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """num_features"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """out_indices"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """out_features"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """out_feature_channels"""))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """channels"""))
  
           self.assertIsInstance(backbone.backbone_type			,      SCREAMING_SNAKE_CASE)
           # Verify num_features has been initialized in the backbone init
           self.assertIsNotNone(backbone.num_features)
           self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
           self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
           self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
           self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs_for_common()
        _lowerCamelCase  :				str      =		inputs_dict["""pixel_values"""].shape[0]
  
        for backbone_class in self.all_model_classes:
           _lowerCamelCase  :				List[Any]      =		backbone_class(SCREAMING_SNAKE_CASE)
           backbone.to(SCREAMING_SNAKE_CASE)
           backbone.eval()
  
           _lowerCamelCase  :				Any      =		backbone(**SCREAMING_SNAKE_CASE)
  
           # Test default outputs and verify feature maps
           self.assertIsInstance(outputs.feature_maps			,      SCREAMING_SNAKE_CASE)
           self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
           for feature_map, n_channels in zip(outputs.feature_maps			,      backbone.channels):
              self.assertTrue(feature_map.shape[:2]			,      (batch_size, n_channels))
           self.assertIsNone(outputs.hidden_states)
           self.assertIsNone(outputs.attentions)
  
           # Test output_hidden_states=True
           _lowerCamelCase  :				Optional[Any]      =		backbone(**SCREAMING_SNAKE_CASE			,      output_hidden_states=SCREAMING_SNAKE_CASE)
           self.assertIsNotNone(outputs.hidden_states)
           self.assertTrue(len(outputs.hidden_states)			,      len(backbone.stage_names))
           for hidden_state, n_channels in zip(outputs.hidden_states			,      backbone.channels):
              self.assertTrue(hidden_state.shape[:2]			,      (batch_size, n_channels))
  
           # Test output_attentions=True
           if self.has_attentions:
              _lowerCamelCase  :				Tuple      =		backbone(**SCREAMING_SNAKE_CASE			,      output_attentions=SCREAMING_SNAKE_CASE)
              self.assertIsNotNone(outputs.attentions)
  
  
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
   )
  
  