"""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        __UpperCAmelCase						:
				A__				:       Optional[Any]  							=	()
				A__				:       Any  							=	True

				def 		_a     (				self				):
										lowerCamelCase__ =self.config_class

										# test default config
										lowerCamelCase__ =config_class()
										self.assertIsNotNone(__UpperCamelCase				)
										lowerCamelCase__ =["stem"] + [F'''stage{idx}''' for idx in range(1     ,			len(config.depths				) + 1				)]
										self.assertEqual(config.stage_names     ,			__UpperCamelCase				)
										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__ =config_class(out_features=__UpperCamelCase     ,			out_indices=__UpperCamelCase				)
										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__ =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__ =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__ =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(__UpperCamelCase				):
																lowerCamelCase__ =config_class(out_features=["stage1", "stage2"]     ,			out_indices=[0, 2]				)

				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																lowerCamelCase__ =inspect.signature(model.forward				)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowerCamelCase__ =[*signature.parameters.keys()]
																lowerCamelCase__ =["pixel_values"]
																self.assertListEqual(arg_names[:1]     ,			__UpperCamelCase				)

				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																self.assertEqual(len(model.channels				)     ,			len(config.out_features				)				)
																lowerCamelCase__ =model.num_features
																lowerCamelCase__ =[config.stage_names.index(__UpperCamelCase				) for feat in config.out_features]
																lowerCamelCase__ =[num_features[idx] for idx in out_indices]
																self.assertListEqual(model.channels     ,			__UpperCamelCase				)

																lowerCamelCase__ =copy.deepcopy(__UpperCamelCase				)
																lowerCamelCase__ =None
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																self.assertEqual(len(model.channels				)     ,			1				)
																self.assertListEqual(model.channels     ,			[num_features[-1]]				)

																lowerCamelCase__ =copy.deepcopy(__UpperCamelCase				)
																lowerCamelCase__ =None
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																self.assertEqual(len(model.channels				)     ,			1				)
																self.assertListEqual(model.channels     ,			[num_features[-1]]				)

				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																model.to(__UpperCamelCase				)
																model.eval()
																lowerCamelCase__ =model(**__UpperCamelCase				)

																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__ =copy.deepcopy(__UpperCamelCase				)
																lowerCamelCase__ =None
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																model.to(__UpperCamelCase				)
																model.eval()
																lowerCamelCase__ =model(**__UpperCamelCase				)

																self.assertEqual(len(result.feature_maps				)     ,			1				)
																self.assertEqual(len(model.channels				)     ,			1				)

																lowerCamelCase__ =copy.deepcopy(__UpperCamelCase				)
																lowerCamelCase__ =None
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																model.to(__UpperCamelCase				)
																model.eval()
																lowerCamelCase__ =model(**__UpperCamelCase				)

																self.assertEqual(len(result.feature_maps				)     ,			1				)
																self.assertEqual(len(model.channels				)     ,			1				)

																# Check backbone can be initialized with fresh weights
																lowerCamelCase__ =copy.deepcopy(__UpperCamelCase				)
																lowerCamelCase__ =False
																lowerCamelCase__ =model_class(__UpperCamelCase				)
																model.to(__UpperCamelCase				)
																model.eval()
																lowerCamelCase__ =model(**__UpperCamelCase				)

				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

										for backbone_class in self.all_model_classes:
																lowerCamelCase__ =backbone_class(__UpperCamelCase				)

																self.assertTrue(hasattr(__UpperCamelCase     ,			"backbone_type"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"stage_names"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"num_features"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"out_indices"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"out_features"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"out_feature_channels"				)				)
																self.assertTrue(hasattr(__UpperCamelCase     ,			"channels"				)				)

																self.assertIsInstance(backbone.backbone_type     ,			__UpperCamelCase				)
																# 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 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__ =inputs_dict["pixel_values"].shape[0]

										for backbone_class in self.all_model_classes:
																lowerCamelCase__ =backbone_class(__UpperCamelCase				)
																backbone.to(__UpperCamelCase				)
																backbone.eval()

																lowerCamelCase__ =backbone(**__UpperCamelCase				)

																# Test default outputs and verify feature maps
																self.assertIsInstance(outputs.feature_maps     ,			__UpperCamelCase				)
																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__ =backbone(**__UpperCamelCase     ,			output_hidden_states=__UpperCamelCase				)
																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__ =backbone(**__UpperCamelCase     ,			output_attentions=__UpperCamelCase				)
																						self.assertIsNotNone(outputs.attentions				)


"""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
	)


