



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
	import jax

@require_flax
class 	__snake_case :







							'''simple docstring'''






							def _a (							self						):
											a__ ,     a__			 =						self.prepare_init_args_and_inputs_for_common()

											a__			 =						self.model_class(**a_						)
											a__			 =						model.init(inputs_dict["""prng_key"""]					,					inputs_dict["""sample"""]						)
											jax.lax.stop_gradient(a_						)

											a__			 =						model.apply(a_					,					inputs_dict["""sample"""]						)

											if isinstance(a_					,					a_						):
															a__			 =						output.sample

											self.assertIsNotNone(a_						)
											a__			 =						inputs_dict["""sample"""].shape
											self.assertEqual(output.shape					,					a_					,					"""Input and output shapes do not match"""						)


							def _a (							self						):
											a__ ,     a__			 =						self.prepare_init_args_and_inputs_for_common()

											a__			 =						16
											a__			 =						(16, 32)

											a__			 =						self.model_class(**a_						)
											a__			 =						model.init(inputs_dict["""prng_key"""]					,					inputs_dict["""sample"""]						)
											jax.lax.stop_gradient(a_						)

											a__			 =						model.apply(a_					,					inputs_dict["""sample"""]						)

											if isinstance(a_					,					a_						):
															a__			 =						output.sample

											self.assertIsNotNone(a_						)
											a__			 =						inputs_dict["""sample"""].shape
											self.assertEqual(output.shape					,					a_					,					"""Input and output shapes do not match"""						)







							def _a (							self						):
											a__			 =						"""kwargs""" in inspect.signature(self.model_class.__init__						).parameters
											a__			 =						len(self.model_class._deprecated_kwargs						) > 0

											if has_kwarg_in_model_class and not has_deprecated_kwarg:
															raise ValueError(
															    F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
															    """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
															    """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
															    """ [<deprecated_argument>]`"""						)

											if not has_kwarg_in_model_class and has_deprecated_kwarg:
															raise ValueError(
															    F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
															    """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
															    F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
															    """ from `_deprecated_kwargs = [<deprecated_argument>]`"""						)






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						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
	)

