"""simple docstring"""


import functools
from math import sqrt
from time import time





def       lowerCamelCase_			( __lowerCAmelCase       ,   *__lowerCAmelCase       ,   **__lowerCAmelCase    )							-> Dict:




						'''simple docstring'''



						lowerCamelCase__ =time()
						lowerCamelCase__ =func(*__lowerCAmelCase       ,   **__lowerCAmelCase    )
						lowerCamelCase__ =time()
						if int(end - start    ) > 0:
												print(F'''{func.__name__} runtime: {(end - start):0.4f} s'''    )
						else:
												print(F'''{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'''    )
						return output





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:




						'''simple docstring'''



						if n < 0:
												raise Exception("n is negative"    )
						if n == 0:
												return [0]
						lowerCamelCase__ =[0, 1]
						for _ in range(n - 1    ):
												fib.append(fib[-1] + fib[-2]    )
						return fib





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:




						'''simple docstring'''



						def fib_recursive_term(__lowerCAmelCase    ) -> int:
												if i < 0:
																		raise Exception("n is negative"    )
												if i < 2:
																		return i
												return fib_recursive_term(i - 1    ) + fib_recursive_term(i - 2    )

						if n < 0:
												raise Exception("n is negative"    )
						return [fib_recursive_term(__lowerCAmelCase    ) for i in range(n + 1    )]





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:




						'''simple docstring'''



						@functools.cache
						def fib_recursive_term(__lowerCAmelCase    ) -> int:
												if i < 0:
																		raise Exception("n is negative"    )
												if i < 2:
																		return i
												return fib_recursive_term(i - 1    ) + fib_recursive_term(i - 2    )

						if n < 0:
												raise Exception("n is negative"    )
						return [fib_recursive_term(__lowerCAmelCase    ) for i in range(n + 1    )]





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:




						'''simple docstring'''



						if n < 0:
												raise Exception("n is negative"    )
						# Cache must be outside recursuive function
						# other it will reset every time it calls itself.
						lowerCamelCase__ ={0: 0, 1: 1, 2: 1}  # Prefilled cache

						def rec_fn_memoized(__lowerCAmelCase    ) -> int:
												if num in cache:
																		return cache[num]

												lowerCamelCase__ =rec_fn_memoized(num - 1    ) + rec_fn_memoized(num - 2    )
												lowerCamelCase__ =value
												return value

						return [rec_fn_memoized(__lowerCAmelCase    ) for i in range(n + 1    )]





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:




						'''simple docstring'''



						if n < 0:
												raise Exception("n is negative"    )
						if n >= 1475:
												raise Exception("n is too large"    )
						lowerCamelCase__ =sqrt(5    )
						lowerCamelCase__ =(1 + sqrt_a) / 2
						return [round(phi**i / sqrt_a    ) for i in range(n + 1    )]


if __name__ == "__main__":
	a							=30
	time_func(fib_iterative, num)
	time_func(fib_recursive, num)  # Around 3s runtime
	time_func(fib_recursive_cached, num)  # Around 0ms runtime
	time_func(fib_memoization, num)
	time_func(fib_binet, num)



"""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
	)


