"""simple docstring"""




from itertools import count
from math import asin, pi, sqrt


def SCREAMING_SNAKE_CASE			(       lowercase_:		float			):
       return (
           (1 - 2 * point) * sqrt(point - point**2			) + 2 * point + asin(sqrt(1 - point			)			)
       ) / 4


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       _UpperCamelCase       :    Union[str, Any]					  =					(circles_number + 1 - sqrt(2 * circles_number			)) / (
           2 * (circles_number**2 + 1)
       )
       _UpperCamelCase       :    List[str]					  =					circles_number * intersection_y

       _UpperCamelCase       :    Any					  =					intersection_x * intersection_y / 2
       _UpperCamelCase       :    Dict					  =					circle_bottom_arc_integral(
           1 / 2			) - circle_bottom_arc_integral(lowercase_			)

       return triangle_area + concave_region_area


def SCREAMING_SNAKE_CASE			(       lowercase_:		float = 1 / 1000			):
       _UpperCamelCase       :    Optional[int]					  =					(1 - pi / 4) / 4

       for n in count(1			):
              if concave_triangle_area(lowercase_			) / l_section_area < fraction:
                     return n

       return -1


if __name__ == "__main__":
 print(f'''{solution() = }''')




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					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
 )



