



import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	UpperCAmelCase								=   True
	UpperCAmelCase								=   """"""
except Exception as e:
	UpperCAmelCase								=   False
	UpperCAmelCase								=   (
	    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
	)


from PIL import Image  # noqa: E402

class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Optional[int]             =   """mel_config.json"""


							@register_to_config
							def __init__(							self					,					a_ = 256					,					a_ = 256					,					a_ = 22_050					,					a_ = 2_048					,					a_ = 512					,					a_ = 80					,					a_ = 32					,					):
											a__			 =						hop_length
											a__			 =						sample_rate
											a__			 =						n_fft
											a__			 =						top_db
											a__			 =						n_iter
											self.set_resolution(a_					,					a_						)
											a__			 =						None

											if not _librosa_can_be_imported:
															raise ValueError(_import_error						)


							def _a (							self					,					a_					,					a_						):
											a__			 =						x_res
											a__			 =						y_res
											a__			 =						self.y_res
											a__			 =						self.x_res * self.hop_length - 1


							def _a (							self					,					a_ = None					,					a_ = None						):
											if audio_file is not None:
															a__ ,     a__			 =						librosa.load(a_					,					mono=a_					,					sr=self.sr						)
											else:
															a__			 =						raw_audio

											# Pad with silence if necessary.
											if len(self.audio						) < self.x_res * self.hop_length:
															a__			 =						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio						),)						)]						)


							def _a (							self						):
											return len(self.audio						) // self.slice_size


							def _a (							self					,					a_ = 0						):
											return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


							def _a (							self						):
											return self.sr


							def _a (							self					,					a_						):
											a__			 =						librosa.feature.melspectrogram(
											    y=self.get_audio_slice(a_						)					,					sr=self.sr					,					n_fft=self.n_fft					,					hop_length=self.hop_length					,					n_mels=self.n_mels						)
											a__			 =						librosa.power_to_db(a_					,					ref=np.max					,					top_db=self.top_db						)
											a__			 =						(((log_S + self.top_db) * 255 / self.top_db).clip(0					,					255						) + 0.5).astype(np.uinta						)
											a__			 =						Image.fromarray(a_						)
											return image







							def _a (							self					,					a_						):
											a__			 =						np.frombuffer(image.tobytes()					,					dtype="""uint8"""						).reshape((image.height, image.width)						)
											a__			 =						bytedata.astype("""float"""						) * self.top_db / 255 - self.top_db
											a__			 =						librosa.db_to_power(a_						)
											a__			 =						librosa.feature.inverse.mel_to_audio(
											    a_					,					sr=self.sr					,					n_fft=self.n_fft					,					hop_length=self.hop_length					,					n_iter=self.n_iter						)
											return audio






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
	)

