



import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


UpperCAmelCase								=   logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: torch.FloatTensor
							UpperCamelCase__			: Optional[torch.FloatTensor]             =   None




def      A_ (		__a       :			List[str]	,						__a       :			Any=0.9_9_9	,						__a       :			int="cosine"	,						):

				"""simple docstring"""
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__a       :			List[Any]						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__a       :			List[Any]						):
												return math.exp(t * -12.0						)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

				a__			 =						[]
				for i in range(__a						):
								a__			 =						i / num_diffusion_timesteps
								a__			 =						(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__a						) / alpha_bar_fn(__a						)	,						__a						)						)
				return torch.tensor(__a	,						dtype=torch.floataa						)




class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Any             =   [e.name for e in KarrasDiffusionSchedulers]
							UpperCamelCase__			: Optional[int]             =   1


							@register_to_config
							def __init__(							self					,					a_ = 1_000					,					a_ = 0.0_001					,					a_ = 0.02					,					a_ = "linear"					,					a_ = None					,					a_ = "epsilon"					,					a_ = "linear"					,					a_ = False					,					a_ = "linspace"					,					a_ = 0					,					):
											if trained_betas is not None:
															a__			 =						torch.tensor(a_					,					dtype=torch.floataa						)
											elif beta_schedule == "linear":
															a__			 =						torch.linspace(a_					,					a_					,					a_					,					dtype=torch.floataa						)
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															a__			 =						(
															    torch.linspace(beta_start**0.5					,					beta_end**0.5					,					a_					,					dtype=torch.floataa						) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															a__			 =						betas_for_alpha_bar(a_						)
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

											a__			 =						1.0 - self.betas
											a__			 =						torch.cumprod(self.alphas					,					dim=0						)

											a__			 =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											a__			 =						np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
											a__			 =						torch.from_numpy(a_						)

											# setable values
											a__			 =						None
											a__			 =						np.linspace(0					,					num_train_timesteps - 1					,					a_					,					dtype=a_						)[::-1].copy()
											a__			 =						torch.from_numpy(a_						)
											a__			 =						False
											a__			 =						use_karras_sigmas


							@property
							def _a (							self						):
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5


							def _a (							self					,					a_					,					a_						):
											if isinstance(a_					,					torch.Tensor						):
															a__			 =						timestep.to(self.timesteps.device						)
											a__			 =						(self.timesteps == timestep).nonzero().item()
											a__			 =						self.sigmas[step_index]

											a__			 =						sample / ((sigma**2 + 1) ** 0.5)

											a__			 =						True
											return sample


							def _a (							self					,					a_					,					a_ = None						):
											a__			 =						num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
															a__			 =						np.linspace(0					,					self.config.num_train_timesteps - 1					,					a_					,					dtype=a_						)[
															    ::-1
															].copy()
											elif self.config.timestep_spacing == "leading":
															a__			 =						self.config.num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															a__			 =						(np.arange(0					,					a_						) * step_ratio).round()[::-1].copy().astype(a_						)
															timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
															a__			 =						self.config.num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															a__			 =						(np.arange(self.config.num_train_timesteps					,					0					,					-step_ratio						)).round().copy().astype(a_						)
															timesteps -= 1
											else:
															raise ValueError(
															    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

											a__			 =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											a__			 =						np.log(a_						)

											if self.config.interpolation_type == "linear":
															a__			 =						np.interp(a_					,					np.arange(0					,					len(a_						)						)					,					a_						)
											elif self.config.interpolation_type == "log_linear":
															a__			 =						torch.linspace(np.log(sigmas[-1]						)					,					np.log(sigmas[0]						)					,					num_inference_steps + 1						).exp()
											else:
															raise ValueError(
															    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
															    """ 'linear' or 'log_linear'"""						)

											if self.use_karras_sigmas:
															a__			 =						self._convert_to_karras(in_sigmas=a_					,					num_inference_steps=self.num_inference_steps						)
															a__			 =						np.array([self._sigma_to_t(a_					,					a_						) for sigma in sigmas]						)

											a__			 =						np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
											a__			 =						torch.from_numpy(a_						).to(device=a_						)
											if str(a_						).startswith("""mps"""						):
															# mps does not support float64
															a__			 =						torch.from_numpy(a_						).to(a_					,					dtype=torch.floataa						)
											else:
															a__			 =						torch.from_numpy(a_						).to(device=a_						)


							def _a (							self					,					a_					,					a_						):
											# get log sigma
											a__			 =						np.log(a_						)

											# get distribution
											a__			 =						log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											a__			 =						np.cumsum((dists >= 0)					,					axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
											a__			 =						low_idx + 1

											a__			 =						log_sigmas[low_idx]
											a__			 =						log_sigmas[high_idx]

											# interpolate sigmas
											a__			 =						(low - log_sigma) / (low - high)
											a__			 =						np.clip(a_					,					0					,					1						)

											# transform interpolation to time range
											a__			 =						(1 - w) * low_idx + w * high_idx
											a__			 =						t.reshape(sigma.shape						)
											return t


							def _a (							self					,					a_					,					a_						):
											a__			 =						in_sigmas[-1].item()
											a__			 =						in_sigmas[0].item()

											a__			 =						7.0  # 7.0 is the value used in the paper
											a__			 =						np.linspace(0					,					1					,					a_						)
											a__			 =						sigma_min ** (1 / rho)
											a__			 =						sigma_max ** (1 / rho)
											a__			 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas


							def _a (							self					,					a_					,					a_					,					a_					,					a_ = 0.0					,					a_ = 0.0					,					a_ = float("""inf"""						)					,					a_ = 1.0					,					a_ = None					,					a_ = True					,					):

											if (
											    isinstance(a_					,					a_						)
											    or isinstance(a_					,					torch.IntTensor						)
											    or isinstance(a_					,					torch.LongTensor						)
											):
															raise ValueError(
															    (
															        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
															        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
															        """ one of the `scheduler.timesteps` as a timestep."""
															    )					,					)

											if not self.is_scale_input_called:
															logger.warning(
															    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
															    """See `StableDiffusionPipeline` for a usage example."""						)

											if isinstance(a_					,					torch.Tensor						):
															a__			 =						timestep.to(self.timesteps.device						)

											a__			 =						(self.timesteps == timestep).nonzero().item()
											a__			 =						self.sigmas[step_index]

											a__			 =						min(s_churn / (len(self.sigmas						) - 1)					,					2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

											a__			 =						randn_tensor(
											    model_output.shape					,					dtype=model_output.dtype					,					device=model_output.device					,					generator=a_						)

											a__			 =						noise * s_noise
											a__			 =						sigma * (gamma + 1)

											if gamma > 0:
															a__			 =						sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
															a__			 =						model_output
											elif self.config.prediction_type == "epsilon":
															a__			 =						sample - sigma_hat * model_output
											elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															a__			 =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											else:
															raise ValueError(
															    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

											# 2. Convert to an ODE derivative
											a__			 =						(sample - pred_original_sample) / sigma_hat

											a__			 =						self.sigmas[step_index + 1] - sigma_hat

											a__			 =						sample + derivative * dt

											if not return_dict:
															return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=a_					,					pred_original_sample=a_						)


							def _a (							self					,					a_					,					a_					,					a_					,					):
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											a__			 =						self.sigmas.to(device=original_samples.device					,					dtype=original_samples.dtype						)
											if original_samples.device.type == "mps" and torch.is_floating_point(a_						):
															# mps does not support float64
															a__			 =						self.timesteps.to(original_samples.device					,					dtype=torch.floataa						)
															a__			 =						timesteps.to(original_samples.device					,					dtype=torch.floataa						)
											else:
															a__			 =						self.timesteps.to(original_samples.device						)
															a__			 =						timesteps.to(original_samples.device						)

											a__			 =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											a__			 =						sigmas[step_indices].flatten()
											while len(sigma.shape						) < len(original_samples.shape						):
															a__			 =						sigma.unsqueeze(-1						)

											a__			 =						original_samples + noise * sigma
											return noisy_samples







							def __len__(							self						):
											return self.config.num_train_timesteps






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
	)

