
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class      A__							(     __magic_name__	):
				lowercase			    =  'decision_transformer'
				lowercase			    =  ['past_key_values']
				lowercase			    =  {
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}






				def __init__(						self					:      str    ,     a					:      Optional[int]=17    ,     a					:      str=4    ,     a					:      str=128    ,     a					:      Any=4_096    ,     a					:      Union[str, Any]=True    ,     a					:      Tuple=1    ,     a					:      Tuple=1_024    ,     a					:      List[Any]=3    ,     a					:      str=1    ,     a					:      Optional[Any]=None    ,     a					:      List[Any]="relu"    ,     a					:      Optional[int]=0.1    ,     a					:      Tuple=0.1    ,     a					:      int=0.1    ,     a					:      Tuple=1E-5    ,     a					:      Union[str, Any]=0.0_2    ,     a					:      List[Any]=True    ,     a					:      Tuple=True    ,     a					:      Optional[int]=50_256    ,     a					:      Optional[Any]=50_256    ,     a					:      Optional[Any]=False    ,     a					:      Optional[int]=False    ,     **a					:      List[str]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		state_dim
							lowerCAmelCase__      :      List[Any]         =		act_dim
							lowerCAmelCase__      :      List[Any]         =		hidden_size
							lowerCAmelCase__      :      List[str]         =		max_ep_len
							lowerCAmelCase__      :      List[Any]         =		action_tanh
							lowerCAmelCase__      :      List[str]         =		vocab_size
							lowerCAmelCase__      :      int         =		n_positions
							lowerCAmelCase__      :      int         =		n_layer
							lowerCAmelCase__      :      Dict         =		n_head
							lowerCAmelCase__      :      List[Any]         =		n_inner
							lowerCAmelCase__      :      List[str]         =		activation_function
							lowerCAmelCase__      :      Tuple         =		resid_pdrop
							lowerCAmelCase__      :      Dict         =		embd_pdrop
							lowerCAmelCase__      :      Dict         =		attn_pdrop
							lowerCAmelCase__      :      Tuple         =		layer_norm_epsilon
							lowerCAmelCase__      :      List[Any]         =		initializer_range
							lowerCAmelCase__      :      List[Any]         =		scale_attn_weights
							lowerCAmelCase__      :      Any         =		use_cache
							lowerCAmelCase__      :      Optional[int]         =		scale_attn_by_inverse_layer_idx
							lowerCAmelCase__      :      Dict         =		reorder_and_upcast_attn

							lowerCAmelCase__      :      Dict         =		bos_token_id
							lowerCAmelCase__      :      List[Any]         =		eos_token_id

							super().__init__(bos_token_id=a    ,     eos_token_id=a    ,     **a   )

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )