


"""simple docstring"""




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


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     """trajectory_transformer"""
		SCREAMING_SNAKE_CASE_						:     Tuple								=     ["""past_key_values"""]
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     {
		    """hidden_size""": """n_embd""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}







		def __init__(	self						,       lowerCAmelCase__=100						,       lowerCAmelCase__=5						,       lowerCAmelCase__=1						,       lowerCAmelCase__=1						,       lowerCAmelCase__=249						,       lowerCAmelCase__=6						,       lowerCAmelCase__=17						,       lowerCAmelCase__=25						,       lowerCAmelCase__=4						,       lowerCAmelCase__=4						,       lowerCAmelCase__=128						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=0.00_06						,       lowerCAmelCase__=512						,       lowerCAmelCase__=0.02						,       lowerCAmelCase__=1e-12						,       lowerCAmelCase__=1						,       lowerCAmelCase__=True						,       lowerCAmelCase__=1						,       lowerCAmelCase__=50_256						,       lowerCAmelCase__=50_256						,       **lowerCAmelCase__						,       )     ->							str:
								SCREAMING_SNAKE_CASE													=					vocab_size
								SCREAMING_SNAKE_CASE													=					action_weight
								SCREAMING_SNAKE_CASE													=					reward_weight
								SCREAMING_SNAKE_CASE													=					value_weight
								SCREAMING_SNAKE_CASE													=					max_position_embeddings
								SCREAMING_SNAKE_CASE													=					block_size
								SCREAMING_SNAKE_CASE													=					action_dim
								SCREAMING_SNAKE_CASE													=					observation_dim
								SCREAMING_SNAKE_CASE													=					transition_dim
								SCREAMING_SNAKE_CASE													=					learning_rate
								SCREAMING_SNAKE_CASE													=					n_layer
								SCREAMING_SNAKE_CASE													=					n_head
								SCREAMING_SNAKE_CASE													=					n_embd
								SCREAMING_SNAKE_CASE													=					embd_pdrop
								SCREAMING_SNAKE_CASE													=					attn_pdrop
								SCREAMING_SNAKE_CASE													=					resid_pdrop
								SCREAMING_SNAKE_CASE													=					initializer_range
								SCREAMING_SNAKE_CASE													=					layer_norm_eps
								SCREAMING_SNAKE_CASE													=					kaiming_initializer_range
								SCREAMING_SNAKE_CASE													=					use_cache
								super().__init__(pad_token_id=lowerCAmelCase__						,       bos_token_id=lowerCAmelCase__						,       eos_token_id=lowerCAmelCase__						,       **lowerCAmelCase__				)






"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


