



"""simple docstring"""







import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
     import torch

     from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
     import pyanvml.pyanvml as nvml


lowercase__						:							str        =      logging.get_logger(__name__)







class  UpperCamelCase__  (      _UpperCamelCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	"PyTorch"



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        return torch.__version__



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Tuple			):
        lowerCAmelCase_  :							Union[str, Any]								=					self._prepare_inference_func(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
        return self._measure_speed(_inference			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]			):
        lowerCAmelCase_  :							Tuple								=					self._prepare_inference_func(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
        return self._measure_memory(_inference			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Any			):
        lowerCAmelCase_  :							int								=					self._prepare_train_func(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
        return self._measure_speed(_train			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: List[Any]			):
        lowerCAmelCase_  :							List[Any]								=					self._prepare_train_func(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
        return self._measure_memory(_train			)



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: Dict			):
        lowerCAmelCase_  :							Optional[Any]								=					self.config_dict[model_name]

        if self.args.torchscript:
          lowerCAmelCase_  :							List[Any]								=					True

        lowerCAmelCase_  :							Optional[Any]								=					(
            hasattr(_SCREAMING_SNAKE_CASE       ,     'architectures'			)
            and isinstance(config.architectures       ,     _SCREAMING_SNAKE_CASE			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            lowerCAmelCase_  :							str								=					config.architectures[0]
            lowerCAmelCase_  :							Optional[Any]								=					__import__('transformers'       ,     fromlist=[model_class]			)
            lowerCAmelCase_  :							Optional[int]								=					getattr(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
            lowerCAmelCase_  :							Dict								=					model_cls(_SCREAMING_SNAKE_CASE			)
          except ImportError:
            raise ImportError(
                F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'			)
        else:
          lowerCAmelCase_  :							str								=					MODEL_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE			)

        model.eval()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase_  :							int								=					config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE       ,     'vocab_size'			) else config.encoder.vocab_size
        lowerCAmelCase_  :							Any								=					torch.randint(_SCREAMING_SNAKE_CASE       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device			)

        if self.args.fpaa:
          logger.info('Running training in Mixed Precision...'			)
          if not self.args.is_gpu:
            raise ValueError('Mixed precision is possible only for GPU.'			)
          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

        if self.args.torchscript:
          with torch.no_grad():
            lowerCAmelCase_  :							Optional[Any]								=					torch.jit.trace(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
        else:
          lowerCAmelCase_  :							Optional[Any]								=					model

        def encoder_decoder_forward():
          with torch.no_grad():
            lowerCAmelCase_  :							List[str]								=					inference_model(_SCREAMING_SNAKE_CASE       ,     decoder_input_ids=_SCREAMING_SNAKE_CASE			)
          return outputs

        def encoder_forward():
          with torch.no_grad():
            lowerCAmelCase_  :							Any								=					inference_model(_SCREAMING_SNAKE_CASE			)
          return outputs

        lowerCAmelCase_  :							Optional[int]								=					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Tuple			):
        lowerCAmelCase_  :							Optional[int]								=					self.config_dict[model_name]

        lowerCAmelCase_  :							int								=					(
            hasattr(_SCREAMING_SNAKE_CASE       ,     'architectures'			)
            and isinstance(config.architectures       ,     _SCREAMING_SNAKE_CASE			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            lowerCAmelCase_  :							List[str]								=					config.architectures[0]
            lowerCAmelCase_  :							List[str]								=					__import__('transformers'       ,     fromlist=[model_class]			)
            lowerCAmelCase_  :							str								=					getattr(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			)
            lowerCAmelCase_  :							str								=					model_cls(_SCREAMING_SNAKE_CASE			)
          except ImportError:
            raise ImportError(
                F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'			)
        else:
          lowerCAmelCase_  :							List[Any]								=					MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE			)

        if self.args.torchscript:
          raise NotImplementedError('Training for torchscript is currently not implemented'			)
        else:
          lowerCAmelCase_  :							Any								=					model

        model.train()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase_  :							Optional[int]								=					config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE       ,     'vocab_size'			) else config.encoder.vocab_size
        lowerCAmelCase_  :							Optional[Any]								=					torch.randint(_SCREAMING_SNAKE_CASE       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device			)

        if self.args.fpaa:
          logger.info('Running training in Mixed Precision...'			)
          if not self.args.is_gpu:
            raise ValueError('Mixed precision is possible only for GPU.'			)

          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

        def compute_loss_and_backprob_encoder():
          lowerCAmelCase_  :							List[str]								=					train_model(_SCREAMING_SNAKE_CASE       ,     labels=_SCREAMING_SNAKE_CASE			)[0]
          loss.backward()
          return loss

        def compute_loss_and_backprob_encoder_decoder():
          lowerCAmelCase_  :							List[str]								=					train_model(_SCREAMING_SNAKE_CASE       ,     decoder_input_ids=_SCREAMING_SNAKE_CASE       ,     labels=_SCREAMING_SNAKE_CASE			)[0]
          loss.backward()
          return loss

        lowerCAmelCase_  :							Optional[int]								=					(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: Optional[int]			):





        try:
          if self.args.is_tpu or self.args.torchscript:
            # run additional 10 times to stabilize compilation for tpu and torchscript
            logger.info('Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'			)
            timeit.repeat(
                _SCREAMING_SNAKE_CASE       ,     repeat=1       ,     number=5       ,     )

          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
          lowerCAmelCase_  :							Dict								=					timeit.repeat(
              _SCREAMING_SNAKE_CASE       ,     repeat=self.args.repeat       ,     number=1_0       ,     )

          if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
            import torch_xla.debug.metrics as met

            self.print_fn(met.metrics_report()			)

          return min(_SCREAMING_SNAKE_CASE			) / 10.0
        except RuntimeError as e:
          self.print_fn(F"Doesn't fit on GPU. {e}"			)
          return "N/A"





      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: str			):





        try:
          if self.args.trace_memory_line_by_line:
            lowerCAmelCase_  :							Dict								=					start_memory_tracing('transformers'			)

          if self.args.is_tpu:
            # tpu
            raise NotImplementedError(
                'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'
                ' `--no-memory` or `args.memory=False`'			)
          elif self.args.is_gpu:
            if not is_pyanvml_available():
              logger.warning(
                  'py3nvml not installed, we won\'t log GPU memory usage. '
                  'Install py3nvml (pip install py3nvml) to log information about GPU.'			)
              lowerCAmelCase_  :							Union[str, Any]								=					'N/A'
            else:
              logger.info(
                  'Measuring total GPU usage on GPU device. Make sure to not have additional processes running'
                  ' on the same GPU.'			)
              # init nvml
              nvml.nvmlInit()
              func()
              lowerCAmelCase_  :							str								=					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
              lowerCAmelCase_  :							List[Any]								=					nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE			)
              lowerCAmelCase_  :							Optional[Any]								=					meminfo.used
              lowerCAmelCase_  :							int								=					Memory(_SCREAMING_SNAKE_CASE			)
              # shutdown nvml
              nvml.nvmlShutdown()
          else:
            # cpu
            lowerCAmelCase_  :							List[Any]								=					measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE			)
            lowerCAmelCase_  :							Dict								=					Memory(_SCREAMING_SNAKE_CASE			) if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE			) else memory_bytes

          if self.args.trace_memory_line_by_line:
            lowerCAmelCase_  :							Optional[int]								=					stop_memory_tracing(_SCREAMING_SNAKE_CASE			)
          else:
            lowerCAmelCase_  :							Tuple								=					None

          return memory, summary
        except RuntimeError as e:
          self.print_fn(F"Doesn't fit on GPU. {e}"			)
          return "N/A", None













"""simple docstring"""



import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

					lowerCamelCase__					=states.reshape(1      ,						-1      ,						model.config.state_dim      )
					lowerCamelCase__					=actions.reshape(1      ,						-1      ,						model.config.act_dim      )
					lowerCamelCase__					=returns_to_go.reshape(1      ,						-1      ,						1      )
					lowerCamelCase__					=timesteps.reshape(1      ,						-1      )

					if model.config.max_length is not None:
										lowerCamelCase__					=states[:, -model.config.max_length :]
										lowerCamelCase__					=actions[:, -model.config.max_length :]
										lowerCamelCase__					=returns_to_go[:, -model.config.max_length :]
										lowerCamelCase__					=timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										lowerCamelCase__					=torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
										lowerCamelCase__					=attention_mask.to(dtype=torch.long      ,						device=states.device      ).reshape(1      ,						-1      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)      ,						device=states.device      ,						),
										        states,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)      ,						device=actions.device      ,						),
										        actions,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)      ,						device=returns_to_go.device      ,						),
										        returns_to_go,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])      ,						device=timesteps.device      ),
										        timesteps,
										    ]      ,						dim=1      ,						).to(dtype=torch.long      )
					else:
										lowerCamelCase__					=None

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=model(
					    states=__A      ,						actions=__A      ,						rewards=__A      ,						returns_to_go=__A      ,						timesteps=__A      ,						attention_mask=__A      ,						return_dict=__A      ,						)

					return action_preds[0, -1]


# build the environment

_snake_case							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_snake_case							:   Tuple	  =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_snake_case							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_snake_case							:   List[Any]	  =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
_snake_case							:   List[str]	  =       model.to(device)
model.eval()

for ep in range(10):
						_snake_case  ,    _snake_case							:   List[str]	  =       0, 0
						_snake_case							:   Any	  =       env.reset()
						_snake_case							:   int	  =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_snake_case							:   Optional[Any]	  =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros(0, device=device, dtype=torch.floataa)

						_snake_case							:   Tuple	  =       torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
						for t in range(max_ep_len):
												env.render()
												# add padding
												_snake_case							:   List[Any]	  =       torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
												_snake_case							:   str	  =       torch.cat([rewards, torch.zeros(1, device=device)])

												_snake_case							:   List[str]	  =       get_action(
												    model,
												    (states.to(dtype=torch.floataa) - state_mean) / state_std,
												    actions.to(dtype=torch.floataa),
												    rewards.to(dtype=torch.floataa),
												    target_return.to(dtype=torch.floataa),
												    timesteps.to(dtype=torch.long),
												)
												_snake_case							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

												_snake_case  ,    _snake_case  ,    _snake_case  ,    _snake_case							:   List[str]	  =       env.step(action)

												_snake_case							:   Optional[int]	  =       torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												_snake_case							:   Optional[int]	  =       torch.cat([states, cur_state], dim=0)
												_snake_case							:   Tuple	  =       reward

												_snake_case							:   List[str]	  =       target_return[0, -1] - (reward / scale)
												_snake_case							:   Optional[int]	  =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_snake_case							:   Any	  =       torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

												episode_return += reward
												episode_length += 1

												if done:
																		break