'''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


a=	logging.get_logger(__name__)






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    "PyTorch"







     @property
     def lowerCAmelCase  (		self     ):
         return torch.__version__







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Dict		     =	self._prepare_inference_func(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )
         return self._measure_speed(_inference     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[int]		     =	self._prepare_inference_func(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )
         return self._measure_memory(_inference     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[Any]		     =	self._prepare_train_func(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )
         return self._measure_speed(_train     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Any		     =	self._prepare_train_func(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )
         return self._measure_memory(_train     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.config_dict[model_name]

         if self.args.torchscript:
             __UpperCamelCase						:						List[str]		     =	True

         __UpperCamelCase						:						Optional[Any]		     =	(
             hasattr(_lowerCamelCase  ,      'architectures'     )
             and isinstance(config.architectures  ,      _lowerCamelCase     )
             and len(config.architectures     ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 __UpperCamelCase						:						int		     =	config.architectures[0]
                 __UpperCamelCase						:						Optional[Any]		     =	__import__('transformers'  ,      fromlist=[model_class]     )
                 __UpperCamelCase						:						Optional[int]		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						List[Any]		     =	model_cls(_lowerCamelCase     )
             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:
             __UpperCamelCase						:						Optional[int]		     =	MODEL_MAPPING[config.__class__](_lowerCamelCase     )

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

         # encoder-decoder has vocab size saved differently
         __UpperCamelCase						:						Tuple		     =	config.vocab_size if hasattr(_lowerCamelCase  ,      'vocab_size'     ) else config.encoder.vocab_size
         __UpperCamelCase						:						str		     =	torch.randint(_lowerCamelCase  ,      (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():
                 __UpperCamelCase						:						List[str]		     =	torch.jit.trace(_lowerCamelCase  ,      _lowerCamelCase     )
         else:
             __UpperCamelCase						:						Dict		     =	model

         def encoder_decoder_forward():
             with torch.no_grad():
                 __UpperCamelCase						:						Any		     =	inference_model(_lowerCamelCase  ,      decoder_input_ids=_lowerCamelCase     )
             return outputs

         def encoder_forward():
             with torch.no_grad():
                 __UpperCamelCase						:						Dict		     =	inference_model(_lowerCamelCase     )
             return outputs

         __UpperCamelCase						:						Optional[Any]		     =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
         return _forward







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Dict		     =	self.config_dict[model_name]

         __UpperCamelCase						:						Tuple		     =	(
             hasattr(_lowerCamelCase  ,      'architectures'     )
             and isinstance(config.architectures  ,      _lowerCamelCase     )
             and len(config.architectures     ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 __UpperCamelCase						:						Union[str, Any]		     =	config.architectures[0]
                 __UpperCamelCase						:						Union[str, Any]		     =	__import__('transformers'  ,      fromlist=[model_class]     )
                 __UpperCamelCase						:						Union[str, Any]		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						Any		     =	model_cls(_lowerCamelCase     )
             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:
             __UpperCamelCase						:						Union[str, Any]		     =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCamelCase     )

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

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

         # encoder-decoder has vocab size saved differently
         __UpperCamelCase						:						List[Any]		     =	config.vocab_size if hasattr(_lowerCamelCase  ,      'vocab_size'     ) else config.encoder.vocab_size
         __UpperCamelCase						:						Tuple		     =	torch.randint(_lowerCamelCase  ,      (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():
             __UpperCamelCase						:						Optional[int]		     =	train_model(_lowerCamelCase  ,      labels=_lowerCamelCase     )[0]
             loss.backward()
             return loss

         def compute_loss_and_backprob_encoder_decoder():
             __UpperCamelCase						:						Any		     =	train_model(_lowerCamelCase  ,      decoder_input_ids=_lowerCamelCase  ,      labels=_lowerCamelCase     )[0]
             loss.backward()
             return loss

         __UpperCamelCase						:						List[str]		     =	(
             compute_loss_and_backprob_encoder_decoder
             if config.is_encoder_decoder
             else compute_loss_and_backprob_encoder
         )
         return _train







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         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(
                     _lowerCamelCase  ,      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
             __UpperCamelCase						:						Dict		     =	timeit.repeat(
                 _lowerCamelCase  ,      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(_lowerCamelCase     ) / 1_0.0
         except RuntimeError as e:
             self.print_fn(f"""Doesn't fit on GPU. {e}"""     )
             return "N/A"







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         try:
             if self.args.trace_memory_line_by_line:
                 __UpperCamelCase						:						List[Any]		     =	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.'     )
                     __UpperCamelCase						:						List[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()
                     __UpperCamelCase						:						str		     =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
                     __UpperCamelCase						:						Dict		     =	nvml.nvmlDeviceGetMemoryInfo(_lowerCamelCase     )
                     __UpperCamelCase						:						Dict		     =	meminfo.used
                     __UpperCamelCase						:						Any		     =	Memory(_lowerCamelCase     )
                     # shutdown nvml
                     nvml.nvmlShutdown()
             else:
                 # cpu
                 __UpperCamelCase						:						Any		     =	measure_peak_memory_cpu(_lowerCamelCase     )
                 __UpperCamelCase						:						List[str]		     =	Memory(_lowerCamelCase     ) if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ) else memory_bytes

             if self.args.trace_memory_line_by_line:
                 __UpperCamelCase						:						Optional[int]		     =	stop_memory_tracing(_lowerCamelCase     )
             else:
                 __UpperCamelCase						:						List[Any]		     =	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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	states.reshape(1					,  -1					,  model.config.state_dim      )
    __UpperCamelCase						:						Tuple		     =	actions.reshape(1					,  -1					,  model.config.act_dim      )
    __UpperCamelCase						:						Optional[Any]		     =	returns_to_go.reshape(1					,  -1					,  1      )
    __UpperCamelCase						:						Optional[int]		     =	timesteps.reshape(1					,  -1      )

    if model.config.max_length is not None:
        __UpperCamelCase						:						Any		     =	states[:, -model.config.max_length :]
        __UpperCamelCase						:						int		     =	actions[:, -model.config.max_length :]
        __UpperCamelCase						:						Any		     =	returns_to_go[:, -model.config.max_length :]
        __UpperCamelCase						:						Tuple		     =	timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        __UpperCamelCase						:						str		     =	torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
        __UpperCamelCase						:						int		     =	attention_mask.to(dtype=torch.long					,  device=states.device      ).reshape(1					,  -1      )
        __UpperCamelCase						:						Any		     =	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      )
        __UpperCamelCase						:						Tuple		     =	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      )
        __UpperCamelCase						:						Optional[Any]		     =	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      )
        __UpperCamelCase						:						Tuple		     =	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:
        __UpperCamelCase						:						List[str]		     =	None

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	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

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a=	np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
a=	DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
a=	model.to(device)
model.eval()

for ep in range(1_0):
  a,      a=	0, 0
  a=	env.reset()
  a=	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  a=	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  a=	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  a=	torch.zeros(0, device=device, dtype=torch.floataa)

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

    a=	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),
    )
    a=	action
    a=	action.detach().cpu().numpy()

    a,      a,      a,      a=	env.step(action)

    a=	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    a=	torch.cat([states, cur_state], dim=0)
    a=	reward

    a=	target_return[0, -1] - (reward / scale)
    a=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a=	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



