'''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 __a			(						__lowerCamelCase      :    str			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    List[Any]					)						->				Tuple:
      '''simple docstring'''


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

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

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

      lowercase_			,				lowercase_			,				lowercase_     			=   model(
          states=__lowerCamelCase			,		actions=__lowerCamelCase			,		rewards=__lowerCamelCase			,		returns_to_go=__lowerCamelCase			,		timesteps=__lowerCamelCase			,		attention_mask=__lowerCamelCase			,		return_dict=__lowerCamelCase			,		)

      return action_preds[0, -1]


# build the environment

lowerCAmelCase_			:			Any    	=   gym.make("Hopper-v3")
lowerCAmelCase_			:			List[Any]    	=   env.observation_space.shape[0]
lowerCAmelCase_			:			Any    	=   env.action_space.shape[0]
lowerCAmelCase_			:			Tuple    	=   1_000
lowerCAmelCase_			:			List[str]    	=   "cuda"
lowerCAmelCase_			:			int    	=   1000.0  # normalization for rewards/returns
lowerCAmelCase_			:			Optional[Any]    	=   3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCAmelCase_			:			Optional[Any]    	=   np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
lowerCAmelCase_			:			int    	=   np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
lowerCAmelCase_			:			Tuple    	=   torch.from_numpy(state_mean).to(device=device)
lowerCAmelCase_			:			Optional[Any]    	=   torch.from_numpy(state_std).to(device=device)

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

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

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

              lowerCAmelCase_			:			List[Any]    	=   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),
              )
              lowerCAmelCase_			:			int    	=   action
              lowerCAmelCase_			:			str    	=   action.detach().cpu().numpy()

              lowerCAmelCase_	,       lowerCAmelCase_	,       lowerCAmelCase_	,       lowerCAmelCase_			:			Any    	=   env.step(action)

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

              lowerCAmelCase_			:			Union[str, Any]    	=   target_return[0, -1] - (reward / scale)
              lowerCAmelCase_			:			Dict    	=   torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              lowerCAmelCase_			:			List[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







'''simple docstring'''

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def __a			(						__lowerCamelCase      :    Optional[Any]=None			,		__lowerCamelCase      :    List[str]=None					)						->				Any:
      '''simple docstring'''


      return field(default_factory=lambda: default			,		metadata=__lowerCamelCase					)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       metadata={'help': 'The csv file to plot.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Disable logarithmic scale when plotting'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}			,					)
   lowerCamelCase_            =list_field(
       default=__lowerCamelCase			,					metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}			)




def __a			(						__lowerCamelCase      :    int					)						->				Tuple:
      '''simple docstring'''


      try:
            int(__lowerCamelCase					)
            return True
      except ValueError:
            return False


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				str:
      '''simple docstring'''


      try:
            float(__lowerCamelCase					)
            return True
      except ValueError:
            return False





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   args
         lowercase_     			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

         with open(self.args.csv_file ,		newline="") as csv_file:
               lowercase_     			=   csv.DictReader(__lowerCAmelCase)
               for row in reader:
                     lowercase_     			=   row["model"]
                     self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
                     self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
                     if can_convert_to_int(row["result"]):
                           # value is not None
                           lowercase_     			=   int(row["result"])
                     elif can_convert_to_float(row["result"]):
                           # value is not None
                           lowercase_     			=   float(row["result"])


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         lowercase_			,				lowercase_     			=   plt.subplots()
         lowercase_     			=   "Time usage" if self.args.is_time else "Memory usage"
         lowercase_     			=   title_str + " for training" if self.args.is_train else title_str + " for inference"

         if not self.args.no_log_scale:
               # set logarithm scales
               ax.set_xscale("log")
               ax.set_yscale("log")

         for axis in [ax.xaxis, ax.yaxis]:
               axis.set_major_formatter(ScalarFormatter())

         for model_name_idx, model_name in enumerate(self.result_dict.keys()):
               lowercase_     			=   sorted(set(self.result_dict[model_name]["bsz"]))
               lowercase_     			=   sorted(set(self.result_dict[model_name]["seq_len"]))
               lowercase_     			=   self.result_dict[model_name]["result"]

               ((lowercase_)			,				(lowercase_))     			=   (
                   (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
               )

               lowercase_     			=   (
                   model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
               )

               for inner_loop_value in inner_loop_array:
                     if self.args.plot_along_batch:
                           lowercase_     			=   np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,		dtype=__lowerCAmelCase ,		)
                     else:
                           lowercase_     			=   np.asarray(
                               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,		dtype=np.floataa ,		)

                     ((lowercase_)			,				(lowercase_))     			=   (
                         ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                     )

                     lowercase_     			=   np.asarray(__lowerCAmelCase ,		__lowerCAmelCase)[: len(__lowerCAmelCase)]
                     plt.scatter(
                         __lowerCAmelCase ,		__lowerCAmelCase ,		label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}')
                     plt.plot(__lowerCAmelCase ,		__lowerCAmelCase ,		"--")

               title_str += F' {label_model_name} vs.'

         lowercase_     			=   title_str[:-4]
         lowercase_     			=   "Time in s" if self.args.is_time else "Memory in MB"

         # plot
         plt.title(__lowerCAmelCase)
         plt.xlabel(__lowerCAmelCase)
         plt.ylabel(__lowerCAmelCase)
         plt.legend()

         if self.args.figure_png_file is not None:
               plt.savefig(self.args.figure_png_file)
         else:
               plt.show()




def __a			(						)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser(__lowerCamelCase					)
      lowercase_     			=   parser.parse_args_into_dataclasses()[0]
      lowercase_     			=   Plot(args=__lowerCamelCase					)
      plot.plot()


if __name__ == "__main__":
       main()






