






"""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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any=None       , snake_case    :      Optional[int]=None							)->						int:






     '''simple docstring'''



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




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




def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]							)->						Union[str, Any]:






     '''simple docstring'''



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


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						int:






     '''simple docstring'''



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




class        lowerCAmelCase__       :
   def __init__(						self	:     str				,       snake_case__	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				args
        UpperCAmelCase__		:  int				=				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

        with open(self.args.csv_file				,       newline=""				) as csv_file:
             UpperCAmelCase__		:  List[str]				=				csv.DictReader(snake_case__				)
             for row in reader:
                  UpperCAmelCase__		:  str				=				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
                       UpperCAmelCase__		:  str				=				int(row["result"]				)
                  elif can_convert_to_float(row["result"]				):
                       # value is not None
                       UpperCAmelCase__		:  Tuple				=				float(row["result"]				)




   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__				,							UpperCAmelCase__		:  Tuple				=				plt.subplots()
        UpperCAmelCase__		:  int				=				"Time usage" if self.args.is_time else "Memory usage"
        UpperCAmelCase__		:  List[str]				=				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()				):
             UpperCAmelCase__		:  Dict				=				sorted(set(self.result_dict[model_name]["bsz"]				)				)
             UpperCAmelCase__		:  List[str]				=				sorted(set(self.result_dict[model_name]["seq_len"]				)				)
             UpperCAmelCase__		:  Tuple				=				self.result_dict[model_name]["result"]

             ((UpperCAmelCase__)				,							(UpperCAmelCase__))		:  Any				=				(
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             UpperCAmelCase__		:  Dict				=				(
                 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:
                       UpperCAmelCase__		:  List[Any]				=				np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,       dtype=snake_case__				,       )
                  else:
                       UpperCAmelCase__		:  int				=				np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,       dtype=np.floataa				,       )

                  ((UpperCAmelCase__)				,							(UpperCAmelCase__))		:  List[Any]				=				(
                      ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                  )

                  UpperCAmelCase__		:  Optional[Any]				=				np.asarray(snake_case__				,       snake_case__				)[: len(snake_case__				)]
                  plt.scatter(
                      snake_case__				,       snake_case__				,       label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'				)
                  plt.plot(snake_case__				,       snake_case__				,       "--"				)

             title_str += f' {label_model_name} vs.'

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

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

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




def      SCREAMING_SNAKE_CASE__    (   )->						Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				HfArgumentParser(snake_case							)
     UpperCAmelCase__		:  Tuple				=				parser.parse_args_into_dataclasses()[0]
     UpperCAmelCase__		:  Tuple				=				Plot(args=snake_case							)
     plot.plot()


if __name__ == "__main__":
 main()










"""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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				states.reshape(1       , -1       , model.config.state_dim							)
     UpperCAmelCase__		:  str				=				actions.reshape(1       , -1       , model.config.act_dim							)
     UpperCAmelCase__		:  Any				=				returns_to_go.reshape(1       , -1       , 1							)
     UpperCAmelCase__		:  Union[str, Any]				=				timesteps.reshape(1       , -1							)

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

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

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				model(
         states=snake_case       , actions=snake_case       , rewards=snake_case       , returns_to_go=snake_case       , timesteps=snake_case       , attention_mask=snake_case       , return_dict=snake_case       , )

     return action_preds[0, -1]


# build the environment

_lowerCAmelCase     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

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

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

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

  _lowerCAmelCase     :     Tuple            =	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     :     List[str]            =	action
  _lowerCAmelCase     :     Optional[int]            =	action.detach().cpu().numpy()

  _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase     :     int            =	env.step(action)

  _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([states, cur_state], dim=0)
  _lowerCAmelCase     :     List[Any]            =	reward

  _lowerCAmelCase     :     List[str]            =	target_return[0, -1] - (reward / scale)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _lowerCAmelCase     :     Tuple            =	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


