

import argparse
import os
import sys
import urllib.request
import zipfile


_UpperCamelCase     =    ['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCamelCase     =    {
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

_UpperCamelCase     =    '''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCamelCase     =    '''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						int     ) ->				List[Any]:
 print(f"Downloading and extracting {task}..."     )
 UpperCAmelCase__           =       f"{task}.zip"
 urllib.request.urlretrieve(TASKaPATH[task]						,					snake_case__     )
 with zipfile.ZipFile(snake_case__     ) as zip_ref:
  zip_ref.extractall(snake_case__     )
 os.remove(snake_case__     )
 print('\tCompleted!'     )
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						int     ) ->				int:
 print('Processing MRPC...'     )
 UpperCAmelCase__           =       os.path.join(snake_case__						,					'MRPC'     )
 if not os.path.isdir(snake_case__     ):
  os.mkdir(snake_case__     )
 if path_to_data:
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_train.txt'     )
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_test.txt'     )
 else:
  print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN     )
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_train.txt'     )
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_test.txt'     )
  urllib.request.urlretrieve(snake_case__						,					snake_case__     )
  urllib.request.urlretrieve(snake_case__						,					snake_case__     )
 if not os.path.isfile(snake_case__     ):
  raise ValueError(f"Train data not found at {mrpc_train_file}"     )
 if not os.path.isfile(snake_case__     ):
  raise ValueError(f"Test data not found at {mrpc_test_file}"     )
 urllib.request.urlretrieve(TASKaPATH['MRPC']						,					os.path.join(snake_case__						,					'dev_ids.tsv'     )     )

 UpperCAmelCase__           =       []
 with open(os.path.join(snake_case__						,					'dev_ids.tsv'     )						,					encoding='utf8'     ) as ids_fh:
  for row in ids_fh:
   dev_ids.append(row.strip().split('\t'     )     )

 with open(snake_case__						,					encoding='utf8'     ) as data_fh, open(
     os.path.join(snake_case__						,					'train.tsv'     )						,					'w'						,					encoding='utf8'     ) as train_fh, open(os.path.join(snake_case__						,					'dev.tsv'     )						,					'w'						,					encoding='utf8'     ) as dev_fh:
  UpperCAmelCase__           =       data_fh.readline()
  train_fh.write(snake_case__     )
  dev_fh.write(snake_case__     )
  for row in data_fh:
   UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       row.strip().split('\t'     )
   if [ida, ida] in dev_ids:
    dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)     )
   else:
    train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)     )

 with open(snake_case__						,					encoding='utf8'     ) as data_fh, open(
     os.path.join(snake_case__						,					'test.tsv'     )						,					'w'						,					encoding='utf8'     ) as test_fh:
  UpperCAmelCase__           =       data_fh.readline()
  test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'     )
  for idx, row in enumerate(snake_case__     ):
   UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       row.strip().split('\t'     )
   test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)     )
 print('\tCompleted!'     )
def 		UpperCamelCase_(    snake_case__:						str     ) ->				Dict:
 print('Downloading and extracting diagnostic...'     )
 if not os.path.isdir(os.path.join(snake_case__						,					'diagnostic'     )     ):
  os.mkdir(os.path.join(snake_case__						,					'diagnostic'     )     )
 UpperCAmelCase__           =       os.path.join(snake_case__						,					'diagnostic'						,					'diagnostic.tsv'     )
 urllib.request.urlretrieve(TASKaPATH['diagnostic']						,					snake_case__     )
 print('\tCompleted!'     )
 return
def 		UpperCamelCase_(    snake_case__:						Optional[Any]     ) ->				Tuple:
 UpperCAmelCase__           =       task_names.split(','     )
 if "all" in task_names:
  UpperCAmelCase__           =       TASKS
 else:
  UpperCAmelCase__           =       []
  for task_name in task_names:
   if task_name not in TASKS:
    raise ValueError(f"Task {task_name} not found!"     )
   tasks.append(snake_case__     )
 return tasks
def 		UpperCamelCase_(    snake_case__:						Union[str, Any]     ) ->				int:
 UpperCAmelCase__           =       argparse.ArgumentParser()
 parser.add_argument('--data_dir'						,					help='directory to save data to'						,					type=snake_case__						,					default='glue_data'     )
 parser.add_argument(
     '--tasks'						,					help='tasks to download data for as a comma separated string'						,					type=snake_case__						,					default='all'     )
 parser.add_argument(
     '--path_to_mrpc'						,					help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'						,					type=snake_case__						,					default=''						,					)
 UpperCAmelCase__           =       parser.parse_args(snake_case__     )

 if not os.path.isdir(args.data_dir     ):
  os.mkdir(args.data_dir     )
 UpperCAmelCase__           =       get_tasks(args.tasks     )

 for task in tasks:
  if task == "MRPC":
   format_mrpc(args.data_dir						,					args.path_to_mrpc     )
  elif task == "diagnostic":
   download_diagnostic(args.data_dir     )
  else:
   download_and_extract(snake_case__						,					args.data_dir     )


if __name__ == "__main__":
 sys.exit(main(sys.argv[1:]))






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_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

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

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

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

 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       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

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase     =    np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_UpperCamelCase     =    np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_UpperCamelCase     =    torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase     =    torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
 _UpperCamelCase ,       _UpperCamelCase     =    0, 0
 _UpperCamelCase     =    env.reset()
 _UpperCamelCase     =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _UpperCamelCase     =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

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



