




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				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		str:





  """simple docstring"""





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

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

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

  _lowercase     ,					_lowercase     ,					_lowercase: int						     =    model(
      states=UpperCamelCase					,     actions=UpperCamelCase					,     rewards=UpperCamelCase					,     returns_to_go=UpperCamelCase					,     timesteps=UpperCamelCase					,     attention_mask=UpperCamelCase					,     return_dict=UpperCamelCase					,     )

  return action_preds[0, -1]


# build the environment

UpperCAmelCase__						:       int							=    gym.make('Hopper-v3')
UpperCAmelCase__						:       str							=    env.observation_space.shape[0]
UpperCAmelCase__						:       Optional[Any]							=    env.action_space.shape[0]
UpperCAmelCase__						:       Union[str, Any]							=    1000
UpperCAmelCase__						:       Tuple							=    'cuda'
UpperCAmelCase__						:       str							=    1000.0  # normalization for rewards/returns
UpperCAmelCase__						:       Optional[Any]							=    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase__						:       List[str]							=    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,
    ]
)
UpperCAmelCase__						:       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,
    ]
)
UpperCAmelCase__						:       Optional[int]							=    torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase__						:       Optional[int]							=    torch.from_numpy(state_std).to(device=device)

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

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

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

      UpperCAmelCase__						:       Dict							=    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__						:       Dict							=    action
      UpperCAmelCase__						:       List[str]							=    action.detach().cpu().numpy()

      UpperCAmelCase__		,     UpperCAmelCase__		,     UpperCAmelCase__		,     UpperCAmelCase__						:       Tuple							=    env.step(action)

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

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






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]=False     )->			List[str]:
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    (
         filename_skip_take['filename'],
         filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
         filename_skip_take['take'] if 'take' in filename_skip_take else None,
     )
     open(os.path.join(UpperCamelCase_     )						,					'wb'     ).close()
     _lowercase: int						     =    pa.Table.from_pydict({'filename': [Path(UpperCamelCase_     ).name] * 1_00}     )
     if take == -1:
       _lowercase: int						     =    len(UpperCamelCase_     ) - skip
     if skip is not None and take is not None:
       _lowercase: Any						     =    pa_table.slice(UpperCamelCase_						,					UpperCamelCase_     )
     return pa_table

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    'my_name'
     _lowercase: Optional[int]						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: Dict						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: Any						     =    [train_info, test_info]
     _lowercase: Any						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Tuple						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: List[str]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    'test[:33%]'
       _lowercase: Optional[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-test'     )
       self.assertEqual(dset.num_rows						,					33     )
       self.assertEqual(dset.num_columns						,					1     )

       _lowercase: Tuple						     =    ['train', 'test[:33%]']
       _lowercase: Union[str, Any]						     =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'     )]
       for instructions in [instructionsa, instructionsa]:
         _lowercase: int						     =    [reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ) for instr in instructions]
         _lowercase     ,					_lowercase: Dict						     =    (Dataset(**UpperCamelCase_     ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0]						,					f'{name}-train'     )
         self.assertEqual(train_dset.num_rows						,					1_00     )
         self.assertEqual(train_dset.num_columns						,					1     )
         self.assertIsInstance(train_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(train_dset.split     )						,					'train'     )
         self.assertEqual(test_dset['filename'][0]						,					f'{name}-test'     )
         self.assertEqual(test_dset.num_rows						,					33     )
         self.assertEqual(test_dset.num_columns						,					1     )
         self.assertIsInstance(test_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(test_dset.split     )						,					'test[:33%]'     )
         del train_dset, test_dset






   def  _a						(	self   :       Any     )->			List[str]:
     _lowercase: int						     =    'my_name'
     _lowercase: List[str]						     =    SplitInfo(name='train'						,					num_examples=10_00						,					shard_lengths=[1_00] * 10     )
     _lowercase: Optional[Any]						     =    [train_info]
     _lowercase: Optional[int]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Optional[int]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    'train[:33%]'
       _lowercase: List[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-train-00000-of-00010'     )
       self.assertEqual(dset['filename'][-1]						,					f'{name}-train-00003-of-00010'     )
       self.assertEqual(dset.num_rows						,					3_30     )
       self.assertEqual(dset.num_columns						,					1     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     _lowercase: Any						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Dict						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    [
           {'filename': os.path.join(UpperCamelCase_						,					'train'     )},
           {'filename': os.path.join(UpperCamelCase_						,					'test'     ), 'skip': 10, 'take': 10},
       ]
       _lowercase: Tuple						     =    Dataset(**reader.read_files(UpperCamelCase_						,					original_instructions='train+test[10:20]'     )     )
       self.assertEqual(dset.num_rows						,					1_10     )
       self.assertEqual(dset.num_columns						,					1     )
       del dset


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: List[Any]						     =    ArrowReader.read_table(UpperCamelCase					,     in_memory=UpperCamelCase							)
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Any						     =    arrow_file
  _lowercase: Union[str, Any]						     =    ArrowReader(''					,     UpperCamelCase							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: Tuple						     =    reader.read_files([{'filename': filename}]					,     in_memory=UpperCamelCase							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _lowercase: Any						     =    dataset_kwargs['arrow_table']
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


def 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  assert ReadInstruction('train'					,     to=10					,     unit='abs'							).to_spec() == "train[:10]"
  assert ReadInstruction('train'					,     from_=-80					,     to=10					,     unit='%'							).to_spec() == "train[-80%:10%]"

  _lowercase: List[str]						     =    'train+test'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test

  _lowercase: Union[str, Any]						     =    'train[2:10]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_abs

  _lowercase: Any						     =    'train[15%:-20%]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct

  _lowercase: List[Any]						     =    'train[:10%](closest)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == "train[:10%]"

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct_rounding

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test_pct_rounding


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





  """simple docstring"""





  _lowercase: int						     =    'dummy'
  _lowercase: Any						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: Any						     =    'train[:33%]'
  _lowercase: Optional[int]						     =    'arrow'
  _lowercase: Optional[int]						     =    'prefix'

  _lowercase: Optional[int]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train.arrow'							), "skip": 0, "take": 33}
  ]

  _lowercase: int						     =    [SplitInfo(name='train'					,     num_examples=100					,     shard_lengths=[10] * 10							)]
  _lowercase: Dict						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00000-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00001-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00002-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00003-of-00010.arrow'							), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize('name, expected_exception'					,     [(None, TypeError), ('', ValueError)]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: List[Any]						     =    'train'
  _lowercase: List[str]						     =    'arrow'
  _lowercase: Optional[Any]						     =    'prefix_path'
  with pytest.raises(UpperCamelCase							):
    _lowercase: List[str]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
