



"""simple docstring"""







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  (					UpperCamelCase__				):





     """simple docstring"""






     def 						A__  (					self :							Any	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							Dict=False ):
          A__				=   (
              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(lowerCAmelCase__ )	,      '''wb''' ).close()
          A__				=   pa.Table.from_pydict({'''filename''': [Path(lowerCAmelCase__ ).name] * 1_0_0} )
          if take == -1:
               A__				=   len(lowerCAmelCase__ ) - skip
          if skip is not None and take is not None:
               A__				=   pa_table.slice(lowerCAmelCase__	,      lowerCAmelCase__ )
          return pa_table


class       UpperCamelCase  (					UpperCamelCase__				):





     """simple docstring"""






     def 						A__  (					self :							Tuple ):
          A__				=   "my_name"
          A__				=   SplitInfo(name='''train'''	,      num_examples=1_0_0 )
          A__				=   SplitInfo(name='''test'''	,      num_examples=1_0_0 )
          A__				=   [train_info, test_info]
          A__				=   SplitDict()
          split_dict.add(lowerCAmelCase__ )
          split_dict.add(lowerCAmelCase__ )
          A__				=   DatasetInfo(splits=lowerCAmelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
               A__				=   ReaderTest(lowerCAmelCase__	,      lowerCAmelCase__ )

               A__				=   "test[:33%]"
               A__				=   Dataset(**reader.read(lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__ ) )
               self.assertEqual(dset['''filename'''][0]	,      F'''{name}-test''' )
               self.assertEqual(dset.num_rows	,      3_3 )
               self.assertEqual(dset.num_columns	,      1 )

               A__				=   ["train", "test[:33%]"]
               A__				=   [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
               for instructions in [instructionsa, instructionsa]:
                    A__				=   [reader.read(lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__ ) for instr in instructions]
                    A__				=   (Dataset(**lowerCAmelCase__ ) for dataset_kwargs in datasets_kwargs)
                    self.assertEqual(train_dset['''filename'''][0]	,      F'''{name}-train''' )
                    self.assertEqual(train_dset.num_rows	,      1_0_0 )
                    self.assertEqual(train_dset.num_columns	,      1 )
                    self.assertIsInstance(train_dset.split	,      lowerCAmelCase__ )
                    self.assertEqual(str(train_dset.split )	,      '''train''' )
                    self.assertEqual(test_dset['''filename'''][0]	,      F'''{name}-test''' )
                    self.assertEqual(test_dset.num_rows	,      3_3 )
                    self.assertEqual(test_dset.num_columns	,      1 )
                    self.assertIsInstance(test_dset.split	,      lowerCAmelCase__ )
                    self.assertEqual(str(test_dset.split )	,      '''test[:33%]''' )
                    del train_dset, test_dset





     def 						A__  (					self :							Any ):
          A__				=   "my_name"
          A__				=   SplitInfo(name='''train'''	,      num_examples=1_0_0_0	,      shard_lengths=[1_0_0] * 1_0 )
          A__				=   [train_info]
          A__				=   SplitDict()
          split_dict.add(lowerCAmelCase__ )
          A__				=   DatasetInfo(splits=lowerCAmelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
               A__				=   ReaderTest(lowerCAmelCase__	,      lowerCAmelCase__ )

               A__				=   "train[:33%]"
               A__				=   Dataset(**reader.read(lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__ ) )
               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_3_0 )
               self.assertEqual(dset.num_columns	,      1 )





     def 						A__  (					self :							str ):
          A__				=   SplitInfo(name='''train'''	,      num_examples=1_0_0 )
          A__				=   SplitInfo(name='''test'''	,      num_examples=1_0_0 )
          A__				=   SplitDict()
          split_dict.add(lowerCAmelCase__ )
          split_dict.add(lowerCAmelCase__ )
          A__				=   DatasetInfo(splits=lowerCAmelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
               A__				=   ReaderTest(lowerCAmelCase__	,      lowerCAmelCase__ )

               A__				=   [
                   {"filename": os.path.join(lowerCAmelCase__	,      '''train''' )},
                   {"filename": os.path.join(lowerCAmelCase__	,      '''test''' ), "skip": 1_0, "take": 1_0},
               ]
               A__				=   Dataset(**reader.read_files(lowerCAmelCase__	,      original_instructions='''train+test[10:20]''' ) )
               self.assertEqual(dset.num_rows	,      1_1_0 )
               self.assertEqual(dset.num_columns	,      1 )
               del dset



@pytest.mark.parametrize('''in_memory'''		,							[False, True]      )
def   a_	(			__a		,							__a		,							__a      ):
     A__				=   arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          A__				=   ArrowReader.read_table(__a		,							in_memory=__a      )
     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   a_	(			__a		,							__a		,							__a      ):
     A__				=   arrow_file
     A__				=   ArrowReader(''''''		,							__a      )
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          A__				=   reader.read_files([{'''filename''': filename}]		,							in_memory=__a      )
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     A__				=   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   a_	(			):
     assert ReadInstruction('''train'''		,							to=10		,							unit='''abs'''      ).to_spec() == "train[:10]"
     assert ReadInstruction('''train'''		,							from_=-80		,							to=10		,							unit='''%'''      ).to_spec() == "train[-80%:10%]"

     A__				=   "train+test"
     assert ReadInstruction.from_spec(__a      ).to_spec() == spec_train_test

     A__				=   "train[2:10]"
     assert ReadInstruction.from_spec(__a      ).to_spec() == spec_train_abs

     A__				=   "train[15%:-20%]"
     assert ReadInstruction.from_spec(__a      ).to_spec() == spec_train_pct

     A__				=   "train[:10%](closest)"
     assert ReadInstruction.from_spec(__a      ).to_spec() == "train[:10%]"

     A__				=   "train[:10%](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__a      ).to_spec() == spec_train_pct_rounding

     A__				=   "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__a      ).to_spec() == spec_train_test_pct_rounding



def   a_	(			):
     A__				=   "dummy"
     A__				=   [SplitInfo(name='''train'''		,							num_examples=100      )]
     A__				=   "train[:33%]"
     A__				=   "arrow"
     A__				=   "prefix"

     A__				=   make_file_instructions(__a		,							__a		,							__a		,							__a		,							__a      )
     assert isinstance(__a		,							__a      )
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__a		,							f'''{name}-train.arrow'''      ), "skip": 0, "take": 33}
     ]

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



@pytest.mark.parametrize('''name, expected_exception'''		,							[(None, TypeError), ('''''', ValueError)]      )
def   a_	(			__a		,							__a      ):
     A__				=   [SplitInfo(name='''train'''		,							num_examples=100      )]
     A__				=   "train"
     A__				=   "arrow"
     A__				=   "prefix_path"
     with pytest.raises(__a      ):
          A__				=   make_file_instructions(__a		,							__a		,							__a		,							__a		,							__a      )








"""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_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

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

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

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

     A__	,     A__	,     A__				=   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

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			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,
    ]
)
__snake_case	:				str  					=			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,
    ]
)
__snake_case	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

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

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

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

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

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

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

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



