'''simple docstring'''
import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil







@pytest.fixture
def 			_UpperCamelCase			(  _a						:						Tuple      ):




    """simple docstring"""



    __UpperCamelCase						:						List[Any]		     =	tmp_path / 'text.txt'
    __UpperCamelCase						:						Optional[int]		     =	textwrap.dedent(
        '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        '      )
    with open(_a					,  'w'					,  encoding='utf-8'      ) as f:
        f.write(_a      )
    return str(_a      )







@pytest.fixture
def 			_UpperCamelCase			(  _a						:						int					,  _a						:						int      ):




    """simple docstring"""



    __UpperCamelCase						:						Any		     =	tmp_path / 'text_with_image.txt'
    with open(_a					,  'w'					,  encoding='utf-8'      ) as f:
        f.write(_a      )
    return str(_a      )







@pytest.mark.parametrize('keep_linebreaks'					,  [True, False]      )
def 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						Dict      ):




    """simple docstring"""



    with open(_a					,  encoding='utf-8'      ) as f:
        __UpperCamelCase						:						Any		     =	f.read().splitlines(keepends=_a      )
    __UpperCamelCase						:						Dict		     =	Text(keep_linebreaks=_a					,  encoding='utf-8'      )
    __UpperCamelCase						:						Optional[int]		     =	text._generate_tables([[text_file]]      )
    __UpperCamelCase						:						str		     =	pa.concat_tables([table for _, table in generator]      ).to_pydict()['text']
    assert generated_content == expected_content







@require_pil
def 			_UpperCamelCase			(  _a						:						Any      ):




    """simple docstring"""



    with open(_a					,  encoding='utf-8'      ) as f:
        __UpperCamelCase						:						List[str]		     =	f.read().splitlines()[0]
    __UpperCamelCase						:						Optional[Any]		     =	Text(encoding='utf-8'					,  features=Features({'image': Image()}      )      )
    __UpperCamelCase						:						Union[str, Any]		     =	text._generate_tables([[text_file_with_image]]      )
    __UpperCamelCase						:						Any		     =	pa.concat_tables([table for _, table in generator]      )
    assert pa_table.schema.field('image'      ).type == Image()()
    __UpperCamelCase						:						List[str]		     =	pa_table.to_pydict()['image']
    assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize('sample_by'					,  ['line', 'paragraph', 'document']      )
def 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						str      ):




    """simple docstring"""



    with open(_a					,  encoding='utf-8'      ) as f:
        __UpperCamelCase						:						Any		     =	f.read()
    if sample_by == "line":
        __UpperCamelCase						:						Any		     =	expected_content.splitlines()
    elif sample_by == "paragraph":
        __UpperCamelCase						:						str		     =	expected_content.split('\n\n'      )
    elif sample_by == "document":
        __UpperCamelCase						:						Optional[int]		     =	[expected_content]
    __UpperCamelCase						:						str		     =	Text(sample_by=_a					,  encoding='utf-8'					,  chunksize=1_0_0      )
    __UpperCamelCase						:						int		     =	text._generate_tables([[text_file]]      )
    __UpperCamelCase						:						Union[str, Any]		     =	pa.concat_tables([table for _, table in generator]      ).to_pydict()['text']
    assert generated_content == expected_content




'''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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



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

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

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

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	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

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	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,
    ]
)
a=	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,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
  a,      a=	0, 0
  a=	env.reset()
  a=	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  a=	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  a=	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  a=	torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

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



