'''simple docstring'''
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @patch('sys.argv'  ,      ['fakeprogrampath', 'env']     )
     def lowerCAmelCase  (		self     ):
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
             transformers.commands.transformers_cli.main()
         self.assertIn('Python version'  ,      cs.out     )
         self.assertIn('Platform'  ,      cs.out     )
         self.assertIn('Using distributed or parallel set-up in script?'  ,      cs.out     )







     @is_pt_tf_cross_test
     @patch(
         'sys.argv'  ,      ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']     )
     def lowerCAmelCase  (		self     ):
         import transformers.commands.transformers_cli

         shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'  ,      ignore_errors=_lowerCamelCase     )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'     )     )







     @require_torch
     @patch('sys.argv'  ,      ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']     )
     def lowerCAmelCase  (		self     ):
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'  ,      ignore_errors=_lowerCamelCase     )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'     )     )
         self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'     )     )
         self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'     )     )







     @require_torch
     @patch(
         'sys.argv'  ,      [
             'fakeprogrampath',
             'download',
             'hf-internal-testing/test_dynamic_model_with_tokenizer',
             '--trust-remote-code',
             '--cache-dir',
             '/tmp',
         ]  ,      )
     def lowerCAmelCase  (		self     ):
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'  ,      ignore_errors=_lowerCamelCase     )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'     )     )
         self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'     )     )
         self.assertTrue(
             os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'     )     )




'''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



