



import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import DecisionTransformerModel
	from transformers.models.decision_transformer.modeling_decision_transformer import (
	    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)





class 					__lowerCAmelCase :




				def __init__(							self						,				_snake_case						,				_snake_case=13						,				_snake_case=7						,				_snake_case=6						,				_snake_case=17						,				_snake_case=23						,				_snake_case=11						,				_snake_case=True						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    parent
									_lowerCAmelCase						=    batch_size
									_lowerCAmelCase						=    seq_length
									_lowerCAmelCase						=    act_dim
									_lowerCAmelCase						=    state_dim
									_lowerCAmelCase						=    hidden_size
									_lowerCAmelCase						=    max_length
									_lowerCAmelCase						=    is_training




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
									_lowerCAmelCase						=    floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
									_lowerCAmelCase						=    floats_tensor((self.batch_size, self.seq_length, 1)		)
									_lowerCAmelCase						=    floats_tensor((self.batch_size, self.seq_length, 1)		)
									_lowerCAmelCase						=    ids_tensor((self.batch_size, self.seq_length)						,				vocab_size=1000		)
									_lowerCAmelCase						=    random_attention_mask((self.batch_size, self.seq_length)		)

									_lowerCAmelCase						=    self.get_config()

									return (
									    config,
									    states,
									    actions,
									    rewards,
									    returns_to_go,
									    timesteps,
									    attention_mask,
									)




				def 							snake_case   (							self		):



									"""simple docstring"""





									return DecisionTransformerConfig(
									    batch_size=self.batch_size						,				seq_length=self.seq_length						,				act_dim=self.act_dim						,				state_dim=self.state_dim						,				hidden_size=self.hidden_size						,				max_length=self.max_length						,				)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    DecisionTransformerModel(config=lowercase_		)
									model.to(lowercase_		)
									model.eval()
									_lowerCAmelCase						=    model(lowercase_						,				lowercase_						,				lowercase_						,				lowercase_						,				lowercase_						,				lowercase_		)

									self.parent.assertEqual(result.state_preds.shape						,				states.shape		)
									self.parent.assertEqual(result.action_preds.shape						,				actions.shape		)
									self.parent.assertEqual(result.return_preds.shape						,				returns_to_go.shape		)
									self.parent.assertEqual(
									    result.last_hidden_state.shape						,				(self.batch_size, self.seq_length * 3, self.hidden_size)		)  # seq length *3 as there are 3 modelities: states, returns and actions




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									(
									    (
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	(
									    _lowerCAmelCase      
									)	,	      
									)						=    config_and_inputs
									_lowerCAmelCase						=    {
									    """states""": states,
									    """actions""": actions,
									    """rewards""": rewards,
									    """returns_to_go""": returns_to_go,
									    """timesteps""": timesteps,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





@require_torch
class 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				(DecisionTransformerModel,) if is_torch_available() else ()
				__lowerCamelCase =				()
				__lowerCamelCase =				{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

				# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
				__lowerCamelCase =				False

				# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    DecisionTransformerModelTester(self		)
									_lowerCAmelCase						=    ConfigTester(self						,				config_class=lowercase_						,				hidden_size=37		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_		)




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase						=    DecisionTransformerModel.from_pretrained(lowercase_		)
														self.assertIsNotNone(lowercase_		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase						=    model_class(lowercase_		)
														_lowerCAmelCase						=    inspect.signature(model.forward		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_lowerCAmelCase						=    [*signature.parameters.keys()]

														_lowerCAmelCase						=    [
														    """states""",
														    """actions""",
														    """rewards""",
														    """returns_to_go""",
														    """timesteps""",
														    """attention_mask""",
														]

														self.assertListEqual(arg_names[: len(lowercase_		)]						,				lowercase_		)





@require_torch
class 					__lowerCAmelCase (     unittest.TestCase       ):




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    2  # number of steps of autoregressive prediction we will perform
									_lowerCAmelCase						=    10  # defined by the RL environment, may be normalized
									_lowerCAmelCase						=    DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""		)
									_lowerCAmelCase						=    model.to(lowercase_		)
									_lowerCAmelCase						=    model.config
									torch.manual_seed(0		)
									_lowerCAmelCase						=    torch.randn(1						,				1						,				config.state_dim		).to(device=lowercase_						,				dtype=torch.floataa		)  # env.reset()

									_lowerCAmelCase						=    torch.tensor(
									    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]						,				device=lowercase_		)

									_lowerCAmelCase						=    torch.tensor(lowercase_						,				device=lowercase_						,				dtype=torch.floataa		).reshape(1						,				1						,				1		)
									_lowerCAmelCase						=    state
									_lowerCAmelCase						=    torch.zeros(1						,				0						,				config.act_dim						,				device=lowercase_						,				dtype=torch.floataa		)
									_lowerCAmelCase						=    torch.zeros(1						,				0						,				device=lowercase_						,				dtype=torch.floataa		)
									_lowerCAmelCase						=    torch.tensor(0						,				device=lowercase_						,				dtype=torch.long		).reshape(1						,				1		)

									for step in range(lowercase_		):
														_lowerCAmelCase						=    torch.cat([actions, torch.zeros(1						,				1						,				config.act_dim						,				device=lowercase_		)]						,				dim=1		)
														_lowerCAmelCase						=    torch.cat([rewards, torch.zeros(1						,				1						,				device=lowercase_		)]						,				dim=1		)

														_lowerCAmelCase						=    torch.ones(1						,				states.shape[1]		).to(dtype=torch.long						,				device=states.device		)

														with torch.no_grad():
																			_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    model(
																			    states=lowercase_						,				actions=lowercase_						,				rewards=lowercase_						,				returns_to_go=lowercase_						,				timesteps=lowercase_						,				attention_mask=lowercase_						,				return_dict=lowercase_						,				)

														self.assertEqual(action_pred.shape						,				actions.shape		)
														self.assertTrue(torch.allclose(action_pred[0, -1]						,				expected_outputs[step]						,				atol=1e-4		)		)
														_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    (  # env.step(action)
														    torch.randn(1						,				1						,				config.state_dim		).to(device=lowercase_						,				dtype=torch.floataa		),
														    1.0,
														    False,
														    {},
														)

														_lowerCAmelCase						=    action_pred[0, -1]
														_lowerCAmelCase						=    torch.cat([states, state]						,				dim=1		)
														_lowerCAmelCase						=    returns_to_go[0, -1] - reward
														_lowerCAmelCase						=    torch.cat([returns_to_go, pred_return.reshape(1						,				1						,				1		)]						,				dim=1		)
														_lowerCAmelCase						=    torch.cat(
														    [timesteps, torch.ones((1, 1)						,				device=lowercase_						,				dtype=torch.long		) * (step + 1)]						,				dim=1		)






def 			UpperCAmelCase__				(			_A      : dict							):





  '''simple docstring'''







  a__  =set()
  # To detect a back edge, keep track of vertices currently in the recursion stack
  a__  =set()
  return any(
      node not in visited and depth_first_search(_A	,      _A	,      _A	,      _A							)
      for node in graph							)




def 			UpperCAmelCase__				(			_A      : dict	,      _A      : int	,      _A      : set	,      _A      : set							):





  '''simple docstring'''







  visited.add(_A							)
  rec_stk.add(_A							)

  for node in graph[vertex]:
    if node not in visited:
      if depth_first_search(_A	,      _A	,      _A	,      _A							):
        return True
    elif node in rec_stk:
      return True

    # The node needs to be removed from recursion stack before function ends
  rec_stk.remove(_A							)
  return False


if __name__ == "__main__":
       from doctest import testmod

       testmod()
