




'''simple docstring'''




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_      :







  '''simple docstring'''







  def __init__(  self			:		Optional[int]  ,						_UpperCAmelCase			:		Union[str, Any]  ,						_UpperCAmelCase			:		str=13  ,						_UpperCAmelCase			:		str=7  ,						_UpperCAmelCase			:		List[str]=6  ,						_UpperCAmelCase			:		List[Any]=17  ,						_UpperCAmelCase			:		Any=23  ,						_UpperCAmelCase			:		str=11  ,						_UpperCAmelCase			:		List[str]=True  ,						):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Optional[int]       ):






   """simple docstring"""






   UpperCAmelCase__								=			floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
   UpperCAmelCase__								=			floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
   UpperCAmelCase__								=			floats_tensor((self.batch_size, self.seq_length, 1)       )
   UpperCAmelCase__								=			floats_tensor((self.batch_size, self.seq_length, 1)       )
   UpperCAmelCase__								=			ids_tensor((self.batch_size, self.seq_length)  ,						vocab_size=10_00       )
   UpperCAmelCase__								=			random_attention_mask((self.batch_size, self.seq_length)       )

   UpperCAmelCase__								=			self.get_config()

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


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Union[str, Any]       ):






   """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 							SCREAMING_SNAKE_CASE__	(  self			:		Union[str, Any]  ,						_UpperCAmelCase			:		Any  ,						_UpperCAmelCase			:		List[str]  ,						_UpperCAmelCase			:		Dict  ,						_UpperCAmelCase			:		Union[str, Any]  ,						_UpperCAmelCase			:		List[Any]  ,						_UpperCAmelCase			:		Optional[int]  ,						_UpperCAmelCase			:		List[Any]  ,						):






   """simple docstring"""






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

   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 							SCREAMING_SNAKE_CASE__	(  self			:		Optional[Any]       ):






   """simple docstring"""






   UpperCAmelCase__								=			self.prepare_config_and_inputs()
   (
       UpperCAmelCase__			
   )								=			config_and_inputs
   UpperCAmelCase__								=			{
       "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      ):







  '''simple docstring'''







  lowerCAmelCase_ :    Any    =  (DecisionTransformerModel,) if is_torch_available() else ()
  lowerCAmelCase_ :    List[Any]    =  ()
  lowerCAmelCase_ :    List[str]    =  {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

  # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
  lowerCAmelCase_ :    int    =  False
  lowerCAmelCase_ :    Dict    =  False
  lowerCAmelCase_ :    int    =  False
  lowerCAmelCase_ :    Dict    =  False
  lowerCAmelCase_ :    Union[str, Any]    =  False
  lowerCAmelCase_ :    Tuple    =  False
  lowerCAmelCase_ :    Union[str, Any]    =  False
  lowerCAmelCase_ :    str    =  False
  lowerCAmelCase_ :    Any    =  False


  def 							SCREAMING_SNAKE_CASE__	(  self			:		List[Any]       ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Tuple       ):






   """simple docstring"""






   self.config_tester.run_common_tests()


  def 							SCREAMING_SNAKE_CASE__	(  self			:		int       ):






   """simple docstring"""






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


  @slow
  def 							SCREAMING_SNAKE_CASE__	(  self			:		List[str]       ):






   """simple docstring"""






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



  def 							SCREAMING_SNAKE_CASE__	(  self			:		Dict       ):






   """simple docstring"""






   UpperCAmelCase__								=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

@require_torch
class   lowerCAmelCase_      (       unittest.TestCase      ):







  '''simple docstring'''







  @slow
  def 							SCREAMING_SNAKE_CASE__	(  self			:		Any       ):






   """simple docstring"""






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

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

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

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

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

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

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

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






import numpy

# List of input, output pairs
_UpperCAmelCase		:  List[str]	=   (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_UpperCAmelCase		:  Optional[Any]	=   (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase		:  Tuple	=   [2, 4, 1, 5]
_UpperCAmelCase		:  Union[str, Any]	=   len(train_data)
_UpperCAmelCase		:  Dict	=   0.0_0_9



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase="train"      ):
  return calculate_hypothesis_value(lowerCamelCase,	lowerCamelCase      ) - output(
      lowerCamelCase,	lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:str							=  0
  for i in range(len(lowerCamelCase      ) - 1      ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]      )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]      )
  return None



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase=m      ):
  lowercase			:Union[str, Any]							=  0
  for i in range(lowerCamelCase      ):
    if index == -1:
      summation_value += _error(lowerCamelCase      )
    else:
      summation_value += _error(lowerCamelCase      ) * train_data[i][0][index]
  return summation_value



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  summation_of_cost_derivative(lowerCamelCase,	lowerCamelCase      ) / m
  return cost_derivative_value



def 					UpperCAmelCase__     (   ):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  lowercase			:str							=  0.000_002
  lowercase			:Tuple							=  0
  lowercase			:Optional[int]							=  0
  while True:
    j += 1
    lowercase			:Union[str, Any]							=  [0, 0, 0, 0]
    for i in range(0,	len(lowerCamelCase      )      ):
      lowercase			:Dict							=  get_cost_derivative(i - 1      )
      lowercase			:Optional[Any]							=  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        lowerCamelCase,	lowerCamelCase,	atol=lowerCamelCase,	rtol=lowerCamelCase,	):
      break
    lowercase			:Union[str, Any]							=  temp_parameter_vector
  print(("Number of iterations:", j)      )



def 					UpperCAmelCase__     (   ):
  for i in range(len(lowerCamelCase      )      ):
    print(("Actual output value:", output(lowerCamelCase,	"test"      ))      )
    print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase,	"test"      ))      )


if __name__ == "__main__":
     run_gradient_descent()
     print("\nTesting gradient descent for a linear hypothesis function.\n")
     test_gradient_descent()



