




'''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 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a=13							,__a=7							,__a=6							,__a=17							,__a=23							,__a=11							,__a=True							,)      ->				List[Any]:
          snake_case			:      Optional[Any]        =			parent
          snake_case			:      Optional[int]        =			batch_size
          snake_case			:      Any        =			seq_length
          snake_case			:      Optional[Any]        =			act_dim
          snake_case			:      str        =			state_dim
          snake_case			:      Union[str, Any]        =			hidden_size
          snake_case			:      Any        =			max_length
          snake_case			:      Optional[Any]        =			is_training







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Any        =			floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
          snake_case			:      str        =			floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
          snake_case			:      List[str]        =			floats_tensor((self.batch_size, self.seq_length, 1)							)
          snake_case			:      Optional[Any]        =			floats_tensor((self.batch_size, self.seq_length, 1)							)
          snake_case			:      List[Any]        =			ids_tensor((self.batch_size, self.seq_length)							,vocab_size=1_000							)
          snake_case			:      Optional[Any]        =			random_attention_mask((self.batch_size, self.seq_length)							)

          snake_case			:      str        =			self.get_config()

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







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          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							,__a							,__a							,__a							,__a							,__a							,__a							,__a							,)      ->				Tuple:
          snake_case			:      Optional[Any]        =			DecisionTransformerModel(config=lowerCamelCase__							)
          model.to(lowerCamelCase__							)
          model.eval()
          snake_case			:      str        =			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 			snake_case_      (				self							)      ->				Union[str, Any]:
          snake_case			:      List[str]        =			self.prepare_config_and_inputs()
          (
              snake_case       
          )			:      Tuple        =			config_and_inputs
          snake_case			:      int        =			{
              """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 					_a (__UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					(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_  :     Union[str, Any]          =					False

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







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Union[str, Any]        =			DecisionTransformerModelTester(self							)
          snake_case			:      Optional[int]        =			ConfigTester(self							,config_class=lowerCamelCase__							,hidden_size=37							)







     def 			snake_case_      (				self							)      ->				Dict:
          self.config_tester.run_common_tests()







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      List[str]        =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase__							)







     @slow
     def 			snake_case_      (				self							)      ->				Dict:
          for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               snake_case			:      Dict        =			DecisionTransformerModel.from_pretrained(lowerCamelCase__							)
               self.assertIsNotNone(lowerCamelCase__							)







     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      Any        =			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               snake_case			:      Any        =			model_class(lowerCamelCase__							)
               snake_case			:      int        =			inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               snake_case			:      List[str]        =			[*signature.parameters.keys()]

               snake_case			:      Optional[Any]        =			[
                   """states""",
                   """actions""",
                   """rewards""",
                   """returns_to_go""",
                   """timesteps""",
                   """attention_mask""",
               ]

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







@require_torch
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     @slow
     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case			:      Optional[Any]        =			2  # number of steps of autoregressive prediction we will perform
          snake_case			:      Optional[int]        =			10  # defined by the RL environment, may be normalized
          snake_case			:      List[str]        =			DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""							)
          snake_case			:      Union[str, Any]        =			model.to(lowerCamelCase__							)
          snake_case			:      Optional[int]        =			model.config
          torch.manual_seed(0							)
          snake_case			:      Optional[int]        =			torch.randn(1							,1							,config.state_dim							).to(device=lowerCamelCase__							,dtype=torch.floataa							)  # env.reset()

          snake_case			:      Optional[Any]        =			torch.tensor(
              [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]							,device=lowerCamelCase__							)

          snake_case			:      int        =			torch.tensor(lowerCamelCase__							,device=lowerCamelCase__							,dtype=torch.floataa							).reshape(1							,1							,1							)
          snake_case			:      Optional[Any]        =			state
          snake_case			:      Optional[Any]        =			torch.zeros(1							,0							,config.act_dim							,device=lowerCamelCase__							,dtype=torch.floataa							)
          snake_case			:      List[Any]        =			torch.zeros(1							,0							,device=lowerCamelCase__							,dtype=torch.floataa							)
          snake_case			:      Optional[Any]        =			torch.tensor(0							,device=lowerCamelCase__							,dtype=torch.long							).reshape(1							,1							)

          for step in range(lowerCamelCase__							):
               snake_case			:      Optional[int]        =			torch.cat([actions, torch.zeros(1							,1							,config.act_dim							,device=lowerCamelCase__							)]							,dim=1							)
               snake_case			:      str        =			torch.cat([rewards, torch.zeros(1							,1							,device=lowerCamelCase__							)]							,dim=1							)

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

               with torch.no_grad():
                    snake_case			:      Optional[Any]        =			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							)							)
               snake_case			:      str        =			(  # env.step(action)
                   torch.randn(1							,1							,config.state_dim							).to(device=lowerCamelCase__							,dtype=torch.floataa							),
                   1.0,
                   False,
                   {},
               )

               snake_case			:      Any        =			action_pred[0, -1]
               snake_case			:      int        =			torch.cat([states, state]							,dim=1							)
               snake_case			:      str        =			returns_to_go[0, -1] - reward
               snake_case			:      int        =			torch.cat([returns_to_go, pred_return.reshape(1							,1							,1							)]							,dim=1							)
               snake_case			:      int        =			torch.cat(
                   [timesteps, torch.ones((1, 1)							,device=lowerCamelCase__							,dtype=torch.long							) * (step + 1)]							,dim=1							)







"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff