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  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=13				,				A_=7				,				A_=6				,				A_=17				,				A_=23				,				A_=11				,				A_=True				,				) ->				Tuple:






           """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 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """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=10_00						)
           _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 		UpperCamelCase_						(					self						) ->				str:






           """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 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				DecisionTransformerModel(config=A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(A_				,				A_				,				A_				,				A_				,				A_				,				A_						)

           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 		UpperCamelCase_						(					self						) ->				int:






           """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  UpperCamelCase				( __lowercase   ,  __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (DecisionTransformerModel,) if is_torch_available() else ()
     A_										=  ()
     A_										=  {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				DecisionTransformerModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=A_				,				hidden_size=37						)



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           self.config_tester.run_common_tests()



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _lowerCamelCase			    =				DecisionTransformerModel.from_pretrained(A_						)
                 self.assertIsNotNone(A_						)





     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCamelCase			    =				model_class(A_						)
                 _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(A_						)]				,				A_						)





@require_torch
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_						(					self						) ->				str:






           """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(A_						)
           _lowerCamelCase			    =				model.config
           torch.manual_seed(0						)
           _lowerCamelCase			    =				torch.randn(1				,				1				,				config.state_dim						).to(device=A_				,				dtype=torch.floataa						)  # env.reset()

           _lowerCamelCase			    =				torch.tensor(
               [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]				,				device=A_						)

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

           for step in range(A_						):
                 _lowerCamelCase			    =				torch.cat([actions, torch.zeros(1				,				1				,				config.act_dim				,				device=A_						)]				,				dim=1						)
                 _lowerCamelCase			    =				torch.cat([rewards, torch.zeros(1				,				1				,				device=A_						)]				,				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=A_				,				actions=A_				,				rewards=A_				,				returns_to_go=A_				,				timesteps=A_				,				attention_mask=A_				,				return_dict=A_				,				)

                 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=A_				,				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=A_				,				dtype=torch.long						) * (step + 1)]				,				dim=1						)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    bool:



      '''simple docstring'''

      _lowerCamelCase			    =				len(__UpperCAmelCase		)
      _lowerCamelCase			    =				[[False] * (required_sum + 1) for _ in range(arr_len + 1		)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1		):
            _lowerCamelCase			    =				True

      # sum is not zero and set is empty then false
      for i in range(1 ,						required_sum + 1		):
            _lowerCamelCase			    =				False

      for i in range(1 ,						arr_len + 1		):
            for j in range(1 ,						required_sum + 1		):
                  if arr[i - 1] > j:
                        _lowerCamelCase			    =				subset[i - 1][j]
                  if arr[i - 1] <= j:
                        _lowerCamelCase			    =				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
     import doctest

     doctest.testmod()