





'''simple docstring'''





import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)

class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           self.assertEqual(x.component(0 )		,1 )
           self.assertEqual(x.component(2 )		,3 )
           SCREAMING_SNAKE_CASE	=		Vector()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([0, 0, 0, 0, 0, 1] )
           self.assertEqual(str(lowerCamelCase__ )		,"""(0,0,0,0,0,1)""" )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3, 4] )
           self.assertEqual(len(lowerCamelCase__ )		,4 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2] )
           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3, 4, 5] )
           SCREAMING_SNAKE_CASE	=		Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
           SCREAMING_SNAKE_CASE	=		Vector([1, -1, 1, -1, 2, -3, 4, -5] )
           self.assertAlmostEqual(x.euclidean_length()		,2.236		,3 )
           self.assertAlmostEqual(y.euclidean_length()		,7.416		,3 )
           self.assertEqual(z.euclidean_length()		,0 )
           self.assertAlmostEqual(w.euclidean_length()		,7.616		,3 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           SCREAMING_SNAKE_CASE	=		Vector([1, 1, 1] )
           self.assertEqual((x + y).component(0 )		,2 )
           self.assertEqual((x + y).component(1 )		,3 )
           self.assertEqual((x + y).component(2 )		,4 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           SCREAMING_SNAKE_CASE	=		Vector([1, 1, 1] )
           self.assertEqual((x - y).component(0 )		,0 )
           self.assertEqual((x - y).component(1 )		,1 )
           self.assertEqual((x - y).component(2 )		,2 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           SCREAMING_SNAKE_CASE	=		Vector([2, -1, 4] )  # for test of dot product
           SCREAMING_SNAKE_CASE	=		Vector([1, -2, -1] )
           self.assertEqual(str(x * 3.0 )		,"""(3.0,6.0,9.0)""" )
           self.assertEqual((a * b)		,0 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           self.assertEqual(str(zero_vector(10 ) ).count("""0""" )		,10 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


           self.assertEqual(str(unit_basis_vector(3		,1 ) )		,"""(0,1,0)""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           SCREAMING_SNAKE_CASE	=		Vector([1, 0, 1] )
           self.assertEqual(str(axpy(2		,lowerCamelCase__		,lowerCamelCase__ ) )		,"""(3,4,7)""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 0, 0, 0, 0, 0] )
           SCREAMING_SNAKE_CASE	=		x.copy()
           self.assertEqual(str(lowerCamelCase__ )		,str(lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Vector([1, 0, 0] )
           x.change_component(0		,0 )
           x.change_component(1		,1 )
           self.assertEqual(str(lowerCamelCase__ )		,"""(0,1,0)""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""		,str(lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           SCREAMING_SNAKE_CASE	=		[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
           for x in range(a.height() ):
                 for y in range(a.width() ):
                       self.assertEqual(minors[x][y]		,a.minor(lowerCamelCase__		,lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           SCREAMING_SNAKE_CASE	=		[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
           for x in range(a.height() ):
                 for y in range(a.width() ):
                       self.assertEqual(cofactors[x][y]		,a.cofactor(lowerCamelCase__		,lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           self.assertEqual(-5		,a.determinant() )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		,3		,3 )
           SCREAMING_SNAKE_CASE	=		Vector([1, 2, 3] )
           self.assertEqual("""(14,32,50)"""		,str(a * x ) )
           self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""		,str(a * 2 ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           a.change_component(0		,2		,5 )
           self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""		,str(lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           self.assertEqual(7		,a.component(2		,1 )		,0.01 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]		,3		,3 )
           self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""		,str(a + b ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]		,3		,3 )
           SCREAMING_SNAKE_CASE	=		Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]		,3		,3 )
           self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""		,str(a - b ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[Any]:




           '''simple docstring'''


           self.assertEqual(
               """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""		,str(square_zero_matrix(5 ) )		,)


if __name__ == "__main__":
      unittest.main()









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		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict=13		,lowerCamelCase__		:					Tuple=7		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					List[str]=17		,lowerCamelCase__		:					Union[str, Any]=23		,lowerCamelCase__		:					Tuple=11		,lowerCamelCase__		:					Any=True		,)  ->	List[Any]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[Any]:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE	=		self.get_config()

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Union[str, Any]		,)  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModel(config=lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.eval()
           SCREAMING_SNAKE_CASE	=		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] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      
           )	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{
               """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__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    (DecisionTransformerModel,) if is_torch_available() else ()
     __snake_case					:			Dict				=    ()
     __snake_case					:			List[Any]				=    {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,hidden_size=37 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

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




           '''simple docstring'''


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

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

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

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

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

                 with torch.no_grad():
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		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 ) )
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		(  # env.step(action)
                     torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,dtype=torch.floataa ),
                     1.0,
                     False,
                     {},
                 )

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


