





import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

SCREAMING_SNAKE_CASE_		     = (
    """4S 3H 2C 7S 5H""",
    """9D 8H 2C 6S 7H""",
    """2D 6D 9D TH 7D""",
    """TC 8C 2S JH 6C""",
    """JH 8S TH AH QH""",
    """TS KS 5S 9S AC""",
    """KD 6S 9D TH AD""",
    """KS 8D 4D 9S 4S""",  # pair
    """8C 4S KH JS 4D""",  # pair
    """QH 8H KD JH 8S""",  # pair
    """KC 4H KS 2H 8D""",  # pair
    """KD 4S KC 3H 8S""",  # pair
    """AH 8S AS KC JH""",  # pair
    """3H 4C 4H 3S 2H""",  # 2 pairs
    """5S 5D 2C KH KH""",  # 2 pairs
    """3C KH 5D 5S KH""",  # 2 pairs
    """AS 3C KH AD KH""",  # 2 pairs
    """7C 7S 3S 7H 5S""",  # 3 of a kind
    """7C 7S KH 2H 7H""",  # 3 of a kind
    """AC KH QH AH AS""",  # 3 of a kind
    """2H 4D 3C AS 5S""",  # straight (low ace)
    """3C 5C 4C 2C 6H""",  # straight
    """6S 8S 7S 5H 9H""",  # straight
    """JS QS 9H TS KH""",  # straight
    """QC KH TS JS AH""",  # straight (high ace)
    """8C 9C 5C 3C TC""",  # flush
    """3S 8S 9S 5S KS""",  # flush
    """4C 5C 9C 8C KC""",  # flush
    """JH 8H AH KH QH""",  # flush
    """3D 2H 3H 2C 2D""",  # full house
    """2H 2C 3S 3H 3D""",  # full house
    """KH KC 3S 3H 3D""",  # full house
    """JC 6H JS JD JH""",  # 4 of a kind
    """JC 7H JS JD JH""",  # 4 of a kind
    """JC KH JS JD JH""",  # 4 of a kind
    """2S AS 4S 5S 3S""",  # straight flush (low ace)
    """2D 6D 3D 4D 5D""",  # straight flush
    """5C 6C 3C 7C 4C""",  # straight flush
    """JH 9H TH KH QH""",  # straight flush
    """JH AH TH KH QH""",  # royal flush (high ace straight flush)
)

SCREAMING_SNAKE_CASE_		     = (
    ("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
    ("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
    ("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
    ("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
    ("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
    ("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
    ("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
    ("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
    ("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
    ("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
    ("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
    ("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
    ("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
    ("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
    ("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
    ("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
    ("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
    ("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
    ("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
    ("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
    ("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
    ("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
    ("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
    ("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
    ("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
    ("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
    ("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)

SCREAMING_SNAKE_CASE_		     = (
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", True),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", False),
    ("""AS 3S 4S 8S 2S""", True),
)

SCREAMING_SNAKE_CASE_		     = (
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", False),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", True),
)

SCREAMING_SNAKE_CASE_		     = (
    ("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 1_4]),
    ("""2H 5D 3C AS 5S""", False, [1_4, 5, 5, 3, 2]),
    ("""JH QD KC AS TS""", False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)

SCREAMING_SNAKE_CASE_		     = (
    ("""JH AH TH KH QH""", 0),
    ("""JH 9H TH KH QH""", 0),
    ("""JC KH JS JD JH""", 7),
    ("""KH KC 3S 3H 3D""", 6),
    ("""8C 9C 5C 3C TC""", 0),
    ("""JS QS 9H TS KH""", 0),
    ("""7C 7S KH 2H 7H""", 3),
    ("""3C KH 5D 5S KH""", 2),
    ("""QH 8H KD JH 8S""", 1),
    ("""2D 6D 9D TH 7D""", 0),
)

SCREAMING_SNAKE_CASE_		     = (
    ("""JH AH TH KH QH""", 2_3),
    ("""JH 9H TH KH QH""", 2_2),
    ("""JC KH JS JD JH""", 2_1),
    ("""KH KC 3S 3H 3D""", 2_0),
    ("""8C 9C 5C 3C TC""", 1_9),
    ("""JS QS 9H TS KH""", 1_8),
    ("""7C 7S KH 2H 7H""", 1_7),
    ("""3C KH 5D 5S KH""", 1_6),
    ("""QH 8H KD JH 8S""", 1_5),
    ("""2D 6D 9D TH 7D""", 1_4),
)



def      __lowercase     (   )				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		randrange(len(_SCREAMING_SNAKE_CASE			)			), randrange(len(_SCREAMING_SNAKE_CASE			)			)
      SCREAMING_SNAKE_CASE	=		["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		SORTED_HANDS[play], SORTED_HANDS[oppo]
      return hand, other, expected



def      __lowercase     (   _SCREAMING_SNAKE_CASE = 1_00			)				->     Dict:


      '''simple docstring'''


      return (generate_random_hand() for _ in range(_SCREAMING_SNAKE_CASE			))



@pytest.mark.parametrize("""hand, expected"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			)._is_flush() == expected



@pytest.mark.parametrize("""hand, expected"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			)._is_straight() == expected



@pytest.mark.parametrize("""hand, expected, card_values"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		PokerHand(_SCREAMING_SNAKE_CASE			)
      assert player._is_five_high_straight() == expected
      assert player._card_values == card_values



@pytest.mark.parametrize("""hand, expected"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			)._is_same_kind() == expected



@pytest.mark.parametrize("""hand, expected"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			)._hand_type == expected



@pytest.mark.parametrize("""hand, other, expected"""				,		_SCREAMING_SNAKE_CASE			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			).compare_with(PokerHand(_SCREAMING_SNAKE_CASE			)			) == expected



@pytest.mark.parametrize("""hand, other, expected"""				,		generate_random_hands()			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      assert PokerHand(_SCREAMING_SNAKE_CASE			).compare_with(PokerHand(_SCREAMING_SNAKE_CASE			)			) == expected



def      __lowercase     (   )				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[PokerHand(_SCREAMING_SNAKE_CASE			) for hand in SORTED_HANDS]
      SCREAMING_SNAKE_CASE	=		poker_hands.copy()
      shuffle(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		chain(sorted(_SCREAMING_SNAKE_CASE			)			)
      for index, hand in enumerate(_SCREAMING_SNAKE_CASE			):
            assert hand == poker_hands[index]



def      __lowercase     (   )				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[PokerHand("""2D AC 3H 4H 5S"""			), PokerHand("""2S 3H 4H 5S 6C"""			)]
      pokerhands.sort(reverse=_SCREAMING_SNAKE_CASE			)
      assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"



def      __lowercase     (   )				->     List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		PokerHand("""2C 4S AS 3D 5C"""			)
      SCREAMING_SNAKE_CASE	=		True
      SCREAMING_SNAKE_CASE	=		[5, 4, 3, 2, 14]
      for _ in range(10			):
            assert pokerhand._is_five_high_straight() == expected
            assert pokerhand._card_values == expected_card_values



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		0
      SCREAMING_SNAKE_CASE	=		os.path.abspath(os.path.dirname(_SCREAMING_SNAKE_CASE			)			)
      SCREAMING_SNAKE_CASE	=		os.path.join(_SCREAMING_SNAKE_CASE				,		"""poker_hands.txt"""			)
      with open(_SCREAMING_SNAKE_CASE			) as file_hand:
            for line in file_hand:
                  SCREAMING_SNAKE_CASE	=		line[:14].strip()
                  SCREAMING_SNAKE_CASE	=		line[15:].strip()
                  SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		PokerHand(_SCREAMING_SNAKE_CASE			), PokerHand(_SCREAMING_SNAKE_CASE			)
                  SCREAMING_SNAKE_CASE	=		player.compare_with(_SCREAMING_SNAKE_CASE			)
                  if output == "Win":
                        answer += 1
      assert answer == 3_76









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 )


