



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   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=6     ,lowercase=17     ,lowercase=23     ,lowercase=11     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		parent
             UpperCAmelCase_  :	str				      =		batch_size
             UpperCAmelCase_  :	Optional[Any]				      =		seq_length
             UpperCAmelCase_  :	int				      =		act_dim
             UpperCAmelCase_  :	List[str]				      =		state_dim
             UpperCAmelCase_  :	Union[str, Any]				      =		hidden_size
             UpperCAmelCase_  :	Optional[Any]				      =		max_length
             UpperCAmelCase_  :	str				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		floats_tensor((self.batch_size, self.seq_length, self.state_dim))
             UpperCAmelCase_  :	Any				      =		floats_tensor((self.batch_size, self.seq_length, self.act_dim))
             UpperCAmelCase_  :	str				      =		floats_tensor((self.batch_size, self.seq_length, 1))
             UpperCAmelCase_  :	List[Any]				      =		floats_tensor((self.batch_size, self.seq_length, 1))
             UpperCAmelCase_  :	int				      =		ids_tensor((self.batch_size, self.seq_length)     ,vocab_size=1000)
             UpperCAmelCase_  :	Union[str, Any]				      =		random_attention_mask((self.batch_size, self.seq_length))

             UpperCAmelCase_  :	List[str]				      =		self.get_config()

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



       def  A_   (     self):

             """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  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		DecisionTransformerModel(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	List[Any]				      =		model(lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase)

             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  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.prepare_config_and_inputs()
             (
                 (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      (
                 UpperCAmelCase_					
             )      ,      					
             )  :	Any				      =		config_and_inputs
             UpperCAmelCase_  :	List[Any]				      =		{
                 "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   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






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

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

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		DecisionTransformerModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,hidden_size=37)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.run_common_tests()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_  :	str				      =		DecisionTransformerModel.from_pretrained(lowercase)
                   self.assertIsNotNone(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_  :	List[Any]				      =		[
                       "states",
                       "actions",
                       "rewards",
                       "returns_to_go",
                       "timesteps",
                       "attention_mask",
                   ]

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


@require_torch
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       @slow
       def  A_   (     self):

             """simple docstring"""





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

             UpperCAmelCase_  :	Optional[int]				      =		torch.tensor(
                 [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]     ,device=lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		torch.tensor(lowercase     ,device=lowercase     ,dtype=torch.floataa).reshape(1     ,1     ,1)
             UpperCAmelCase_  :	Optional[Any]				      =		state
             UpperCAmelCase_  :	Optional[Any]				      =		torch.zeros(1     ,0     ,config.act_dim     ,device=lowercase     ,dtype=torch.floataa)
             UpperCAmelCase_  :	List[str]				      =		torch.zeros(1     ,0     ,device=lowercase     ,dtype=torch.floataa)
             UpperCAmelCase_  :	Optional[Any]				      =		torch.tensor(0     ,device=lowercase     ,dtype=torch.long).reshape(1     ,1)

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

                   UpperCAmelCase_  :	Optional[Any]				      =		torch.ones(1     ,states.shape[1]).to(dtype=torch.long     ,device=states.device)

                   with torch.no_grad():
                         UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		model(
                             states=lowercase     ,actions=lowercase     ,rewards=lowercase     ,returns_to_go=lowercase     ,timesteps=lowercase     ,attention_mask=lowercase     ,return_dict=lowercase     ,)

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

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





def    _snake_case (       __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







      return int((input_a, input_a).count(0    ) != 0    )







def    _snake_case (       )     ->     None:






      '''simple docstring'''







      assert nand_gate(0					,  0    ) == 1
      assert nand_gate(0					,  1    ) == 1
      assert nand_gate(1					,  0    ) == 1
      assert nand_gate(1					,  1    ) == 0


if __name__ == "__main__":
   print(nand_gate(0, 0))
   print(nand_gate(0, 1))
   print(nand_gate(1, 0))
   print(nand_gate(1, 1))
