


"""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 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=13							, _a=7							, _a=6							, _a=17							, _a=23							, _a=11							, _a=True							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			act_dim
				__a           =			state_dim
				__a           =			hidden_size
				__a           =			max_length
				__a           =			is_training

		def 			__UpperCAmelCase       (  self      ):
				__a           =			floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
				__a           =			floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
				__a           =			floats_tensor((self.batch_size, self.seq_length, 1)      )
				__a           =			floats_tensor((self.batch_size, self.seq_length, 1)      )
				__a           =			ids_tensor((self.batch_size, self.seq_length)							, vocab_size=1_000      )
				__a           =			random_attention_mask((self.batch_size, self.seq_length)      )

				__a           =			self.get_config()

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

		def 			__UpperCAmelCase       (  self      ):
				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							, ):
				__a           =			DecisionTransformerModel(config=_a      )
				model.to(_a      )
				model.eval()
				__a           =			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      ):
				__a           =			self.prepare_config_and_inputs()
				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			config_and_inputs
				__a           =			{
				    '''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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Any										=				(DecisionTransformerModel,) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				()
		__UpperCAmelCase		:			Any										=				{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			DecisionTransformerModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, hidden_size=37      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			DecisionTransformerModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

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

						__a           =			[
						    '''states''',
						    '''actions''',
						    '''rewards''',
						    '''returns_to_go''',
						    '''timesteps''',
						    '''attention_mask''',
						]

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







@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



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

				__a           =			torch.tensor(
				    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]							, device=_a      )

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

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

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

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

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










"""simple docstring"""




import numpy as np

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     float			) ->      np.ndarray:
		return np.where(vector > 0    ,					lowerCAmelCase__    ,					(alpha * (np.exp(lowerCAmelCase__			) - 1))			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






