"""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			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				Tuple=13   , _snake_case			:				Optional[Any]=7   , _snake_case			:				Dict=6   , _snake_case			:				Optional[Any]=17   , _snake_case			:				str=23   , _snake_case			:				Tuple=11   , _snake_case			:				int=True   , )							->       str:
									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 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Union[str, Any]:
									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 			lowerCAmelCase_					(						self			:				List[str]		)							->       Tuple:
									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 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Tuple   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]   , _snake_case			:				Optional[Any]   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				DecisionTransformerModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)

									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 			lowerCAmelCase_					(						self			:				Any		)							->       Any:
									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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    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 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				DecisionTransformerModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[Any]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				DecisionTransformerModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									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(_snake_case		)
													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(_snake_case		)]   , _snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									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(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model.config
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				torch.randn(1   , 1   , config.state_dim		).to(device=_snake_case   , dtype=torch.floataa		)  # env.reset()

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

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

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

													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=_snake_case   , 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=_snake_case   , dtype=torch.long		) * (step + 1)]   , dim=1		)





"""simple docstring"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




