





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 			lowercase				:



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Any=13 ,				lowerCamelCase_			:		Any=7 ,				lowerCamelCase_			:		Tuple=6 ,				lowerCamelCase_			:		int=17 ,				lowerCamelCase_			:		List[str]=23 ,				lowerCamelCase_			:		Optional[Any]=11 ,				lowerCamelCase_			:		List[Any]=True ,				):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   parent
										_snake_case :			Optional[int]  						=   batch_size
										_snake_case :			Any  						=   seq_length
										_snake_case :			int  						=   act_dim
										_snake_case :			Any  						=   state_dim
										_snake_case :			Dict  						=   hidden_size
										_snake_case :			Optional[Any]  						=   max_length
										_snake_case :			List[Any]  						=   is_training







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
										_snake_case :			Tuple  						=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
										_snake_case :			Dict  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			Optional[int]  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			str  						=   ids_tensor((self.batch_size, self.seq_length) ,				vocab_size=10_00					)
										_snake_case :			List[str]  						=   random_attention_mask((self.batch_size, self.seq_length)					)

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

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







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''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 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Dict ,				):





										'''simple docstring'''







										_snake_case :			Tuple  						=   DecisionTransformerModel(config=UpperCamelCase__					)
										model.to(UpperCamelCase__					)
										model.eval()
										_snake_case :			List[str]  						=   model(UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__					)

										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			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Dict  						=   self.prepare_config_and_inputs()
										(
										    _snake_case						
										) :			Union[str, Any]  						=   config_and_inputs
										_snake_case :			Optional[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 			lowercase				(							UpperCamelCase_		,			UpperCamelCase_		,			UpperCamelCase_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	(DecisionTransformerModel,) if is_torch_available() else ()
						_UpperCamelCase  :	Dict								=	()
						_UpperCamelCase  :	Optional[int]								=	{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

						# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
						_UpperCamelCase  :	Dict								=	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  :	Union[str, Any]								=	False
						_UpperCamelCase  :	List[str]								=	False
						_UpperCamelCase  :	List[Any]								=	False
						_UpperCamelCase  :	Union[str, Any]								=	False
						_UpperCamelCase  :	Any								=	False
						_UpperCamelCase  :	List[str]								=	False







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   DecisionTransformerModelTester(self					)
										_snake_case :			Any  						=   ConfigTester(self ,				config_class=UpperCamelCase__ ,				hidden_size=37					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										self.config_tester.run_common_tests()







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase__					)







						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_snake_case :			List[str]  						=   DecisionTransformerModel.from_pretrained(UpperCamelCase__					)
														self.assertIsNotNone(UpperCamelCase__					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   self.model_tester.prepare_config_and_inputs_for_common()

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

														_snake_case :			str  						=   [
														    '''states''',
														    '''actions''',
														    '''rewards''',
														    '''returns_to_go''',
														    '''timesteps''',
														    '''attention_mask''',
														]

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



@require_torch
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@slow
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







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

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

										_snake_case :			Tuple  						=   torch.tensor(UpperCamelCase__ ,				device=UpperCamelCase__ ,				dtype=torch.floataa					).reshape(1 ,				1 ,				1					)
										_snake_case :			Optional[int]  						=   state
										_snake_case :			Any  						=   torch.zeros(1 ,				0 ,				config.act_dim ,				device=UpperCamelCase__ ,				dtype=torch.floataa					)
										_snake_case :			int  						=   torch.zeros(1 ,				0 ,				device=UpperCamelCase__ ,				dtype=torch.floataa					)
										_snake_case :			Any  						=   torch.tensor(0 ,				device=UpperCamelCase__ ,				dtype=torch.long					).reshape(1 ,				1					)

										for step in range(UpperCamelCase__					):
														_snake_case :			str  						=   torch.cat([actions, torch.zeros(1 ,				1 ,				config.act_dim ,				device=UpperCamelCase__					)] ,				dim=1					)
														_snake_case :			Union[str, Any]  						=   torch.cat([rewards, torch.zeros(1 ,				1 ,				device=UpperCamelCase__					)] ,				dim=1					)

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

														with torch.no_grad():
																		_snake_case :			str  						=   model(
																		    states=UpperCamelCase__ ,				actions=UpperCamelCase__ ,				rewards=UpperCamelCase__ ,				returns_to_go=UpperCamelCase__ ,				timesteps=UpperCamelCase__ ,				attention_mask=UpperCamelCase__ ,				return_dict=UpperCamelCase__ ,				)

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

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













from __future__ import annotations

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   list(range(len(__lowerCAmelCase ) ) )
				_snake_case :			Optional[int]  						=   [v / w for v, w in zip(__lowerCAmelCase	,			__lowerCAmelCase )]
				index.sort(key=lambda __lowerCAmelCase : ratio[i]	,			reverse=__lowerCAmelCase )

				_snake_case :			float  						=   0
				_snake_case :			list[float]  						=   [0] * len(__lowerCAmelCase )
				for i in index:
								if weight[i] <= capacity:
												_snake_case :			List[Any]  						=   1
												max_value += value[i]
												capacity -= weight[i]
								else:
												_snake_case :			Any  						=   capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()






