






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 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   List[str]		,     __A:   Tuple		,     __A:   List[str]=13		,     __A:   Union[str, Any]=7		,     __A:   Union[str, Any]=6		,     __A:   Dict=17		,     __A:   List[str]=23		,     __A:   Any=11		,     __A:   str=True		,     )    ->				Any:
												_A								= parent
												_A								= batch_size
												_A								= seq_length
												_A								= act_dim
												_A								= state_dim
												_A								= hidden_size
												_A								= max_length
												_A								= is_training

							def       __A (  self:   str   )    ->				Optional[Any]:
												_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=10_00   )
												_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       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
												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:   List[Any]		,     __A:   Tuple		,     __A:   int		,     __A:   Any		,     __A:   Union[str, Any]		,     __A:   Union[str, Any]		,     __A:   List[str]		,     __A:   Any		,     )    ->				Any:
												_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       __A (  self:   str   )    ->				Tuple:
												_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 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	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       __A (  self:   Any   )    ->				Optional[int]:
												_A								= DecisionTransformerModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A		,     hidden_size=37   )

							def       __A (  self:   str   )    ->				Dict:
												self.config_tester.run_common_tests()

							def       __A (  self:   Optional[Any]   )    ->				Optional[Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A   )

							@slow
							def       __A (  self:   Optional[int]   )    ->				str:
												for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A								= DecisionTransformerModel.from_pretrained(__A   )
																	self.assertIsNotNone(__A   )



							def       __A (  self:   List[str]   )    ->				List[Any]:
												_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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							@slow
							def       __A (  self:   str   )    ->				Any:
												_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(
												    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]		,     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   )











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															_A								= mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= [[0] * (w + 1) for _ in range(n + 1 )]

					for i in range(1					,			n + 1 ):
										for w_ in range(1					,			w + 1 ):
															if wt[i - 1] <= w_:
																				_A								= max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]					,			dp[i - 1][w_] )
															else:
																				_A								= dp[i - 1][w_]

					return dp[n][w_], dp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if not (isinstance(_lowercase					,			(list, tuple) ) and isinstance(_lowercase					,			(list, tuple) )):
										raise ValueError(
										    '''Both the weights and values vectors must be either lists or tuples''' )

					_A								= len(_lowercase )
					if num_items != len(_lowercase ):
										_A								= (
										    '''The number of weights must be the same as the number of values.\n'''
										    f"""But got {num_items} weights and {len(_lowercase )} values"""
										)
										raise ValueError(_lowercase )
					for i in range(_lowercase ):
										if not isinstance(wt[i]					,			_lowercase ):
															_A								= (
															    '''All weights must be integers but got weight of '''
															    f"""type {type(wt[i] )} at index {i}"""
															)
															raise TypeError(_lowercase )

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			_lowercase					,			_lowercase )
										else:
															optimal_set.add(_lowercase )
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			j - wt[i - 1]					,			_lowercase )


if __name__ == "__main__":
				__A      =				[3, 2, 4, 4]
				__A      =				[4, 3, 2, 3]
				__A      =				4
				__A      =				6
				__A      =				[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				__A ,  __A      =				knapsack(w, wt, val, n)
				print(optimal_solution)
				print(mf_knapsack(n, wt, val, w))  # switched the n and w

				# testing the dynamic programming problem with example
				# the optimal subset for the above example are items 3 and 4
				__A ,  __A      =				knapsack_with_example_solution(w, wt, val)
				assert optimal_solution == 8
				assert optimal_subset == {3, 4}
				print('optimal_value = ', optimal_solution)
				print('An optimal subset corresponding to the optimal value', optimal_subset)



