






"""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    lowercase_    :






						def __init__(    self     ,     a_     ,     a_=1_3     ,     a_=7     ,     a_=6     ,     a_=1_7     ,     a_=2_3     ,     a_=1_1     ,     a_=True     ,     )	->str:







								'''simple docstring'''


								_a							=	parent
								_a							=	batch_size
								_a							=	seq_length
								_a							=	act_dim
								_a							=	state_dim
								_a							=	hidden_size
								_a							=	max_length
								_a							=	is_training






						def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







								'''simple docstring'''


								_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_0_0_0	)
								_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 			lowerCamelCase__    (    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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     )	->str:







								'''simple docstring'''


								_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 			lowerCamelCase__    (    self	)	->Optional[Any]:







								'''simple docstring'''


								_a							=	self.prepare_config_and_inputs()
								(
								    _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    lowercase_    (_UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase,    unittest.TestCase     ):
						A__	:		str       			=				(DecisionTransformerModel,) if is_torch_available() else ()
						A__	:		int       			=				()
						A__	:		Tuple       			=				{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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






						def 			lowerCamelCase__    (    self	)	->Dict:







								'''simple docstring'''


								_a							=	DecisionTransformerModelTester(self	)
								_a							=	ConfigTester(self     ,     config_class=a_     ,     hidden_size=3_7	)






						def 			lowerCamelCase__    (    self	)	->Any:







								'''simple docstring'''


								self.config_tester.run_common_tests()






						def 			lowerCamelCase__    (    self	)	->Tuple:







								'''simple docstring'''


								_a							=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a_	)






						@slow
						def 			lowerCamelCase__    (    self	)	->Any:







								'''simple docstring'''


								for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_a							=	DecisionTransformerModel.from_pretrained(a_	)
										self.assertIsNotNone(a_	)






						def 			lowerCamelCase__    (    self	)	->int:







								'''simple docstring'''


								_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    lowercase_    (unittest.TestCase     ):






						@slow
						def 			lowerCamelCase__    (    self	)	->str:







								'''simple docstring'''


								_a							=	2  # number of steps of autoregressive prediction we will perform
								_a							=	1_0  # 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							=	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							=	(  # 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 warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase							       =    logging.get_logger(__name__)




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     *a_     ,     **a_	)	->None:







        '''simple docstring'''


        warnings.warn(
            "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use YolosImageProcessor instead."     ,     a_     ,     )
        super().__init__(*a_     ,     **a_	)


