






"""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     :  int   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  Tuple=13   ,       snake_case__     :  str=7   ,       snake_case__     :  Tuple=6   ,       snake_case__     :  Any=17   ,       snake_case__     :  Dict=23   ,       snake_case__     :  int=11   ,       snake_case__     :  Dict=True   ,       ):
										lowerCamelCase_       :							Tuple									=parent
										lowerCamelCase_       :							Tuple									=batch_size
										lowerCamelCase_       :							int									=seq_length
										lowerCamelCase_       :							List[str]									=act_dim
										lowerCamelCase_       :							List[Any]									=state_dim
										lowerCamelCase_       :							List[str]									=hidden_size
										lowerCamelCase_       :							List[Any]									=max_length
										lowerCamelCase_       :							Optional[int]									=is_training


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							str									=floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
										lowerCamelCase_       :							Dict									=floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
										lowerCamelCase_       :							Tuple									=floats_tensor((self.batch_size, self.seq_length, 1)					)
										lowerCamelCase_       :							List[str]									=floats_tensor((self.batch_size, self.seq_length, 1)					)
										lowerCamelCase_       :							Optional[int]									=ids_tensor((self.batch_size, self.seq_length)   ,       vocab_size=1000					)
										lowerCamelCase_       :							Union[str, Any]									=random_attention_mask((self.batch_size, self.seq_length)					)

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

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


			def 	UpperCAmelCase__						(      self     :  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 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Tuple   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Tuple   ,       snake_case__     :  List[Any]   ,       ):
										lowerCamelCase_       :							List[str]									=DecisionTransformerModel(config=snake_case__					)
										model.to(snake_case__					)
										model.eval()
										lowerCamelCase_       :							str									=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 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_       :							Any									=self.prepare_config_and_inputs()
										(
										    lowerCamelCase_
										)       :							Tuple									=config_and_inputs
										lowerCamelCase_       :							int									={
										    """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__			(							snake_case__,   snake_case__,   snake_case__,   unittest.TestCase   ):
			_UpperCAmelCase		:Optional[Any]   			= (DecisionTransformerModel,) if is_torch_available() else ()
			_UpperCAmelCase		:List[Any]   			= ()
			_UpperCAmelCase		: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		:Dict   			= False
			_UpperCAmelCase		:List[Any]   			= False
			_UpperCAmelCase		:Union[str, Any]   			= False
			_UpperCAmelCase		:Union[str, Any]   			= False
			_UpperCAmelCase		:int   			= False
			_UpperCAmelCase		:Tuple   			= False
			_UpperCAmelCase		:List[Any]   			= False
			_UpperCAmelCase		:Dict   			= False
			_UpperCAmelCase		:Tuple   			= False


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Tuple									=DecisionTransformerModelTester(self					)
										lowerCamelCase_       :							List[Any]									=ConfigTester(self   ,       config_class=snake_case__   ,       hidden_size=37					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										self.config_tester.run_common_tests()


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Dict									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__					)


			@slow
			def 	UpperCAmelCase__						(      self     :  Optional[int]					):






										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_       :							int									=DecisionTransformerModel.from_pretrained(snake_case__					)
																	self.assertIsNotNone(snake_case__					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							int									=self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowerCamelCase_       :							List[Any]									=[
																	    """states""",
																	    """actions""",
																	    """rewards""",
																	    """returns_to_go""",
																	    """timesteps""",
																	    """attention_mask""",
																	]

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







@require_torch
class    lowercase__			(							unittest.TestCase   ):


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

										lowerCamelCase_       :							List[str]									=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=snake_case__					)

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

										for step in range(snake_case__					):
																	lowerCamelCase_       :							Optional[int]									=torch.cat([actions, torch.zeros(1   ,       1   ,       config.act_dim   ,       device=snake_case__					)]   ,       dim=1					)
																	lowerCamelCase_       :							Tuple									=torch.cat([rewards, torch.zeros(1   ,       1   ,       device=snake_case__					)]   ,       dim=1					)

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

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

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







"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
