




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 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Any       ,     a       :	int       ,     a       :	Union[str, Any]=13       ,     a       :	Union[str, Any]=7       ,     a       :	Union[str, Any]=6       ,     a       :	Tuple=17       ,     a       :	Optional[Any]=23       ,     a       :	Dict=11       ,     a       :	List[str]=True       ,     )			->					Tuple:
										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    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Dict:
										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=1_000			)
										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    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										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       :	int       ,     a       :	Union[str, Any]       ,     a       :	int       ,     a       :	Optional[Any]       ,     a       :	List[Any]       ,     a       :	str       ,     a       :	str       ,     a       :	List[Any]       ,     )			->					Dict:
										SCREAMING_SNAKE_CASE           =					DecisionTransformerModel(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	List[str]			)			->					Tuple:
										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 			UpperCAmelCase_      (   A		,      A		,      A		,      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    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					DecisionTransformerModelTester(self			)
										SCREAMING_SNAKE_CASE           =					ConfigTester(self       ,     config_class=a       ,     hidden_size=37			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										self.config_tester.run_common_tests()





							def    _UpperCAmelCase     (					self       :	str			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Any			)			->					Dict:
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					DecisionTransformerModel.from_pretrained(a			)
													self.assertIsNotNone(a			)







							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Dict:
										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(a			)
													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(a			)]       ,     a			)





@require_torch
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										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(a			)
										SCREAMING_SNAKE_CASE           =					model.config
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					torch.randn(1       ,     1       ,     config.state_dim			).to(device=a       ,     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=a			)

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

										for step in range(a			):
													SCREAMING_SNAKE_CASE           =					torch.cat([actions, torch.zeros(1       ,     1       ,     config.act_dim       ,     device=a			)]       ,     dim=1			)
													SCREAMING_SNAKE_CASE           =					torch.cat([rewards, torch.zeros(1       ,     1       ,     device=a			)]       ,     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=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			)			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					(  # env.step(action)
													    torch.randn(1       ,     1       ,     config.state_dim			).to(device=a       ,     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=a       ,     dtype=torch.long			) * (step + 1)]       ,     dim=1			)












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
													SCREAMING_SNAKE_CASE           =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,     device=self.device			)

													# correction step
													for _ in range(self.scheduler.config.correct_steps			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			).sample
																SCREAMING_SNAKE_CASE           =					self.scheduler.step_correct(a       ,     a       ,     generator=a			).prev_sample

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE           =					sample_mean.clamp(0       ,     1			)
										SCREAMING_SNAKE_CASE           =					sample.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






