"""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   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=7		,    lowerCAmelCase=6		,    lowerCAmelCase=17		,    lowerCAmelCase=23		,    lowerCAmelCase=11		,    lowerCAmelCase=True		,    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
										snake_case								=			floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
										snake_case								=			floats_tensor((self.batch_size, self.seq_length, 1)    )
										snake_case								=			floats_tensor((self.batch_size, self.seq_length, 1)    )
										snake_case								=			ids_tensor((self.batch_size, self.seq_length)		,    vocab_size=10_00    )
										snake_case								=			random_attention_mask((self.batch_size, self.seq_length)    )

										snake_case								=			self.get_config()

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

				def 		snake_case (				self    ):






										"""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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			DecisionTransformerModel(config=UpperCamelCase__    )
										model.to(UpperCamelCase__    )
										model.eval()
										snake_case								=			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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.prepare_config_and_inputs()
										(
										    snake_case						
										)								=			config_and_inputs
										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   lowerCAmelCase_    (			UpperCamelCase_     ,    UpperCamelCase_     ,    UpperCamelCase_     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						(DecisionTransformerModel,) if is_torch_available() else ()
				_lowerCAmelCase     :    Optional[Any]			  =						()
				_lowerCAmelCase     :    List[str]			  =						{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DecisionTransformerModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=UpperCamelCase__		,    hidden_size=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase__    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case								=			[
																    '''states''',
																    '''actions''',
																    '''rewards''',
																    '''returns_to_go''',
																    '''timesteps''',
																    '''attention_mask''',
																]

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





@require_torch
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										snake_case								=			torch.tensor(
										    [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]		,    device=UpperCamelCase__    )

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

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

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

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

																snake_case								=			action_pred[0, -1]
																snake_case								=			torch.cat([states, state]		,    dim=1    )
																snake_case								=			returns_to_go[0, -1] - reward
																snake_case								=			torch.cat([returns_to_go, pred_return.reshape(1		,    1		,    1    )]		,    dim=1    )
																snake_case								=			torch.cat(
																    [timesteps, torch.ones((1, 1)		,    device=UpperCamelCase__		,    dtype=torch.long    ) * (step + 1)]		,    dim=1    )







"""simple docstring"""
SCREAMING_SNAKE_CASE__      						=  "0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
					if not is_onnx_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_onnx_objects import *  # noqa F403
else:
					from .pipelines import OnnxRuntimeModel

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_pt_objects import *  # noqa F403
else:
					from .models import (
					    AutoencoderKL,
					    ControlNetModel,
					    ModelMixin,
					    PriorTransformer,
					    TaFilmDecoder,
					    TransformeraDModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    VQModel,
					)
					from .optimization import (
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					    get_scheduler,
					)
					from .pipelines import (
					    AudioPipelineOutput,
					    ConsistencyModelPipeline,
					    DanceDiffusionPipeline,
					    DDIMPipeline,
					    DDPMPipeline,
					    DiffusionPipeline,
					    DiTPipeline,
					    ImagePipelineOutput,
					    KarrasVePipeline,
					    LDMPipeline,
					    LDMSuperResolutionPipeline,
					    PNDMPipeline,
					    RePaintPipeline,
					    ScoreSdeVePipeline,
					)
					from .schedulers import (
					    CMStochasticIterativeScheduler,
					    DDIMInverseScheduler,
					    DDIMParallelScheduler,
					    DDIMScheduler,
					    DDPMParallelScheduler,
					    DDPMScheduler,
					    DEISMultistepScheduler,
					    DPMSolverMultistepInverseScheduler,
					    DPMSolverMultistepScheduler,
					    DPMSolverSinglestepScheduler,
					    EulerAncestralDiscreteScheduler,
					    EulerDiscreteScheduler,
					    HeunDiscreteScheduler,
					    IPNDMScheduler,
					    KarrasVeScheduler,
					    KDPMaAncestralDiscreteScheduler,
					    KDPMaDiscreteScheduler,
					    PNDMScheduler,
					    RePaintScheduler,
					    SchedulerMixin,
					    ScoreSdeVeScheduler,
					    UnCLIPScheduler,
					    UniPCMultistepScheduler,
					    VQDiffusionScheduler,
					)
					from .training_utils import EMAModel

try:
					if not (is_torch_available() and is_scipy_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
					from .schedulers import LMSDiscreteScheduler

try:
					if not (is_torch_available() and is_torchsde_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
					from .schedulers import DPMSolverSDEScheduler

try:
					if not (is_torch_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    AltDiffusionImgaImgPipeline,
					    AltDiffusionPipeline,
					    AudioLDMPipeline,
					    CycleDiffusionPipeline,
					    IFImgaImgPipeline,
					    IFImgaImgSuperResolutionPipeline,
					    IFInpaintingPipeline,
					    IFInpaintingSuperResolutionPipeline,
					    IFPipeline,
					    IFSuperResolutionPipeline,
					    ImageTextPipelineOutput,
					    KandinskyImgaImgPipeline,
					    KandinskyInpaintPipeline,
					    KandinskyPipeline,
					    KandinskyPriorPipeline,
					    KandinskyVaaControlnetImgaImgPipeline,
					    KandinskyVaaControlnetPipeline,
					    KandinskyVaaImgaImgPipeline,
					    KandinskyVaaInpaintPipeline,
					    KandinskyVaaPipeline,
					    KandinskyVaaPriorEmbaEmbPipeline,
					    KandinskyVaaPriorPipeline,
					    LDMTextToImagePipeline,
					    PaintByExamplePipeline,
					    SemanticStableDiffusionPipeline,
					    ShapEImgaImgPipeline,
					    ShapEPipeline,
					    StableDiffusionAttendAndExcitePipeline,
					    StableDiffusionControlNetImgaImgPipeline,
					    StableDiffusionControlNetInpaintPipeline,
					    StableDiffusionControlNetPipeline,
					    StableDiffusionDepthaImgPipeline,
					    StableDiffusionDiffEditPipeline,
					    StableDiffusionImageVariationPipeline,
					    StableDiffusionImgaImgPipeline,
					    StableDiffusionInpaintPipeline,
					    StableDiffusionInpaintPipelineLegacy,
					    StableDiffusionInstructPixaPixPipeline,
					    StableDiffusionLatentUpscalePipeline,
					    StableDiffusionLDMaDPipeline,
					    StableDiffusionModelEditingPipeline,
					    StableDiffusionPanoramaPipeline,
					    StableDiffusionParadigmsPipeline,
					    StableDiffusionPipeline,
					    StableDiffusionPipelineSafe,
					    StableDiffusionPixaPixZeroPipeline,
					    StableDiffusionSAGPipeline,
					    StableDiffusionUpscalePipeline,
					    StableUnCLIPImgaImgPipeline,
					    StableUnCLIPPipeline,
					    TextToVideoSDPipeline,
					    TextToVideoZeroPipeline,
					    UnCLIPImageVariationPipeline,
					    UnCLIPPipeline,
					    UniDiffuserModel,
					    UniDiffuserPipeline,
					    UniDiffuserTextDecoder,
					    VersatileDiffusionDualGuidedPipeline,
					    VersatileDiffusionImageVariationPipeline,
					    VersatileDiffusionPipeline,
					    VersatileDiffusionTextToImagePipeline,
					    VideoToVideoSDPipeline,
					    VQDiffusionPipeline,
					)

try:
					if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionKDiffusionPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
					from .pipelines import (
					    OnnxStableDiffusionImgaImgPipeline,
					    OnnxStableDiffusionInpaintPipeline,
					    OnnxStableDiffusionInpaintPipelineLegacy,
					    OnnxStableDiffusionPipeline,
					    OnnxStableDiffusionUpscalePipeline,
					    StableDiffusionOnnxPipeline,
					)

try:
					if not (is_torch_available() and is_librosa_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
					from .pipelines import AudioDiffusionPipeline, Mel

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .pipelines import SpectrogramDiffusionPipeline

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_objects import *  # noqa F403
else:
					from .models.controlnet_flax import FlaxControlNetModel
					from .models.modeling_flax_utils import FlaxModelMixin
					from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .models.vae_flax import FlaxAutoencoderKL
					from .pipelines import FlaxDiffusionPipeline
					from .schedulers import (
					    FlaxDDIMScheduler,
					    FlaxDDPMScheduler,
					    FlaxDPMSolverMultistepScheduler,
					    FlaxKarrasVeScheduler,
					    FlaxLMSDiscreteScheduler,
					    FlaxPNDMScheduler,
					    FlaxSchedulerMixin,
					    FlaxScoreSdeVeScheduler,
					)


try:
					if not (is_flax_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    FlaxStableDiffusionControlNetPipeline,
					    FlaxStableDiffusionImgaImgPipeline,
					    FlaxStableDiffusionInpaintPipeline,
					    FlaxStableDiffusionPipeline,
					)

try:
					if not (is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_note_seq_objects import *  # noqa F403
else:
					from .pipelines import MidiProcessor


