






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    :







	def __init__(  self      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Tuple=13       ,				_lowerCAmelCase      :							Union[str, Any]=7       ,				_lowerCAmelCase      :							Union[str, Any]=6       ,				_lowerCAmelCase      :							List[Any]=17       ,				_lowerCAmelCase      :							Optional[Any]=23       ,				_lowerCAmelCase      :							Optional[int]=11       ,				_lowerCAmelCase      :							List[str]=True       ,				)		-> int:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
		__lowercase             =  floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
		__lowercase             =  floats_tensor((self.batch_size, self.seq_length, 1)			)
		__lowercase             =  floats_tensor((self.batch_size, self.seq_length, 1)			)
		__lowercase             =  ids_tensor((self.batch_size, self.seq_length)       ,				vocab_size=1000			)
		__lowercase             =  random_attention_mask((self.batch_size, self.seq_length)			)

		__lowercase             =  self.get_config()

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







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""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  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Union[str, Any]       ,				)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  DecisionTransformerModel(config=_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

		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      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  config_and_inputs
		__lowercase             =  {
		    """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    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Optional[Any]   =       (DecisionTransformerModel,) if is_torch_available() else ()
	__snake_case	:Optional[int]   =       ()
	__snake_case	:Optional[Any]   =       {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  DecisionTransformerModelTester(self			)
		__lowercase             =  ConfigTester(self       ,				config_class=_lowerCAmelCase       ,				hidden_size=37			)







	def  _a   (  self      :							Union[str, Any]			)		-> Optional[int]:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							List[str]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

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

			__lowercase             =  [
			    """states""",
			    """actions""",
			    """rewards""",
			    """returns_to_go""",
			    """timesteps""",
			    """attention_mask""",
			]

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


@require_torch
class        __UpperCamelCase    (     unittest.TestCase   ):







	@slow
	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







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

		__lowercase             =  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=_lowerCAmelCase			)

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

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

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

			with torch.no_grad():
				__lowercase       ,						__lowercase       ,						__lowercase             =  model(
				    states=_lowerCAmelCase       ,				actions=_lowerCAmelCase       ,				rewards=_lowerCAmelCase       ,				returns_to_go=_lowerCAmelCase       ,				timesteps=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				return_dict=_lowerCAmelCase       ,				)

			self.assertEqual(action_pred.shape       ,				actions.shape			)
			self.assertTrue(torch.allclose(action_pred[0, -1]       ,				expected_outputs[step]       ,				atol=1e-4			)			)
			__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  (  # env.step(action)
			    torch.randn(1       ,				1       ,				config.state_dim			).to(device=_lowerCAmelCase       ,				dtype=torch.floataa			),
			    1.0,
			    False,
			    {},
			)

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









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

