




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: int										=(UniPCMultistepScheduler,)
				__a							: Dict										=(("""num_inference_steps""", 2_5),)



				def       __snake_case		( self       , **UpperCAmelCase_						):
											lowerCAmelCase   =		{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''solver_order''': 2,
											    '''solver_type''': '''bh2''',
											}

											config.update(**UpperCAmelCase_						)
											return config



				def       __snake_case		( self       , UpperCAmelCase_=0       , **UpperCAmelCase_						):
											lowerCAmelCase   =		dict(self.forward_default_kwargs						)
											lowerCAmelCase   =		kwargs.pop('''num_inference_steps'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		self.dummy_sample
											lowerCAmelCase   =		0.1 * sample
											lowerCAmelCase   =		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		lowerCAmelCase   =		self.get_scheduler_config(**UpperCAmelCase_						)
																		lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)
																		scheduler.set_timesteps(UpperCAmelCase_						)
																		# copy over dummy past residuals
																		lowerCAmelCase   =		dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(UpperCAmelCase_						)
																									lowerCAmelCase   =		scheduler_class.from_pretrained(UpperCAmelCase_						)
																									new_scheduler.set_timesteps(UpperCAmelCase_						)
																									# copy over dummy past residuals
																									lowerCAmelCase   =		dummy_past_residuals[: new_scheduler.config.solver_order]

																		lowerCAmelCase   ,     lowerCAmelCase   =		sample, sample
																		for t in range(UpperCAmelCase_       , time_step + scheduler.config.solver_order + 1						):
																									lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample
																									lowerCAmelCase   =		new_scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample

																									assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"



				def       __snake_case		( self       , UpperCAmelCase_=0       , **UpperCAmelCase_						):
											lowerCAmelCase   =		dict(self.forward_default_kwargs						)
											lowerCAmelCase   =		kwargs.pop('''num_inference_steps'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		self.dummy_sample
											lowerCAmelCase   =		0.1 * sample
											lowerCAmelCase   =		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		lowerCAmelCase   =		self.get_scheduler_config()
																		lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)
																		scheduler.set_timesteps(UpperCAmelCase_						)

																		# copy over dummy past residuals (must be after setting timesteps)
																		lowerCAmelCase   =		dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(UpperCAmelCase_						)
																									lowerCAmelCase   =		scheduler_class.from_pretrained(UpperCAmelCase_						)
																									# copy over dummy past residuals
																									new_scheduler.set_timesteps(UpperCAmelCase_						)

																									# copy over dummy past residual (must be after setting timesteps)
																									lowerCAmelCase   =		dummy_past_residuals[: new_scheduler.config.solver_order]

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample
																		lowerCAmelCase   =		new_scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample

																		assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"



				def       __snake_case		( self       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
											if scheduler is None:
																		lowerCAmelCase   =		self.scheduler_classes[0]
																		lowerCAmelCase   =		self.get_scheduler_config(**UpperCAmelCase_						)
																		lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config(**UpperCAmelCase_						)
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		10
											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter
											scheduler.set_timesteps(UpperCAmelCase_						)

											for i, t in enumerate(scheduler.timesteps						):
																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)
																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						).prev_sample

											return sample



				def       __snake_case		( self						):
											lowerCAmelCase   =		dict(self.forward_default_kwargs						)

											lowerCAmelCase   =		kwargs.pop('''num_inference_steps'''       , UpperCAmelCase_						)

											for scheduler_class in self.scheduler_classes:
																		lowerCAmelCase   =		self.get_scheduler_config()
																		lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

																		lowerCAmelCase   =		self.dummy_sample
																		lowerCAmelCase   =		0.1 * sample

																		if num_inference_steps is not None and hasattr(UpperCAmelCase_       , '''set_timesteps'''						):
																									scheduler.set_timesteps(UpperCAmelCase_						)
																		elif num_inference_steps is not None and not hasattr(UpperCAmelCase_       , '''set_timesteps'''						):
																									lowerCAmelCase   =		num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		lowerCAmelCase   =		[residual + 0.2, residual + 0.15, residual + 0.10]
																		lowerCAmelCase   =		dummy_past_residuals[: scheduler.config.solver_order]

																		lowerCAmelCase   =		scheduler.timesteps[5]
																		lowerCAmelCase   =		scheduler.timesteps[6]

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample
																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_						).prev_sample

																		self.assertEqual(output_a.shape       , sample.shape						)
																		self.assertEqual(output_a.shape       , output_a.shape						)



				def       __snake_case		( self						):
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											lowerCAmelCase   =		UniPCMultistepScheduler(**self.get_scheduler_config()						)
											lowerCAmelCase   =		self.full_loop(scheduler=UpperCAmelCase_						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_mean.item() - 0.2464						) < 1E-3

											lowerCAmelCase   =		DPMSolverSinglestepScheduler.from_config(scheduler.config						)
											lowerCAmelCase   =		DEISMultistepScheduler.from_config(scheduler.config						)
											lowerCAmelCase   =		DPMSolverMultistepScheduler.from_config(scheduler.config						)
											lowerCAmelCase   =		UniPCMultistepScheduler.from_config(scheduler.config						)

											lowerCAmelCase   =		self.full_loop(scheduler=UpperCAmelCase_						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_mean.item() - 0.2464						) < 1E-3



				def       __snake_case		( self						):
											for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																		self.check_over_configs(num_train_timesteps=UpperCAmelCase_						)



				def       __snake_case		( self						):
											self.check_over_configs(thresholding=UpperCAmelCase_						)
											for order in [1, 2, 3]:
																		for solver_type in ["bh1", "bh2"]:
																									for threshold in [0.5, 1.0, 2.0]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    thresholding=UpperCAmelCase_       , prediction_type=UpperCAmelCase_       , sample_max_value=UpperCAmelCase_       , solver_order=UpperCAmelCase_       , solver_type=UpperCAmelCase_       , )



				def       __snake_case		( self						):
											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=UpperCAmelCase_						)



				def       __snake_case		( self						):
											for solver_type in ["bh1", "bh2"]:
																		for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    solver_order=UpperCAmelCase_       , solver_type=UpperCAmelCase_       , prediction_type=UpperCAmelCase_       , )
																																lowerCAmelCase   =		self.full_loop(
																																    solver_order=UpperCAmelCase_       , solver_type=UpperCAmelCase_       , prediction_type=UpperCAmelCase_       , )
																																assert not torch.isnan(UpperCAmelCase_						).any(), "Samples have nan numbers"



				def       __snake_case		( self						):
											self.check_over_configs(lower_order_final=UpperCAmelCase_						)
											self.check_over_configs(lower_order_final=UpperCAmelCase_						)



				def       __snake_case		( self						):
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																		self.check_over_forward(num_inference_steps=UpperCAmelCase_       , time_step=0						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.full_loop()
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_mean.item() - 0.2464						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.full_loop(prediction_type='''v_prediction'''						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_mean.item() - 0.1014						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config(thresholding=UpperCAmelCase_       , dynamic_thresholding_ratio=0						)
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		10
											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter.half()
											scheduler.set_timesteps(UpperCAmelCase_						)

											for i, t in enumerate(scheduler.timesteps						):
																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)
																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						).prev_sample

											assert sample.dtype == torch.floataa






				def       __snake_case		( self       , **UpperCAmelCase_						):
											for scheduler_class in self.scheduler_classes:
																		lowerCAmelCase   =		self.get_scheduler_config(**UpperCAmelCase_						)
																		lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

																		scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
																		assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''original_image''': original_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       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1						)



				def       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



