



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		int			       =    (DDPMScheduler,)



					def     _a							(     self					:       Optional[Any]				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    {
								    'num_train_timesteps': 10_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'variance_type': 'fixed_small',
								    'clip_sample': True,
								}

								config.update(**_snake_case					)
								return config



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,					[0.002, 0.02, 0.2, 2]					):
											self.check_over_configs(beta_start=_snake_case				,					beta_end=_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								self.check_over_configs(thresholding=_snake_case					)
								for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=_snake_case				,					prediction_type=_snake_case				,					sample_max_value=_snake_case				,					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for t in [0, 5_00, 9_99]:
											self.check_over_forward(time_step=_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0979					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.02					)					) < 1E-5



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    len(_snake_case					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter
								A__       =    torch.manual_seed(0					)

								for t in reversed(range(_snake_case					)					):
											# 1. predict noise residual
											A__       =    model(_snake_case				,					_snake_case					)

											# 2. predict previous mean of sample x_t-1
											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case				,					generator=_snake_case					).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											A__       =    pred_prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								assert abs(result_sum.item() - 258.9606					) < 1E-2
								assert abs(result_mean.item() - 0.3372					) < 1E-3



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config(prediction_type='v_prediction'					)
								A__       =    scheduler_class(**_snake_case					)

								A__       =    len(_snake_case					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter
								A__       =    torch.manual_seed(0					)

								for t in reversed(range(_snake_case					)					):
											# 1. predict noise residual
											A__       =    model(_snake_case				,					_snake_case					)

											# 2. predict previous mean of sample x_t-1
											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case				,					generator=_snake_case					).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											A__       =    pred_prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								assert abs(result_sum.item() - 202.0296					) < 1E-2
								assert abs(result_mean.item() - 0.2631					) < 1E-3



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=_snake_case					)

								A__       =    scheduler.timesteps

								for i, timestep in enumerate(_snake_case					):
											if i == len(_snake_case					) - 1:
														A__       =    -1
											else:
														A__       =    timesteps[i + 1]

											A__       =    scheduler.previous_timestep(_snake_case					)
											A__       =    prev_t.item()

											self.assertEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 51, 0]

								with self.assertRaises(_snake_case				,					msg='`custom_timesteps` must be in descending order.'					):
											scheduler.set_timesteps(timesteps=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 1, 0]
								A__       =    len(_snake_case					)

								with self.assertRaises(_snake_case				,					msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'					):
											scheduler.set_timesteps(num_inference_steps=_snake_case				,					timesteps=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    _snake_case				,					msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'				,					):
											scheduler.set_timesteps(timesteps=_snake_case					)









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      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    '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					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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




