




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				UpperCAmelCase_		):
      UpperCAmelCase_        =		(DDPMScheduler,)
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      **lowerCamelCase   :Any      )     ->			str:
       UpperCAmelCase__							 =						{
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "variance_type": "fixed_small",
           "clip_sample": True,
       }

       config.update(**_snake_case      )
       return config
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:


       for timesteps in [1, 5, 100, 1000]:
        self.check_over_configs(num_train_timesteps=_snake_case      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:


       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] ,      [0.0_02, 0.02, 0.2, 2]      ):
        self.check_over_configs(beta_start=_snake_case ,      beta_end=_snake_case      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Optional[int]:


       for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=_snake_case      )
      def 	UpperCAmelCase_						(			self   :Any      )     ->			List[Any]:


       for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=_snake_case      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Any:


       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_snake_case      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       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 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Dict:


       for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=_snake_case      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Union[str, Any]:


       for t in [0, 500, 999]:
        self.check_over_forward(time_step=_snake_case      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						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(487      ) - 0.0_09_79      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						len(_snake_case      )

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

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

        # 2. predict previous mean of sample x_t-1
        UpperCAmelCase__							 =						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
        UpperCAmelCase__							 =						pred_prev_sample

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

       assert abs(result_sum.item() - 2_58.96_06      ) < 1e-2
       assert abs(result_mean.item() - 0.33_72      ) < 1e-3
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(prediction_type="v_prediction"      )
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						len(_snake_case      )

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

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

        # 2. predict previous mean of sample x_t-1
        UpperCAmelCase__							 =						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
        UpperCAmelCase__							 =						pred_prev_sample

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

       assert abs(result_sum.item() - 2_02.02_96      ) < 1e-2
       assert abs(result_mean.item() - 0.26_31      ) < 1e-3
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						[100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=_snake_case      )

       UpperCAmelCase__							 =						scheduler.timesteps

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

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

        self.assertEqual(_snake_case ,      _snake_case      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						[100, 87, 50, 51, 0]

       with self.assertRaises(_snake_case ,      msg="`custom_timesteps` must be in descending order."      ):
        scheduler.set_timesteps(timesteps=_snake_case      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						[100, 87, 50, 1, 0]
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Tuple:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**_snake_case      )

       UpperCAmelCase__							 =						[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 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      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		IFInpaintingSuperResolutionPipeline
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}		)
      UpperCAmelCase_        =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       return self._get_superresolution_dummy_components()
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]=0      )     ->			int:
       if str(lowerCamelCase      ).startswith("mps"      ):
        UpperCAmelCase__							 =						torch.manual_seed(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

       UpperCAmelCase__							 =						floats_tensor((1, 3, 16, 16) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						{
           "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 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Dict:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda" ,      reason="float16 requires CUDA"      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       # 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 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_save_load_local()







      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2 ,      )





