


'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class   __lowerCAmelCase						(__A					):





      '''simple docstring'''






      lowerCAmelCase__ :					List[Any]   =							(DDIMScheduler,)
      lowerCAmelCase__ :					str   =							(("""eta""", 0.0), ("""num_inference_steps""", 50))



      def 				UpperCamelCase__		(self   :   int       ,						**UpperCamelCase   :   Dict			):

             '''simple docstring'''







             lowercase__				      =			{
                 "num_train_timesteps": 1000,
                 "beta_start": 0.00_01,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
                 "clip_sample": True,
             }

             config.update(**UpperCamelCase			)
             return config



      def 				UpperCamelCase__		(self   :   int       ,						**UpperCamelCase   :   Any			):

             '''simple docstring'''







             lowercase__				      =			self.scheduler_classes[0]
             lowercase__				      =			self.get_scheduler_config(**UpperCamelCase			)
             lowercase__				      =			scheduler_class(**UpperCamelCase			)

             lowercase__				      =			10, 0.0

             lowercase__				      =			self.dummy_model()
             lowercase__				      =			self.dummy_sample_deter

             scheduler.set_timesteps(UpperCamelCase			)

             for t in scheduler.timesteps:
                    lowercase__				      =			model(UpperCamelCase       ,						UpperCamelCase			)
                    lowercase__				      =			scheduler.step(UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase			).prev_sample

             return sample



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             for timesteps in [100, 500, 1000]:
                    self.check_over_configs(num_train_timesteps=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Tuple			):

             '''simple docstring'''







             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=UpperCamelCase			)

             lowercase__				      =			self.scheduler_classes[0]
             lowercase__				      =			self.get_scheduler_config(steps_offset=1			)
             lowercase__				      =			scheduler_class(**UpperCamelCase			)
             scheduler.set_timesteps(5			)
             assert torch.equal(scheduler.timesteps       ,						torch.LongTensor([801, 601, 401, 201, 1]			)			)



      def 				UpperCamelCase__		(self   :   Tuple			):

             '''simple docstring'''







             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=UpperCamelCase       ,						beta_end=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Tuple			):

             '''simple docstring'''







             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             for t in [1, 10, 49]:
                    self.check_over_forward(time_step=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             for t, num_inference_steps in zip([1, 10, 50]       ,						[10, 50, 500]			):
                    self.check_over_forward(time_step=UpperCamelCase       ,						num_inference_steps=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             for t, eta in zip([1, 10, 49]       ,						[0.0, 0.5, 1.0]			):
                    self.check_over_forward(time_step=UpperCamelCase       ,						eta=UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             lowercase__				      =			self.scheduler_classes[0]
             lowercase__				      =			self.get_scheduler_config()
             lowercase__				      =			scheduler_class(**UpperCamelCase			)

             assert torch.sum(torch.abs(scheduler._get_variance(0       ,						0			) - 0.0			)			) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(420       ,						400			) - 0.1_47_71			)			) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980       ,						960			) - 0.3_24_60			)			) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0       ,						0			) - 0.0			)			) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487       ,						486			) - 0.0_09_79			)			) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999       ,						998			) - 0.02			)			) < 1E-5



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			self.full_loop()

             lowercase__				      =			torch.sum(torch.abs(UpperCamelCase			)			)
             lowercase__				      =			torch.mean(torch.abs(UpperCamelCase			)			)

             assert abs(result_sum.item() - 1_72.00_67			) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67			) < 1E-3



      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.full_loop(prediction_type='''v_prediction'''			)

             lowercase__				      =			torch.sum(torch.abs(UpperCamelCase			)			)
             lowercase__				      =			torch.mean(torch.abs(UpperCamelCase			)			)

             assert abs(result_sum.item() - 52.53_02			) < 1E-2
             assert abs(result_mean.item() - 0.06_84			) < 1E-3



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			self.full_loop(set_alpha_to_one=UpperCamelCase       ,						beta_start=0.01			)
             lowercase__				      =			torch.sum(torch.abs(UpperCamelCase			)			)
             lowercase__				      =			torch.mean(torch.abs(UpperCamelCase			)			)

             assert abs(result_sum.item() - 1_49.82_95			) < 1E-2
             assert abs(result_mean.item() - 0.19_51			) < 1E-3






      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             lowercase__				      =			self.full_loop(set_alpha_to_one=UpperCamelCase       ,						beta_start=0.01			)
             lowercase__				      =			torch.sum(torch.abs(UpperCamelCase			)			)
             lowercase__				      =			torch.mean(torch.abs(UpperCamelCase			)			)

             assert abs(result_sum.item() - 1_49.07_84			) < 1E-2
             assert abs(result_mean.item() - 0.19_41			) < 1E-3








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

             lowercase__				      =			{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             self._test_save_load_optional_components()



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()       ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,						)
      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







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



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







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




