






"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (DDIMScheduler,)
    __magic_name__         =     (('''eta''', 0.0), ('''num_inference_steps''', 50))





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			List[Any]				=   {
          'num_train_timesteps': 1_0_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'clip_sample': True,
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					int				,		**A	:					Optional[Any]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			List[Any]				=   self.get_scheduler_config(**A			)
      a     :			Tuple				=   scheduler_class(**A			)

      a,     a     :			Union[str, Any]				=   1_0, 0.0

      a     :			Optional[int]				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter

      scheduler.set_timesteps(A			)

      for t in scheduler.timesteps:
        a     :			Any				=   model(A				,		A			)
        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A				,		A			).prev_sample

      return sample





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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

      a     :			Union[str, Any]				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config(steps_offset=1			)
      a     :			Any				=   scheduler_class(**A			)
      scheduler.set_timesteps(5			)
      assert torch.equal(scheduler.timesteps				,		torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]			)			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''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=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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





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


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      for t in [1, 1_0, 4_9]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      for t, num_inference_steps in zip([1, 1_0, 5_0]				,		[1_0, 5_0, 5_0_0]			):
        self.check_over_forward(time_step=A				,		num_inference_steps=A			)





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      for t, eta in zip([1, 1_0, 4_9]				,		[0.0, 0.5, 1.0]			):
        self.check_over_forward(time_step=A				,		eta=A			)





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config()
      a     :			Optional[int]				=   scheduler_class(**A			)

      assert torch.sum(torch.abs(scheduler._get_variance(0				,		0			) - 0.0			)			) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_2_0				,		4_0_0			) - 0.1_47_71			)			) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_8_0				,		9_6_0			) - 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(4_8_7				,		4_8_6			) - 0.0_09_79			)			) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				,		9_9_8			) - 0.02			)			) < 1E-5





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.full_loop()

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

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





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.full_loop(prediction_type='v_prediction'			)

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

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





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      a     :			Any				=   self.full_loop(set_alpha_to_one=A				,		beta_start=0.01			)
      a     :			int				=   torch.sum(torch.abs(A			)			)
      a     :			List[Any]				=   torch.mean(torch.abs(A			)			)

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





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.full_loop(set_alpha_to_one=A				,		beta_start=0.01			)
      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			List[Any]				=   torch.mean(torch.abs(A			)			)

      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 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

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

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''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 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





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


      '''simple docstring'''




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





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


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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




