import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				(EulerDiscreteScheduler,)
   __lowerCAmelCase           =				10







   def 						lowerCamelCase_	(     self						:						List[str]   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        'num_train_timesteps': 1100,
        'beta_start': 0.0_0_0_1,
        'beta_end': 0.0_2,
        'beta_schedule': 'linear',
    }

    config.update(**UpperCamelCase_				)
    return config







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    for timesteps in [10, 50, 100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,   [0.0_0_0_2, 0.0_0_2, 0.0_2]				):
     self.check_over_configs(beta_start=UpperCamelCase_   ,   beta_end=UpperCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


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

    scheduler.set_timesteps(self.num_inference_steps				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCamelCase			  =      sample.to(UpperCamelCase_				)

    for i, t in enumerate(scheduler.timesteps				):
     UpperCamelCase			  =      scheduler.scale_model_input(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      model(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      scheduler.step(UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_   ,   generator=UpperCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

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

    assert abs(result_sum.item() - 1_0.0_8_0_7				) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1				) < 1E-3







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config(prediction_type="""v_prediction"""				)
    UpperCamelCase			  =      scheduler_class(**UpperCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCamelCase			  =      sample.to(UpperCamelCase_				)

    for i, t in enumerate(scheduler.timesteps				):
     UpperCamelCase			  =      scheduler.scale_model_input(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      model(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      scheduler.step(UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_   ,   generator=UpperCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

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

    assert abs(result_sum.item() - 0.0_0_0_2				) < 1E-2
    assert abs(result_mean.item() - 2.2_676E-06				) < 1E-3







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


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

    scheduler.set_timesteps(self.num_inference_steps   ,   device=UpperCamelCase_				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCamelCase			  =      sample.to(UpperCamelCase_				)

    for t in scheduler.timesteps:
     UpperCamelCase			  =      scheduler.scale_model_input(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      model(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      scheduler.step(UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_   ,   generator=UpperCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

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

    assert abs(result_sum.item() - 1_0.0_8_0_7				) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1				) < 1E-3







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config()
    UpperCamelCase			  =      scheduler_class(**UpperCamelCase_   ,   use_karras_sigmas=UpperCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps   ,   device=UpperCamelCase_				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCamelCase			  =      sample.to(UpperCamelCase_				)

    for t in scheduler.timesteps:
     UpperCamelCase			  =      scheduler.scale_model_input(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      model(UpperCamelCase_   ,   UpperCamelCase_				)

     UpperCamelCase			  =      scheduler.step(UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_   ,   generator=UpperCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

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

    assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9				) < 1E-2
    assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3				) < 1E-3


"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   int           =     IFImgaImgSuperResolutionPipeline
      UpperCamelCase							:   Union[str, Any]           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCamelCase							:   Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
      UpperCamelCase							:   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'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      int     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 3, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'original_image': original_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     ,	)

