
"""simple docstring"""

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class __lowerCAmelCase						( unittest.TestCase     ):
     # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
     lowercase		  =		(ScoreSdeVeScheduler,)
     lowercase		  =		()





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			4
      __UpperCamelCase							    =			3
      __UpperCamelCase							    =			8
      __UpperCamelCase							    =			8

      __UpperCamelCase							    =			torch.rand((batch_size, num_channels, height, width) )

      return sample





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			4
      __UpperCamelCase							    =			3
      __UpperCamelCase							    =			8
      __UpperCamelCase							    =			8

      __UpperCamelCase							    =			batch_size * num_channels * height * width
      __UpperCamelCase							    =			torch.arange(__UpperCAmelCase )
      __UpperCamelCase							    =			sample.reshape(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			sample / num_elems
      __UpperCamelCase							    =			sample.permute(3      ,	0      ,	1      ,	2 )

      return sample





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      def model(__UpperCAmelCase      ,	__UpperCAmelCase      ,	*__UpperCAmelCase ):
       return sample * t / (t + 1)

      return model





     def 		UpperCAmelCase   (      self      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			{
          'num_train_timesteps': 2000,
          'snr': 0.1_5,
          'sigma_min': 0.0_1,
          'sigma_max': 1348,
          'sampling_eps': 1E-5,
      }

      config.update(**__UpperCAmelCase )
      return config





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase=0      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			dict(self.forward_default_kwargs )

      for scheduler_class in self.scheduler_classes:
       __UpperCamelCase							    =			self.dummy_sample
       __UpperCamelCase							    =			0.1 * sample

       __UpperCamelCase							    =			self.get_scheduler_config(**__UpperCAmelCase )
       __UpperCamelCase							    =			scheduler_class(**__UpperCAmelCase )

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(__UpperCAmelCase )
        __UpperCamelCase							    =			scheduler_class.from_pretrained(__UpperCAmelCase )

       __UpperCamelCase							    =			scheduler.step_pred(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample
       __UpperCamelCase							    =			new_scheduler.step_pred(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample

       assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

       __UpperCamelCase							    =			scheduler.step_correct(__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample
       __UpperCamelCase							    =			new_scheduler.step_correct(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample

       assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler correction are not identical"





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase=0      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			dict(self.forward_default_kwargs )
      kwargs.update(__UpperCAmelCase )

      for scheduler_class in self.scheduler_classes:
       __UpperCamelCase							    =			self.dummy_sample
       __UpperCamelCase							    =			0.1 * sample

       __UpperCamelCase							    =			self.get_scheduler_config()
       __UpperCamelCase							    =			scheduler_class(**__UpperCAmelCase )

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(__UpperCAmelCase )
        __UpperCamelCase							    =			scheduler_class.from_pretrained(__UpperCAmelCase )

       __UpperCamelCase							    =			scheduler.step_pred(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample
       __UpperCamelCase							    =			new_scheduler.step_pred(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample

       assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

       __UpperCamelCase							    =			scheduler.step_correct(__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample
       __UpperCamelCase							    =			new_scheduler.step_correct(
           __UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample

       assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler correction are not identical"





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]      ,	[1, 100, 1000] ):
       self.check_over_configs(sigma_min=__UpperCAmelCase      ,	sigma_max=__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for t in [0.1, 0.5, 0.7_5]:
       self.check_over_forward(time_step=__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			dict(self.forward_default_kwargs )

      __UpperCamelCase							    =			self.scheduler_classes[0]
      __UpperCamelCase							    =			self.get_scheduler_config()
      __UpperCamelCase							    =			scheduler_class(**__UpperCAmelCase )

      __UpperCamelCase							    =			3

      __UpperCamelCase							    =			self.dummy_model()
      __UpperCamelCase							    =			self.dummy_sample_deter

      scheduler.set_sigmas(__UpperCAmelCase )
      scheduler.set_timesteps(__UpperCAmelCase )
      __UpperCamelCase							    =			torch.manual_seed(0 )

      for i, t in enumerate(scheduler.timesteps ):
       __UpperCamelCase							    =			scheduler.sigmas[i]

       for _ in range(scheduler.config.correct_steps ):
        with torch.no_grad():
         __UpperCamelCase							    =			model(__UpperCAmelCase      ,	__UpperCAmelCase )
        __UpperCamelCase							    =			scheduler.step_correct(__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=__UpperCAmelCase      ,	**__UpperCAmelCase ).prev_sample

       with torch.no_grad():
        __UpperCamelCase							    =			model(__UpperCAmelCase      ,	__UpperCAmelCase )

       __UpperCamelCase							    =			scheduler.step_pred(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	generator=__UpperCAmelCase      ,	**__UpperCAmelCase )
       __UpperCamelCase    ,      __UpperCamelCase							    =			output.prev_sample, output.prev_sample_mean

      __UpperCamelCase							    =			torch.sum(torch.abs(__UpperCAmelCase ) )
      __UpperCamelCase							    =			torch.mean(torch.abs(__UpperCAmelCase ) )

      assert np.isclose(result_sum.item()      ,	1_4_3_7_2_7_5_8_5_2_8.0 )
      assert np.isclose(result_mean.item()      ,	1_8_7_1_4_5_3_0.0 )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			dict(self.forward_default_kwargs )

      __UpperCamelCase							    =			kwargs.pop('num_inference_steps'      ,	__UpperCAmelCase )

      for scheduler_class in self.scheduler_classes:
       __UpperCamelCase							    =			self.get_scheduler_config()
       __UpperCamelCase							    =			scheduler_class(**__UpperCAmelCase )

       __UpperCamelCase							    =			self.dummy_sample
       __UpperCamelCase							    =			0.1 * sample

       if num_inference_steps is not None and hasattr(__UpperCAmelCase      ,	'set_timesteps' ):
        scheduler.set_timesteps(__UpperCAmelCase )
       elif num_inference_steps is not None and not hasattr(__UpperCAmelCase      ,	'set_timesteps' ):
        __UpperCamelCase							    =			num_inference_steps

       __UpperCamelCase							    =			scheduler.step_pred(__UpperCAmelCase      ,	0      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample
       __UpperCamelCase							    =			scheduler.step_pred(__UpperCAmelCase      ,	1      ,	__UpperCAmelCase      ,	generator=torch.manual_seed(0 )      ,	**__UpperCAmelCase ).prev_sample

       self.assertEqual(output_a.shape      ,	sample.shape )
       self.assertEqual(output_a.shape      ,	output_a.shape )





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': 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 ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





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

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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



