




'''simple docstring'''




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







		'''simple docstring'''







		lowerCAmelCase_				:					Optional[Any]				     =						(DDIMScheduler,)
		lowerCAmelCase_				:					Optional[Any]				     =						(("""eta""", 0.0), ("""num_inference_steps""", 50))


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		**_UpperCAmelCase    :	Optional[int] ):






			"""simple docstring"""






			UpperCAmelCase__										=    {
			    """num_train_timesteps""": 10_00,
			    """beta_start""": 0.0001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			    """clip_sample""": True,
			}

			config.update(**A_ )
			return config


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		**_UpperCAmelCase    :	Any ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.scheduler_classes[0]
			UpperCAmelCase__										=    self.get_scheduler_config(**A_ )
			UpperCAmelCase__										=    scheduler_class(**A_ )

			UpperCAmelCase__							,			UpperCAmelCase__										=    10, 0.0

			UpperCAmelCase__										=    self.dummy_model()
			UpperCAmelCase__										=    self.dummy_sample_deter

			scheduler.set_timesteps(A_ )

			for t in scheduler.timesteps:
				UpperCAmelCase__										=    model(A_						,		A_ )
				UpperCAmelCase__										=    scheduler.step(A_						,		A_						,		A_						,		A_ ).prev_sample

			return sample


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






			"""simple docstring"""






			for timesteps in [1_00, 5_00, 10_00]:
				self.check_over_configs(num_train_timesteps=A_ )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






			"""simple docstring"""






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

			UpperCAmelCase__										=    self.scheduler_classes[0]
			UpperCAmelCase__										=    self.get_scheduler_config(steps_offset=1 )
			UpperCAmelCase__										=    scheduler_class(**A_ )
			scheduler.set_timesteps(5 )
			assert torch.equal(scheduler.timesteps						,		torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )


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






			"""simple docstring"""






			for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,		[0.002, 0.02, 0.2, 2] ):
				self.check_over_configs(beta_start=A_						,		beta_end=A_ )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






			"""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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






			"""simple docstring"""






			for t, num_inference_steps in zip([1, 10, 50]						,		[10, 50, 5_00] ):
				self.check_over_forward(time_step=A_						,		num_inference_steps=A_ )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.scheduler_classes[0]
			UpperCAmelCase__										=    self.get_scheduler_config()
			UpperCAmelCase__										=    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_20						,		4_00 ) - 0.1_4771 ) ) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_80						,		9_60 ) - 0.3_2460 ) ) < 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_87						,		4_86 ) - 0.0_0979 ) ) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99						,		9_98 ) - 0.02 ) ) < 1E-5


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.full_loop()

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

			assert abs(result_sum.item() - 172.0067 ) < 1E-2
			assert abs(result_mean.item() - 0.22_3967 ) < 1E-3


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.full_loop(prediction_type="""v_prediction""" )

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

			assert abs(result_sum.item() - 52.5302 ) < 1E-2
			assert abs(result_mean.item() - 0.0684 ) < 1E-3


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.full_loop(set_alpha_to_one=A_						,		beta_start=0.01 )
			UpperCAmelCase__										=    torch.sum(torch.abs(A_ ) )
			UpperCAmelCase__										=    torch.mean(torch.abs(A_ ) )

			assert abs(result_sum.item() - 149.8295 ) < 1E-2
			assert abs(result_mean.item() - 0.1951 ) < 1E-3



		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.full_loop(set_alpha_to_one=A_						,		beta_start=0.01 )
			UpperCAmelCase__										=    torch.sum(torch.abs(A_ ) )
			UpperCAmelCase__										=    torch.mean(torch.abs(A_ ) )

			assert abs(result_sum.item() - 149.0784 ) < 1E-2
			assert abs(result_mean.item() - 0.1941 ) < 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_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






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


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






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