



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 							_a							(					lowerCamelCase__ ):
			A							=     (PNDMScheduler,)
			A							=     (('num_inference_steps', 50),)

			def 			__snake_case    (self,					**SCREAMING_SNAKE_CASE_ )    ->					Any:
						UpperCAmelCase_:						Optional[Any]					=			{
						    """num_train_timesteps""": 1000,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						}

						config.update(**lowercase_ )
						return config

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_=0,					**SCREAMING_SNAKE_CASE_ )    ->					Tuple:
						UpperCAmelCase_:						List[Any]					=			dict(self.forward_default_kwargs )
						UpperCAmelCase_:						Optional[Any]					=			kwargs.pop("""num_inference_steps""",					lowercase_ )
						UpperCAmelCase_:						Any					=			self.dummy_sample
						UpperCAmelCase_:						Optional[int]					=			0.1 * sample
						UpperCAmelCase_:						Optional[Any]					=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
									UpperCAmelCase_:						str					=			self.get_scheduler_config(**lowercase_ )
									UpperCAmelCase_:						Optional[int]					=			scheduler_class(**lowercase_ )
									scheduler.set_timesteps(lowercase_ )
									# copy over dummy past residuals
									UpperCAmelCase_:						int					=			dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(lowercase_ )
												UpperCAmelCase_:						List[Any]					=			scheduler_class.from_pretrained(lowercase_ )
												new_scheduler.set_timesteps(lowercase_ )
												# copy over dummy past residuals
												UpperCAmelCase_:						Optional[int]					=			dummy_past_residuals[:]

									UpperCAmelCase_:						Optional[Any]					=			scheduler.step_prk(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						Optional[int]					=			new_scheduler.step_prk(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample

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

									UpperCAmelCase_:						Union[str, Any]					=			scheduler.step_plms(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						Union[str, Any]					=			new_scheduler.step_plms(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample

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

			def 			__snake_case    (self )    ->					int:
						pass

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_=0,					**SCREAMING_SNAKE_CASE_ )    ->					Any:
						UpperCAmelCase_:						List[Any]					=			dict(self.forward_default_kwargs )
						UpperCAmelCase_:						Any					=			kwargs.pop("""num_inference_steps""",					lowercase_ )
						UpperCAmelCase_:						str					=			self.dummy_sample
						UpperCAmelCase_:						Dict					=			0.1 * sample
						UpperCAmelCase_:						int					=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
									UpperCAmelCase_:						List[Any]					=			self.get_scheduler_config()
									UpperCAmelCase_:						Tuple					=			scheduler_class(**lowercase_ )
									scheduler.set_timesteps(lowercase_ )

									# copy over dummy past residuals (must be after setting timesteps)
									UpperCAmelCase_:						List[Any]					=			dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(lowercase_ )
												UpperCAmelCase_:						Any					=			scheduler_class.from_pretrained(lowercase_ )
												# copy over dummy past residuals
												new_scheduler.set_timesteps(lowercase_ )

												# copy over dummy past residual (must be after setting timesteps)
												UpperCAmelCase_:						int					=			dummy_past_residuals[:]

									UpperCAmelCase_:						List[Any]					=			scheduler.step_prk(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						int					=			new_scheduler.step_prk(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample

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

									UpperCAmelCase_:						str					=			scheduler.step_plms(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						List[str]					=			new_scheduler.step_plms(lowercase_,					lowercase_,					lowercase_,					**lowercase_ ).prev_sample

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

			def 			__snake_case    (self,					**SCREAMING_SNAKE_CASE_ )    ->					Dict:
						UpperCAmelCase_:						int					=			self.scheduler_classes[0]
						UpperCAmelCase_:						Optional[int]					=			self.get_scheduler_config(**lowercase_ )
						UpperCAmelCase_:						int					=			scheduler_class(**lowercase_ )

						UpperCAmelCase_:						Any					=			10
						UpperCAmelCase_:						Any					=			self.dummy_model()
						UpperCAmelCase_:						Tuple					=			self.dummy_sample_deter
						scheduler.set_timesteps(lowercase_ )

						for i, t in enumerate(scheduler.prk_timesteps ):
									UpperCAmelCase_:						int					=			model(lowercase_,					lowercase_ )
									UpperCAmelCase_:						List[str]					=			scheduler.step_prk(lowercase_,					lowercase_,					lowercase_ ).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps ):
									UpperCAmelCase_:						str					=			model(lowercase_,					lowercase_ )
									UpperCAmelCase_:						List[str]					=			scheduler.step_plms(lowercase_,					lowercase_,					lowercase_ ).prev_sample

						return sample

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						Dict					=			dict(self.forward_default_kwargs )

						UpperCAmelCase_:						Tuple					=			kwargs.pop("""num_inference_steps""",					lowercase_ )

						for scheduler_class in self.scheduler_classes:
									UpperCAmelCase_:						str					=			self.get_scheduler_config()
									UpperCAmelCase_:						List[Any]					=			scheduler_class(**lowercase_ )

									UpperCAmelCase_:						Union[str, Any]					=			self.dummy_sample
									UpperCAmelCase_:						Optional[int]					=			0.1 * sample

									if num_inference_steps is not None and hasattr(lowercase_,					"""set_timesteps""" ):
												scheduler.set_timesteps(lowercase_ )
									elif num_inference_steps is not None and not hasattr(lowercase_,					"""set_timesteps""" ):
												UpperCAmelCase_:						Tuple					=			num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									UpperCAmelCase_:						Any					=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
									UpperCAmelCase_:						Tuple					=			dummy_past_residuals[:]

									UpperCAmelCase_:						List[Any]					=			scheduler.step_prk(lowercase_,					0,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						Optional[Any]					=			scheduler.step_prk(lowercase_,					1,					lowercase_,					**lowercase_ ).prev_sample

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

									UpperCAmelCase_:						int					=			scheduler.step_plms(lowercase_,					0,					lowercase_,					**lowercase_ ).prev_sample
									UpperCAmelCase_:						Any					=			scheduler.step_plms(lowercase_,					1,					lowercase_,					**lowercase_ ).prev_sample

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

			def 			__snake_case    (self )    ->					str:






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

			def 			__snake_case    (self )    ->					Optional[int]:






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

						UpperCAmelCase_:						List[Any]					=			self.scheduler_classes[0]
						UpperCAmelCase_:						str					=			self.get_scheduler_config(steps_offset=1 )
						UpperCAmelCase_:						str					=			scheduler_class(**lowercase_ )
						scheduler.set_timesteps(10 )
						assert torch.equal(
						    scheduler.timesteps,					torch.LongTensor(
						        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ),					)

			def 			__snake_case    (self )    ->					Union[str, Any]:






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

			def 			__snake_case    (self )    ->					Optional[Any]:






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

			def 			__snake_case    (self )    ->					List[str]:






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

			def 			__snake_case    (self )    ->					Union[str, Any]:






						for t in [1, 5, 10]:
									self.check_over_forward(time_step=lowercase_ )

			def 			__snake_case    (self )    ->					str:






						for t, num_inference_steps in zip([1, 5, 10],					[10, 50, 100] ):
									self.check_over_forward(num_inference_steps=lowercase_ )

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_:						Tuple					=			27

						for scheduler_class in self.scheduler_classes:
									UpperCAmelCase_:						Optional[Any]					=			self.dummy_sample
									UpperCAmelCase_:						List[Any]					=			0.1 * sample

									UpperCAmelCase_:						List[str]					=			self.get_scheduler_config()
									UpperCAmelCase_:						List[str]					=			scheduler_class(**lowercase_ )

									scheduler.set_timesteps(lowercase_ )

									# before power of 3 fix, would error on first step, so we only need to do two
									for i, t in enumerate(scheduler.prk_timesteps[:2] ):
												UpperCAmelCase_:						List[str]					=			scheduler.step_prk(lowercase_,					lowercase_,					lowercase_ ).prev_sample

			def 			__snake_case    (self )    ->					Dict:






						with self.assertRaises(lowercase_ ):
									UpperCAmelCase_:						Tuple					=			self.scheduler_classes[0]
									UpperCAmelCase_:						Dict					=			self.get_scheduler_config()
									UpperCAmelCase_:						List[str]					=			scheduler_class(**lowercase_ )

									scheduler.step_plms(self.dummy_sample,					1,					self.dummy_sample ).prev_sample

			def 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						List[str]					=			self.full_loop()
						UpperCAmelCase_:						List[Any]					=			torch.sum(torch.abs(lowercase_ ) )
						UpperCAmelCase_:						int					=			torch.mean(torch.abs(lowercase_ ) )

						assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1E-2
						assert abs(result_mean.item() - 0.2_5_8_0 ) < 1E-3

			def 			__snake_case    (self )    ->					Tuple:
						UpperCAmelCase_:						List[str]					=			self.full_loop(prediction_type="""v_prediction""" )
						UpperCAmelCase_:						int					=			torch.sum(torch.abs(lowercase_ ) )
						UpperCAmelCase_:						Any					=			torch.mean(torch.abs(lowercase_ ) )

						assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1E-2
						assert abs(result_mean.item() - 0.0_8_7_8 ) < 1E-3

			def 			__snake_case    (self )    ->					Any:
						UpperCAmelCase_:						str					=			self.full_loop(set_alpha_to_one=lowercase_,					beta_start=0.0_1 )
						UpperCAmelCase_:						Optional[Any]					=			torch.sum(torch.abs(lowercase_ ) )
						UpperCAmelCase_:						List[Any]					=			torch.mean(torch.abs(lowercase_ ) )

						assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1E-2
						assert abs(result_mean.item() - 0.2_9_9_5 ) < 1E-3

			def 			__snake_case    (self )    ->					Tuple:
						UpperCAmelCase_:						Optional[int]					=			self.full_loop(set_alpha_to_one=lowercase_,					beta_start=0.0_1 )
						UpperCAmelCase_:						Optional[int]					=			torch.sum(torch.abs(lowercase_ ) )
						UpperCAmelCase_:						Optional[int]					=			torch.mean(torch.abs(lowercase_ ) )

						assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1E-2
						assert abs(result_mean.item() - 0.2_4_3_4 ) < 1E-3









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   IFInpaintingSuperResolutionPipeline
   lowerCamelCase__ :	str    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   lowerCamelCase__ :	List[Any]    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
   lowerCamelCase__ :	str    =   PipelineTesterMixin.required_optional_params - {'latents'}



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_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       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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