"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      (DPMSolverSinglestepScheduler,)
       SCREAMING_SNAKE_CASE_			=      (("num_inference_steps", 2_5),)







       def 		a_						( self,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'prediction_type': 'epsilon',
                'thresholding': False,
                'sample_max_value': 1.0,
                'algorithm_type': 'dpmsolver++',
                'solver_type': 'midpoint',
                'lambda_min_clipped': -float('inf'),
                'variance_type': None,
            }

            config.update(**lowerCAmelCase__)
            return config







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)
                 # copy over dummy past residuals
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					scheduler_class.from_pretrained(lowerCAmelCase__)
                      new_scheduler.set_timesteps(lowerCAmelCase__)
                      # copy over dummy past residuals
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_       ,	snake_case_					       =					sample, sample
                 for t in range(lowerCAmelCase__,							time_step + scheduler.config.solver_order + 1):
                      snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                      snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

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







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







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)

                 # copy over dummy past residuals (must be after setting timesteps)
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

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







       def 		a_						( self,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      Union[str, Any]:
            if scheduler is None:
                 snake_case_					       =					self.scheduler_classes[0]
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            return sample







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					DPMSolverSinglestepScheduler(**self.get_scheduler_config())
            snake_case_					       =					50
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            # make sure that the first t is uneven
            for i, t in enumerate(scheduler.timesteps[3:]):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2574) < 1e-3







       def 		a_						( self)      ->      Dict:
            for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[Any]:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            snake_case_					       =					DPMSolverSinglestepScheduler(**self.get_scheduler_config())
            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3

            snake_case_					       =					DEISMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					UniPCMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverSinglestepScheduler.from_config(scheduler.config)

            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3







       def 		a_						( self)      ->      str:
            self.check_over_configs(thresholding=lowerCAmelCase__)
            for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							sample_max_value=lowerCAmelCase__,							algorithm_type='dpmsolver++',							solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							)







       def 		a_						( self)      ->      Tuple:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							algorithm_type=lowerCAmelCase__,							)
                                snake_case_					       =					self.full_loop(
                                    solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							algorithm_type=lowerCAmelCase__,							)
                                assert not torch.isnan(lowerCAmelCase__).any(), "Samples have nan numbers"







       def 		a_						( self)      ->      Optional[Any]:
            self.check_over_configs(lower_order_final=lowerCAmelCase__)
            self.check_over_configs(lower_order_final=lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            self.check_over_configs(lambda_min_clipped=-float('inf'))
            self.check_over_configs(lambda_min_clipped=-5.1)







       def 		a_						( self)      ->      Any:
            self.check_over_configs(variance_type=lowerCAmelCase__)
            self.check_over_configs(variance_type='learned_range')







       def 		a_						( self)      ->      List[Any]:
            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=lowerCAmelCase__,							time_step=0)







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.full_loop()
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.full_loop(use_karras_sigmas=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2248) < 1e-3







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction')
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.1453) < 1e-3







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction',							use_karras_sigmas=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.0649) < 1e-3







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(thresholding=lowerCAmelCase__,							dynamic_thresholding_ratio=0)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter.half()
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            assert sample.dtype == torch.floataa









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
	import torch

	from transformers import AutoModelForImageClassification

if is_vision_available():
	from transformers import AutoImageProcessor




@require_torch
@require_vision
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347]       ,			device=a       ,			dtype=torch.float       ,			)
								self.assertTrue(torch.allclose(logits[0, :3]       ,			a       ,			atol=1E-4					)					)





