
"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(DPMSolverMultistepScheduler,)
	lowercase_        =	(('num_inference_steps', 2_5),)




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			Optional[Any]		=    {
			    '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',
			    'lower_order_final': False,
			    'lambda_min_clipped': -float('inf'					),
			    'variance_type': None,
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			Dict		=    dict(self.forward_default_kwargs					)
			lowerCamelCase     :			str		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample
			lowerCamelCase     :			Optional[int]		=    0.1 * sample
			lowerCamelCase     :			str		=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			List[str]		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			str		=    scheduler_class(**UpperCAmelCase_					)
					scheduler.set_timesteps(UpperCAmelCase_					)
					# copy over dummy past residuals
					lowerCamelCase     :			Optional[Any]		=    dummy_past_residuals[: scheduler.config.solver_order]

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

					lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    sample, sample
					for t in range(UpperCAmelCase_		, time_step + scheduler.config.solver_order + 1					):
							lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
							lowerCamelCase     :			Tuple		=    new_scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			pass




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0		, **UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			str		=    dict(self.forward_default_kwargs					)
			lowerCamelCase     :			Optional[int]		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    self.dummy_sample
			lowerCamelCase     :			Dict		=    0.1 * sample
			lowerCamelCase     :			Any		=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			int		=    self.get_scheduler_config()
					lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)
					scheduler.set_timesteps(UpperCAmelCase_					)

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

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

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

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
					lowerCamelCase     :			List[str]		=    new_scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=None		, **UpperCAmelCase_					)		->							Optional[Any]:
			if scheduler is None:
					lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
					lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			List[str]		=    scheduler_class(**UpperCAmelCase_					)

			lowerCamelCase     :			str		=    10
			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Any		=    self.dummy_sample_deter
			scheduler.set_timesteps(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    model(UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Any		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					).prev_sample

			return sample




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			List[str]		=    dict(self.forward_default_kwargs					)

			lowerCamelCase     :			Tuple		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			Optional[int]		=    self.get_scheduler_config()
					lowerCamelCase     :			Tuple		=    scheduler_class(**UpperCAmelCase_					)

					lowerCamelCase     :			Optional[int]		=    self.dummy_sample
					lowerCamelCase     :			str		=    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'					):
							lowerCamelCase     :			Dict		=    num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					lowerCamelCase     :			Optional[Any]		=    [residual + 0.2, residual + 0.15, residual + 0.10]
					lowerCamelCase     :			Optional[Any]		=    dummy_past_residuals[: scheduler.config.solver_order]

					lowerCamelCase     :			Optional[int]		=    scheduler.timesteps[5]
					lowerCamelCase     :			str		=    scheduler.timesteps[6]

					lowerCamelCase     :			Union[str, Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
					lowerCamelCase     :			Optional[Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			self.check_over_configs(thresholding=UpperCAmelCase_					)
			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=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, sample_max_value=UpperCAmelCase_		, algorithm_type='dpmsolver++'		, solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, )




	def 			_UpperCamelCase    ( self					)		->							Dict:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
															continue
											else:
													self.check_over_configs(
													    solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, algorithm_type=UpperCAmelCase_		, )
											lowerCamelCase     :			Any		=    self.full_loop(
											    solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, algorithm_type=UpperCAmelCase_		, )
											assert not torch.isnan(UpperCAmelCase_					).any(), "Samples have nan numbers"




	def 			_UpperCamelCase    ( self					)		->							Dict:
			self.check_over_configs(lower_order_final=UpperCAmelCase_					)
			self.check_over_configs(lower_order_final=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			self.check_over_configs(lambda_min_clipped=-float('inf'					)					)
			self.check_over_configs(lambda_min_clipped=-5.1					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			self.check_over_configs(variance_type=UpperCAmelCase_					)
			self.check_over_configs(variance_type='learned_range'					)




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




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Optional[Any]		=    self.full_loop()
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Tuple		=    self.full_loop(thresholding=UpperCAmelCase_		, dynamic_thresholding_ratio=0.87		, sample_max_value=0.5					)
			lowerCamelCase     :			Dict		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 1.1364					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[Any]		=    self.full_loop(prediction_type='v_prediction'					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.2251					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    self.full_loop(prediction_type='v_prediction'		, use_karras_sigmas=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.2096					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							str:
			# make sure that iterating over schedulers with same config names gives same results
			# for defaults
			lowerCamelCase     :			Optional[int]		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
			lowerCamelCase     :			str		=    self.full_loop(scheduler=UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3

			lowerCamelCase     :			List[str]		=    DPMSolverSinglestepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			str		=    UniPCMultistepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			List[Any]		=    DEISMultistepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			Dict		=    DPMSolverMultistepScheduler.from_config(scheduler.config					)

			lowerCamelCase     :			Dict		=    self.full_loop(scheduler=UpperCAmelCase_					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Optional[Any]		=    self.get_scheduler_config(thresholding=UpperCAmelCase_		, dynamic_thresholding_ratio=0					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			Optional[int]		=    self.dummy_model()
			lowerCamelCase     :			List[str]		=    self.dummy_sample_deter.half()
			scheduler.set_timesteps(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Optional[int]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					).prev_sample

			assert sample.dtype == torch.floataa




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Dict:
			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			Dict		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

					scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
					assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps









'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor
class 	SCREAMING_SNAKE_CASE__			(				unittest.TestCase      ):




    def __init__(					self				,     lowercase__				,     lowercase__=7				,     lowercase__=3				,     lowercase__=18				,     lowercase__=30				,     lowercase__=400				,     lowercase__=True				,     lowercase__=None				,     lowercase__=True				,     lowercase__=None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       size if size is not None else {"shortest_edge": 20}
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       crop_size if crop_size is not None else {"height": 18, "width": 18}
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       parent
          SCREAMING_SNAKE_CASE_      :			Tuple     =       batch_size
          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Dict     =       min_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       do_resize
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       size
          SCREAMING_SNAKE_CASE_      :			Tuple     =       do_center_crop
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       crop_size







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,unittest.TestCase      ):
    _A			    =							MobileNetVaImageProcessor if is_vision_available() else None




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       MobileNetVaImageProcessingTester(self			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.image_processor_tester.prepare_image_processor_dict()




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowercase__				,     "do_resize"			)			)
          self.assertTrue(hasattr(lowercase__				,     "size"			)			)
          self.assertTrue(hasattr(lowercase__				,     "do_center_crop"			)			)
          self.assertTrue(hasattr(lowercase__				,     "center_crop"			)			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 20}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 18, "width": 18}			)

          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 42}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 84, "width": 84}			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          pass




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     Image.Image			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_      :			Dict     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     numpify=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     np.ndarray			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Dict     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			Dict     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_      :			Any     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     torchify=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     torch.Tensor			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

