
"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		(DPMSolverSinglestepScheduler,)
       UpperCamelCase_ =		(('''num_inference_steps''', 25),)
       def 				lowercase_				(  self			,					**A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Any   			=  {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.00_01,
             '''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(**A_							)
         return config
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Dict   			=  dict(self.forward_default_kwargs							)
         _lowercase:      List[str]   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      Tuple   			=  self.dummy_sample
         _lowercase:      List[Any]   			=  0.1 * sample
         _lowercase:      Any   			=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A_							)
             _lowercase:      Union[str, Any]   			=  scheduler_class.from_pretrained(A_							)
             new_scheduler.set_timesteps(A_							)
             # copy over dummy past residuals
             _lowercase:      int   			=  dummy_past_residuals[: new_scheduler.config.solver_order]

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

             assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  dict(self.forward_default_kwargs							)
         _lowercase:      Dict   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      Union[str, Any]   			=  self.dummy_sample
         _lowercase:      int   			=  0.1 * sample
         _lowercase:      Dict   			=  [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Tuple   			=  self.get_scheduler_config()
           _lowercase:      Union[str, Any]   			=  scheduler_class(**A_							)
           scheduler.set_timesteps(A_							)

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

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

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

           _lowercase:      int   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      int   			=  new_scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self			,					A_=None			,					**A_							)  ->						Optional[int]:





         """simple docstring"""




         if scheduler is None:
           _lowercase:      Tuple   			=  self.scheduler_classes[0]
           _lowercase:      str   			=  self.get_scheduler_config(**A_							)
           _lowercase:      Optional[Any]   			=  scheduler_class(**A_							)

         _lowercase:      List[str]   			=  self.scheduler_classes[0]
         _lowercase:      Optional[Any]   			=  self.get_scheduler_config(**A_							)
         _lowercase:      str   			=  scheduler_class(**A_							)

         _lowercase:      Any   			=  10
         _lowercase:      str   			=  self.dummy_model()
         _lowercase:      Tuple   			=  self.dummy_sample_deter
         scheduler.set_timesteps(A_							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Any   			=  model(A_			,					A_							)
           _lowercase:      str   			=  scheduler.step(A_			,					A_			,					A_							).prev_sample

         return sample
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Tuple   			=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _lowercase:      List[str]   			=  50
         _lowercase:      List[str]   			=  self.dummy_model()
         _lowercase:      Optional[int]   			=  self.dummy_sample_deter
         scheduler.set_timesteps(A_							)

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

         _lowercase:      Any   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.25_74							) < 1E-3
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _lowercase:      List[Any]   			=  self.full_loop(scheduler=A_							)
         _lowercase:      Optional[Any]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.27_91							) < 1E-3

         _lowercase:      Optional[Any]   			=  DEISMultistepScheduler.from_config(scheduler.config							)
         _lowercase:      Optional[int]   			=  DPMSolverMultistepScheduler.from_config(scheduler.config							)
         _lowercase:      Dict   			=  UniPCMultistepScheduler.from_config(scheduler.config							)
         _lowercase:      Optional[Any]   			=  DPMSolverSinglestepScheduler.from_config(scheduler.config							)

         _lowercase:      List[Any]   			=  self.full_loop(scheduler=A_							)
         _lowercase:      List[Any]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.27_91							) < 1E-3
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         self.check_over_configs(thresholding=A_							)
         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=A_			,					prediction_type=A_			,					sample_max_value=A_			,					algorithm_type='''dpmsolver++'''			,					solver_order=A_			,					solver_type=A_			,					)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         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=A_			,					solver_type=A_			,					prediction_type=A_			,					algorithm_type=A_			,					)
                 _lowercase:      Tuple   			=  self.full_loop(
                     solver_order=A_			,					solver_type=A_			,					prediction_type=A_			,					algorithm_type=A_			,					)
                 assert not torch.isnan(A_							).any(), "Samples have nan numbers"
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         self.check_over_configs(lower_order_final=A_							)
         self.check_over_configs(lower_order_final=A_							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         self.check_over_configs(lambda_min_clipped=-float('''inf'''							)							)
         self.check_over_configs(lambda_min_clipped=-5.1							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         self.check_over_configs(variance_type=A_							)
         self.check_over_configs(variance_type='''learned_range'''							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.full_loop()
         _lowercase:      Optional[Any]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.27_91							) < 1E-3
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.full_loop(use_karras_sigmas=A_							)
         _lowercase:      List[str]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.22_48							) < 1E-3
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.full_loop(prediction_type='''v_prediction'''							)
         _lowercase:      Any   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.14_53							) < 1E-3
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      str   			=  self.full_loop(prediction_type='''v_prediction'''			,					use_karras_sigmas=A_							)
         _lowercase:      Optional[int]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 0.06_49							) < 1E-3



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Dict   			=  self.scheduler_classes[0]
         _lowercase:      Tuple   			=  self.get_scheduler_config(thresholding=A_			,					dynamic_thresholding_ratio=0							)
         _lowercase:      Optional[Any]   			=  scheduler_class(**A_							)

         _lowercase:      str   			=  10
         _lowercase:      List[Any]   			=  self.dummy_model()
         _lowercase:      List[str]   			=  self.dummy_sample_deter.half()
         scheduler.set_timesteps(A_							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Tuple   			=  model(A_			,					A_							)
           _lowercase:      List[Any]   			=  scheduler.step(A_			,					A_			,					A_							).prev_sample

         assert sample.dtype == torch.floataa


"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=18			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=False			,					A_=True			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



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





         """simple docstring"""




         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_thumbnail'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_align_long_axis'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_pad'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=42							)
         self.assertEqual(image_processor.size			,					{'''height''': 42, '''width''': 42}							)

         # Previous config had dimensions in (width, height) order
         _lowercase:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      Dict   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					Image.Image							)

         # Test not batched input
         _lowercase:      Tuple   			=  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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      List[Any]   			=  image_processing(A_			,					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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
       @is_flaky()
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					numpify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					np.ndarray							)

         # Test not batched input
         _lowercase:      Union[str, Any]   			=  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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      Dict   			=  image_processing(A_			,					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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)



       @is_flaky()
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




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

         # Test not batched input
         _lowercase:      Union[str, Any]   			=  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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      Dict   			=  image_processing(A_			,					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.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
