'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			(DPMSolverMultistepScheduler,)
			UpperCamelCase__							=			(('num_inference_steps', 25),)







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     **lowercase_							:    int       ):
										lowercase_   :     int									=			{
										    """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""",
										    """lower_order_final""": False,
										    """lambda_min_clipped""": -float("""inf"""       ),
										    """variance_type""": None,
										}

										config.update(**lowercase_       )
										return config







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     lowercase_							:    List[str]=0							,     **lowercase_							:    Tuple       ):
										lowercase_   :     Optional[int]									=			dict(self.forward_default_kwargs       )
										lowercase_   :     Union[str, Any]									=			kwargs.pop("""num_inference_steps"""							,     lowercase_       )
										lowercase_   :     str									=			self.dummy_sample
										lowercase_   :     List[Any]									=			0.1 * sample
										lowercase_   :     int									=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																	lowercase_		,		lowercase_   :     int									=			sample, sample
																	for t in range(lowercase_							,     time_step + scheduler.config.solver_order + 1       ):
																								lowercase_   :     int									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample
																								lowercase_   :     List[str]									=			new_scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										pass







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    Union[str, Any]=0							,     **lowercase_							:    Any       ):
										lowercase_   :     Optional[int]									=			dict(self.forward_default_kwargs       )
										lowercase_   :     Tuple									=			kwargs.pop("""num_inference_steps"""							,     lowercase_       )
										lowercase_   :     Union[str, Any]									=			self.dummy_sample
										lowercase_   :     Optional[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()
																	lowercase_   :     List[str]									=			scheduler_class(**lowercase_       )
																	scheduler.set_timesteps(lowercase_       )

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_       )
																								lowercase_   :     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)
																								lowercase_   :     int									=			dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase_   :     Optional[Any]									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample
																	lowercase_   :     Dict									=			new_scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Optional[int]=None							,     **lowercase_							:    Optional[Any]       ):






										if scheduler is None:
																	lowercase_   :     List[Any]									=			self.scheduler_classes[0]
																	lowercase_   :     List[Any]									=			self.get_scheduler_config(**lowercase_       )
																	lowercase_   :     Union[str, Any]									=			scheduler_class(**lowercase_       )

										lowercase_   :     Dict									=			10
										lowercase_   :     Dict									=			self.dummy_model()
										lowercase_   :     Optional[Any]									=			self.dummy_sample_deter
										scheduler.set_timesteps(lowercase_       )

										for i, t in enumerate(scheduler.timesteps       ):
																	lowercase_   :     Optional[int]									=			model(lowercase_							,     lowercase_       )
																	lowercase_   :     Optional[Any]									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_       ).prev_sample

										return sample







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     str									=			dict(self.forward_default_kwargs       )

										lowercase_   :     Optional[int]									=			kwargs.pop("""num_inference_steps"""							,     lowercase_       )

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

																	lowercase_   :     Optional[int]									=			self.dummy_sample
																	lowercase_   :     Optional[Any]									=			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"""       ):
																								lowercase_   :     Union[str, Any]									=			num_inference_steps

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

																	lowercase_   :     Optional[int]									=			scheduler.timesteps[5]
																	lowercase_   :     Union[str, Any]									=			scheduler.timesteps[6]

																	lowercase_   :     List[Any]									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample
																	lowercase_   :     List[Any]									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_							,     **lowercase_       ).prev_sample

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):






										for timesteps in [25, 50, 100, 999, 1000]:
																	self.check_over_configs(num_train_timesteps=lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										self.check_over_configs(thresholding=lowercase_       )
										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=lowercase_							,     prediction_type=lowercase_							,     sample_max_value=lowercase_							,     algorithm_type="""dpmsolver++"""							,     solver_order=lowercase_							,     solver_type=lowercase_							,     )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):






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







			def       SCREAMING_SNAKE_CASE_	(      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=lowercase_							,     solver_type=lowercase_							,     prediction_type=lowercase_							,     algorithm_type=lowercase_							,     )
																																						lowercase_   :     int									=			self.full_loop(
																																						    solver_order=lowercase_							,     solver_type=lowercase_							,     prediction_type=lowercase_							,     algorithm_type=lowercase_							,     )
																																						assert not torch.isnan(lowercase_       ).any(), "Samples have nan numbers"







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										self.check_over_configs(lower_order_final=lowercase_       )
										self.check_over_configs(lower_order_final=lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										self.check_over_configs(lambda_min_clipped=-float("""inf"""       )       )
										self.check_over_configs(lambda_min_clipped=-5.1       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										self.check_over_configs(variance_type=lowercase_       )
										self.check_over_configs(variance_type="""learned_range"""       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):






										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																	self.check_over_forward(num_inference_steps=lowercase_							,     time_step=0       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_   :     Union[str, Any]									=			self.full_loop()
										lowercase_   :     Any									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 0.33_01       ) < 1E-3







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     Optional[Any]									=			self.full_loop(thresholding=lowercase_							,     dynamic_thresholding_ratio=0.87							,     sample_max_value=0.5       )
										lowercase_   :     str									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 1.13_64       ) < 1E-3







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     List[str]									=			self.full_loop(prediction_type="""v_prediction"""       )
										lowercase_   :     str									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 0.22_51       ) < 1E-3







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     List[str]									=			self.full_loop(prediction_type="""v_prediction"""							,     use_karras_sigmas=lowercase_       )
										lowercase_   :     str									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 0.20_96       ) < 1E-3







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     Any									=			DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
										lowercase_   :     Optional[int]									=			self.full_loop(scheduler=lowercase_       )
										lowercase_   :     str									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 0.33_01       ) < 1E-3

										lowercase_   :     Optional[int]									=			DPMSolverSinglestepScheduler.from_config(scheduler.config       )
										lowercase_   :     List[str]									=			UniPCMultistepScheduler.from_config(scheduler.config       )
										lowercase_   :     Dict									=			DEISMultistepScheduler.from_config(scheduler.config       )
										lowercase_   :     Dict									=			DPMSolverMultistepScheduler.from_config(scheduler.config       )

										lowercase_   :     Union[str, Any]									=			self.full_loop(scheduler=lowercase_       )
										lowercase_   :     int									=			torch.mean(torch.abs(lowercase_       )       )

										assert abs(result_mean.item() - 0.33_01       ) < 1E-3







			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     int									=			self.scheduler_classes[0]
										lowercase_   :     int									=			self.get_scheduler_config(thresholding=lowercase_							,     dynamic_thresholding_ratio=0       )
										lowercase_   :     List[Any]									=			scheduler_class(**lowercase_       )

										lowercase_   :     Union[str, Any]									=			10
										lowercase_   :     str									=			self.dummy_model()
										lowercase_   :     List[Any]									=			self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowercase_       )

										for i, t in enumerate(scheduler.timesteps       ):
																	lowercase_   :     Optional[Any]									=			model(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			scheduler.step(lowercase_							,     lowercase_							,     lowercase_       ).prev_sample

										assert sample.dtype == torch.floataa







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     **lowercase_							:    int       ):






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

																	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					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[Any]=7       ,    __magic_name__ :int=3       ,    __magic_name__ :Union[str, Any]=18       ,    __magic_name__ :Union[str, Any]=30       ,    __magic_name__ :str=400       ,    __magic_name__ :str=True       ,    __magic_name__ :Any=None       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[str]=None       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''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       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


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

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     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}			)

			a__   		=     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    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    torchify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    torch.Tensor			)

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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'''],
			    )       ,    )




