



'''simple docstring'''
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							lowercase__     (							__lowercase       ):
	lowercase__      =      (KDPMaDiscreteScheduler,)
	lowercase__      =      10





	def      UpperCamelCase_  (			self :    Tuple			,**lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       {
							    'num_train_timesteps': 1100,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							}

							config.update(**UpperCAmelCase__			)
							return config





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=UpperCAmelCase__			)





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=UpperCAmelCase__			,beta_end=UpperCAmelCase__			)





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=UpperCAmelCase__			)





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCAmelCase__			)





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       self.scheduler_classes[0]
							_UpperCamelCase :		Optional[int]			    =       self.get_scheduler_config(prediction_type='v_prediction'			)
							_UpperCamelCase :		str			    =       scheduler_class(**UpperCAmelCase__			)

							scheduler.set_timesteps(self.num_inference_steps			)

							_UpperCamelCase :		Dict			    =       self.dummy_model()
							_UpperCamelCase :		Dict			    =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase :		int			    =       sample.to(UpperCAmelCase__			)

							for i, t in enumerate(scheduler.timesteps			):
													_UpperCamelCase :		Optional[int]			    =       scheduler.scale_model_input(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		List[Any]			    =       model(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		str			    =       scheduler.step(UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__			)
													_UpperCamelCase :		str			    =       output.prev_sample

							_UpperCamelCase :		List[str]			    =       torch.sum(torch.abs(UpperCAmelCase__			)			)
							_UpperCamelCase :		List[Any]			    =       torch.mean(torch.abs(UpperCAmelCase__			)			)

							if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
													assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
													assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							if torch_device == "mps":
													return
							_UpperCamelCase :		int			    =       self.scheduler_classes[0]
							_UpperCamelCase :		Union[str, Any]			    =       self.get_scheduler_config()
							_UpperCamelCase :		List[Any]			    =       scheduler_class(**UpperCAmelCase__			)

							scheduler.set_timesteps(self.num_inference_steps			)

							_UpperCamelCase :		Dict			    =       self.dummy_model()
							_UpperCamelCase :		str			    =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase :		Tuple			    =       sample.to(UpperCAmelCase__			)

							for i, t in enumerate(scheduler.timesteps			):
													_UpperCamelCase :		List[Any]			    =       scheduler.scale_model_input(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		List[Any]			    =       model(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		Union[str, Any]			    =       scheduler.step(UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__			)
													_UpperCamelCase :		int			    =       output.prev_sample

							_UpperCamelCase :		Dict			    =       torch.sum(torch.abs(UpperCAmelCase__			)			)
							_UpperCamelCase :		Optional[Any]			    =       torch.mean(torch.abs(UpperCAmelCase__			)			)

							if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1E-2
													assert abs(result_mean.item() - 0.0_2_6_6			) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1E-2
													assert abs(result_mean.item() - 0.0_2_6_6			) < 1E-3





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							if torch_device == "mps":
													return
							_UpperCamelCase :		Any			    =       self.scheduler_classes[0]
							_UpperCamelCase :		str			    =       self.get_scheduler_config()
							_UpperCamelCase :		Tuple			    =       scheduler_class(**UpperCAmelCase__			)

							scheduler.set_timesteps(self.num_inference_steps			,device=UpperCAmelCase__			)

							_UpperCamelCase :		List[Any]			    =       self.dummy_model()
							_UpperCamelCase :		List[Any]			    =       self.dummy_sample_deter.to(UpperCAmelCase__			) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
													_UpperCamelCase :		Optional[Any]			    =       scheduler.scale_model_input(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		List[str]			    =       model(UpperCAmelCase__			,UpperCAmelCase__			)

													_UpperCamelCase :		Optional[Any]			    =       scheduler.step(UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__			)
													_UpperCamelCase :		int			    =       output.prev_sample

							_UpperCamelCase :		List[str]			    =       torch.sum(torch.abs(UpperCAmelCase__			)			)
							_UpperCamelCase :		List[Any]			    =       torch.mean(torch.abs(UpperCAmelCase__			)			)

							if str(UpperCAmelCase__			).startswith('cpu'			):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1E-2
													assert abs(result_mean.item() - 0.0_2_6_6			) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1E-2
													assert abs(result_mean.item() - 0.0_2_6_6			) < 1E-3










'''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 MobileViTImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





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







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       size if size is not None else {'shortest_edge': 20}
							_UpperCamelCase :		int			    =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase :		List[str]			    =       parent
							_UpperCamelCase :		List[Any]			    =       batch_size
							_UpperCamelCase :		int			    =       num_channels
							_UpperCamelCase :		Optional[int]			    =       image_size
							_UpperCamelCase :		List[Any]			    =       min_resolution
							_UpperCamelCase :		List[str]			    =       max_resolution
							_UpperCamelCase :		int			    =       do_resize
							_UpperCamelCase :		Optional[int]			    =       size
							_UpperCamelCase :		str			    =       do_center_crop
							_UpperCamelCase :		List[Any]			    =       crop_size
							_UpperCamelCase :		Optional[Any]			    =       do_flip_channel_order





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}






@require_torch
@require_vision
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      MobileViTImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       MobileViTImageProcessingTester(self			)





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_resize'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'size'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_flip_channel_order'			)			)





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       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}			)

							_UpperCamelCase :		Union[str, Any]			    =       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 :    List[Any]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_UpperCamelCase :		int			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,Image.Image			)

							# Test not batched input
							_UpperCamelCase :		Optional[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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		int			    =       image_processing(lowerCamelCase__			,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 :    Dict			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_UpperCamelCase :		List[Any]			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			,numpify=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,np.ndarray			)

							# Test not batched input
							_UpperCamelCase :		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
							_UpperCamelCase :		List[str]			    =       image_processing(lowerCamelCase__			,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]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		Optional[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_UpperCamelCase :		str			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			,torchify=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,torch.Tensor			)

							# Test not batched input
							_UpperCamelCase :		str			    =       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
							_UpperCamelCase :		Optional[int]			    =       image_processing(lowerCamelCase__			,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'],
							    )			,)


