

'''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  _snake_case						(unittest.TestCase):






			def __init__(   self       ,_snake_case       ,_snake_case=7       ,_snake_case=3       ,_snake_case=18       ,_snake_case=30       ,_snake_case=4_00       ,_snake_case=True       ,_snake_case=None       ,_snake_case=True       ,_snake_case=None       ,):
				UpperCAmelCase_			:	Optional[Any]      =   size if size is not None else {"shortest_edge": 20}
				UpperCAmelCase_			:	Dict      =   crop_size if crop_size is not None else {"height": 18, "width": 18}
				UpperCAmelCase_			:	int      =   parent
				UpperCAmelCase_			:	List[str]      =   batch_size
				UpperCAmelCase_			:	Optional[int]      =   num_channels
				UpperCAmelCase_			:	List[str]      =   image_size
				UpperCAmelCase_			:	List[str]      =   min_resolution
				UpperCAmelCase_			:	Optional[Any]      =   max_resolution
				UpperCAmelCase_			:	Any      =   do_resize
				UpperCAmelCase_			:	Optional[int]      =   size
				UpperCAmelCase_			:	Tuple      =   do_center_crop
				UpperCAmelCase_			:	Optional[int]      =   crop_size






			def 						UpperCamelCase__				(   self			):
				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  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     Optional[Any]          =MobileNetVaImageProcessor if is_vision_available() else None






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	int      =   MobileNetVaImageProcessingTester(self			)






			@property
			def 						UpperCamelCase__				(   self			):
				return self.image_processor_tester.prepare_image_processor_dict()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   self.image_processing_class(**self.image_processor_dict			)
				self.assertTrue(hasattr(_snake_case       ,"do_resize"			)			)
				self.assertTrue(hasattr(_snake_case       ,"size"			)			)
				self.assertTrue(hasattr(_snake_case       ,"do_center_crop"			)			)
				self.assertTrue(hasattr(_snake_case       ,"center_crop"			)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[str]      =   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_			:	Optional[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			):
				pass






			def 						UpperCamelCase__				(   self			):
				# Initialize image_processing
				UpperCAmelCase_			:	int      =   self.image_processing_class(**self.image_processor_dict			)
				# create random PIL images
				UpperCAmelCase_			:	List[str]      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,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_			:	Any      =   image_processing(_snake_case       ,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			):
				# Initialize image_processing
				UpperCAmelCase_			:	List[str]      =   self.image_processing_class(**self.image_processor_dict			)
				# create random numpy tensors
				UpperCAmelCase_			:	str      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case       ,numpify=_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,np.ndarray			)

				# Test not batched input
				UpperCAmelCase_			:	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_			:	Any      =   image_processing(_snake_case       ,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			):
				# Initialize image_processing
				UpperCAmelCase_			:	Optional[int]      =   self.image_processing_class(**self.image_processor_dict			)
				# create random PyTorch tensors
				UpperCAmelCase_			:	List[str]      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case       ,torchify=_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,torch.Tensor			)

				# 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_			:	int      =   image_processing(_snake_case       ,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"],
				    )       ,)







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






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






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




