





"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			__lowercase     (     _UpperCamelCase							):







     '''simple docstring'''




     __lowerCAmelCase					=					(EulerDiscreteScheduler,)
     __lowerCAmelCase					=					10







     def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
         __a	:  Optional[Any]         =					{
             '''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 	_lowerCamelCase						(  self       ):
         for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase       )







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







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







     def 	_lowerCamelCase						(  self       ):
         __a	:  Optional[Any]         =					self.scheduler_classes[0]
         __a	:  str         =					self.get_scheduler_config()
         __a	:  int         =					scheduler_class(**_UpperCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         __a	:  int         =					torch.manual_seed(0       )

         __a	:  Any         =					self.dummy_model()
         __a	:  Optional[int]         =					self.dummy_sample_deter * scheduler.init_noise_sigma
         __a	:  List[str]         =					sample.to(_UpperCAmelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
             __a	:  Dict         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  Optional[int]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  Optional[Any]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
             __a	:  Optional[Any]         =					output.prev_sample

         __a	:  int         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
         __a	:  List[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

         assert abs(result_sum.item() - 10.0807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3







     def 	_lowerCamelCase						(  self       ):
         __a	:  Union[str, Any]         =					self.scheduler_classes[0]
         __a	:  Union[str, Any]         =					self.get_scheduler_config(prediction_type='''v_prediction'''       )
         __a	:  List[str]         =					scheduler_class(**_UpperCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         __a	:  Tuple         =					torch.manual_seed(0       )

         __a	:  str         =					self.dummy_model()
         __a	:  Dict         =					self.dummy_sample_deter * scheduler.init_noise_sigma
         __a	:  Any         =					sample.to(_UpperCAmelCase       )

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

             __a	:  Any         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

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

         __a	:  Tuple         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
         __a	:  Optional[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

         assert abs(result_sum.item() - 0.0_0_0_2       ) < 1e-2
         assert abs(result_mean.item() - 2.2_6_7_6e-0_6       ) < 1e-3







     def 	_lowerCamelCase						(  self       ):
         __a	:  List[Any]         =					self.scheduler_classes[0]
         __a	:  List[str]         =					self.get_scheduler_config()
         __a	:  Union[str, Any]         =					scheduler_class(**_UpperCAmelCase       )

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

         __a	:  Dict         =					torch.manual_seed(0       )

         __a	:  Dict         =					self.dummy_model()
         __a	:  str         =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __a	:  Any         =					sample.to(_UpperCAmelCase       )

         for t in scheduler.timesteps:
             __a	:  Tuple         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  str         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  Optional[int]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
             __a	:  Any         =					output.prev_sample

         __a	:  List[str]         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
         __a	:  Any         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

         assert abs(result_sum.item() - 10.0807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3







     def 	_lowerCamelCase						(  self       ):
         __a	:  Tuple         =					self.scheduler_classes[0]
         __a	:  Optional[int]         =					self.get_scheduler_config()
         __a	:  List[Any]         =					scheduler_class(**_UpperCAmelCase		,     use_karras_sigmas=_UpperCAmelCase       )

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

         __a	:  Tuple         =					torch.manual_seed(0       )

         __a	:  Optional[int]         =					self.dummy_model()
         __a	:  Any         =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __a	:  List[Any]         =					sample.to(_UpperCAmelCase       )

         for t in scheduler.timesteps:
             __a	:  List[str]         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  Optional[Any]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

             __a	:  Union[str, Any]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
             __a	:  Optional[int]         =					output.prev_sample

         __a	:  Any         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
         __a	:  List[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

         assert abs(result_sum.item() - 124.52299499511719       ) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 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 ViTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=3		,     _UpperCAmelCase=224		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     ):
									__a	:  int         =					size if size is not None else {'''height''': 18, '''width''': 18}
									__a	:  List[Any]         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Dict         =					num_channels
									__a	:  int         =					image_size
									__a	:  Optional[Any]         =					min_resolution
									__a	:  Optional[int]         =					max_resolution
									__a	:  Dict         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  int         =					do_normalize
									__a	:  Optional[Any]         =					image_mean
									__a	:  int         =					image_std







					def 	_lowerCamelCase						(  self       ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}






@require_torch
@require_vision
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					ViTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					EfficientFormerImageProcessorTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_proc_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_mean'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_std'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_normalize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Dict         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Any         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     numpify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  Optional[int]         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  List[Any]         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     torchify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  str         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Tuple         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )