




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 DeiTImageProcessor


class        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):

					def __init__(       self				, __lowercase				, __lowercase=7				, __lowercase=3				, __lowercase=18				, __lowercase=30				, __lowercase=400				, __lowercase=True				, __lowercase=None				, __lowercase=True				, __lowercase=None				, __lowercase=True				, __lowercase=[0.5, 0.5, 0.5]				, __lowercase=[0.5, 0.5, 0.5]				, ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							size if size is not None else {'height': 20, 'width': 20}
						lowerCamelCase_							:	str							     =							crop_size if crop_size is not None else {'height': 18, 'width': 18}

						lowerCamelCase_							:	Any							     =							parent
						lowerCamelCase_							:	List[Any]							     =							batch_size
						lowerCamelCase_							:	Optional[Any]							     =							num_channels
						lowerCamelCase_							:	str							     =							image_size
						lowerCamelCase_							:	Optional[int]							     =							min_resolution
						lowerCamelCase_							:	Dict							     =							max_resolution
						lowerCamelCase_							:	Any							     =							do_resize
						lowerCamelCase_							:	List[Any]							     =							size
						lowerCamelCase_							:	List[str]							     =							do_center_crop
						lowerCamelCase_							:	List[Any]							     =							crop_size
						lowerCamelCase_							:	List[str]							     =							do_normalize
						lowerCamelCase_							:	Any							     =							image_mean
						lowerCamelCase_							:	int							     =							image_std





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				DeiTImageProcessor if is_vision_available() else None
					_lowerCamelCase							=				True

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							DeiTImageProcessingTester(self      )

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self.image_processor_tester.prepare_image_processor_dict()

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(__lowercase				, 'do_resize'      )      )
						self.assertTrue(hasattr(__lowercase				, 'size'      )      )
						self.assertTrue(hasattr(__lowercase				, 'do_center_crop'      )      )
						self.assertTrue(hasattr(__lowercase				, 'center_crop'      )      )
						self.assertTrue(hasattr(__lowercase				, 'do_normalize'      )      )
						self.assertTrue(hasattr(__lowercase				, 'image_mean'      )      )
						self.assertTrue(hasattr(__lowercase				, 'image_std'      )      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

						lowerCamelCase_							:	Any							     =							self.image_processing_class.from_dict(self.image_processor_dict				, size=42				, crop_size=84      )
						self.assertEqual(image_processor.size				, {'height': 42, 'width': 42}      )
						self.assertEqual(image_processor.crop_size				, {'height': 84, 'width': 84}      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						pass

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Tuple							     =							self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						lowerCamelCase_							:	Optional[int]							     =							prepare_image_inputs(self.image_processor_tester				, equal_resolution=__lowercase      )
						for image in image_inputs:
							self.assertIsInstance(__lowercase				, Image.Image      )

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

						# Test batched
						lowerCamelCase_							:	Any							     =							image_processing(__lowercase				, 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 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						lowerCamelCase_							:	Union[str, Any]							     =							prepare_image_inputs(self.image_processor_tester				, equal_resolution=__lowercase				, numpify=__lowercase      )
						for image in image_inputs:
							self.assertIsInstance(__lowercase				, np.ndarray      )

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

						# Test batched
						lowerCamelCase_							:	str							     =							image_processing(__lowercase				, 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 			lowercase_							(       self      ):







						'''simple docstring'''




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

						# Test not batched input
						lowerCamelCase_							:	List[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
						lowerCamelCase_							:	Optional[int]							     =							image_processing(__lowercase				, 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'],
						    )				, )






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				(DDIMScheduler,)
					_lowerCamelCase							=				(("""eta""", 0.0), ("""num_inference_steps""", 50))

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							{
						    'num_train_timesteps': 1_000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__lowercase      )
						return config

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[Any]							     =							self.get_scheduler_config(**__lowercase      )
						lowerCamelCase_							:	Tuple							     =							scheduler_class(**__lowercase      )

						lowerCamelCase_     ,    lowerCamelCase_							:	Dict							     =							10, 0.0

						lowerCamelCase_							:	Optional[int]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter

						scheduler.set_timesteps(__lowercase      )

						for t in scheduler.timesteps:
							lowerCamelCase_							:	Any							     =							model(__lowercase				, __lowercase      )
							lowerCamelCase_							:	Optional[Any]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase				, __lowercase      ).prev_sample

						return sample

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timesteps in [100, 500, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[str]							     =							self.get_scheduler_config(steps_offset=1      )
						lowerCamelCase_							:	int							     =							scheduler_class(**__lowercase      )
						scheduler.set_timesteps(5      )
						assert torch.equal(scheduler.timesteps				, torch.LongTensor([801, 601, 401, 201, 1]      )      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				, [0.0_02, 0.02, 0.2, 2]      ):
							self.check_over_configs(beta_start=__lowercase				, beta_end=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.check_over_configs(thresholding=__lowercase      )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=__lowercase				, prediction_type=__lowercase				, sample_max_value=__lowercase				, )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t in [1, 10, 49]:
							self.check_over_forward(time_step=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, num_inference_steps in zip([1, 10, 50]				, [10, 50, 500]      ):
							self.check_over_forward(time_step=__lowercase				, num_inference_steps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, eta in zip([1, 10, 49]				, [0.0, 0.5, 1.0]      ):
							self.check_over_forward(time_step=__lowercase				, eta=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Dict							     =							self.get_scheduler_config()
						lowerCamelCase_							:	List[str]							     =							scheduler_class(**__lowercase      )

						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420				, 400      ) - 0.1_47_71      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980				, 960      ) - 0.3_24_60      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487				, 486      ) - 0.0_09_79      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999				, 998      ) - 0.02      )      ) < 1E-5

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.full_loop()

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	str							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.full_loop(prediction_type='v_prediction'      )

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	List[str]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 52.53_02      ) < 1E-2
						assert abs(result_mean.item() - 0.06_84      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[Any]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Dict							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
						assert abs(result_mean.item() - 0.19_51      ) < 1E-3





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[int]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Optional[int]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
						assert abs(result_mean.item() - 0.19_41      ) < 1E-3
