

'''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 ConvNextImageProcessor


class 					lowerCAmelCase       ( unittest.TestCase				):







       def __init__(							self     :	Tuple  , __lowercase     :	Optional[Any]  , __lowercase     :	Optional[int]=7  , __lowercase     :	Optional[Any]=3  , __lowercase     :	Union[str, Any]=18  , __lowercase     :	Any=30  , __lowercase     :	str=400  , __lowercase     :	Any=True  , __lowercase     :	Optional[int]=None  , __lowercase     :	List[Any]=0.8_7_5  , __lowercase     :	Any=True  , __lowercase     :	Optional[int]=[0.5, 0.5, 0.5]  , __lowercase     :	Optional[Any]=[0.5, 0.5, 0.5]  , ):







            """simple docstring"""







            __lowercase     						=size if size is not None else {'shortest_edge': 20}
            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=num_channels
            __lowercase     						=image_size
            __lowercase     						=min_resolution
            __lowercase     						=max_resolution
            __lowercase     						=do_resize
            __lowercase     						=size
            __lowercase     						=crop_pct
            __lowercase     						=do_normalize
            __lowercase     						=image_mean
            __lowercase     						=image_std







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







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






@require_torch
@require_vision
class 					lowerCAmelCase       ( A				,     unittest.TestCase				):
       lowerCAmelCase_				=	ConvNextImageProcessor if is_vision_available() else None







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=ConvNextImageProcessingTester(self )







       @property
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(__lowercase  , 'do_resize' ) )
            self.assertTrue(hasattr(__lowercase  , 'size' ) )
            self.assertTrue(hasattr(__lowercase  , 'crop_pct' ) )
            self.assertTrue(hasattr(__lowercase  , 'do_normalize' ) )
            self.assertTrue(hasattr(__lowercase  , 'image_mean' ) )
            self.assertTrue(hasattr(__lowercase  , 'image_std' ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size  , {'shortest_edge': 20} )

            __lowercase     						=self.image_processing_class.from_dict(self.image_processor_dict  , size=42 )
            self.assertEqual(image_processor.size  , {'shortest_edge': 42} )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            pass







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







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

            # Test not batched input
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            __lowercase     						=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
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            __lowercase     						=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
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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.size['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







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

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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







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







       def      snake_case				(							self     :	int ):







            """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      snake_case				(							self     :	Dict ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """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      snake_case				(							self     :	Tuple ):







            """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      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=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_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 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_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


