'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						(UnCLIPScheduler,)




      def      _A	(       self						:				List[Any]						,					**A						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    {
                 "num_train_timesteps": 1000,
                 "variance_type": "fixed_small_log",
                 "clip_sample": True,
                 "clip_sample_range": 1.0,
                 "prediction_type": "epsilon",
             }

             config.update(**A       )
             return config




      def      _A	(       self						:				Any       ):
             for timesteps in [1, 5, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=A       )




      def      _A	(       self						:				Any       ):
             for variance in ["fixed_small_log", "learned_range"]:
                    self.check_over_configs(variance_type=A       )




      def      _A	(       self						:				Tuple       ):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=A       )




      def      _A	(       self						:				Optional[Any]       ):
             for clip_sample_range in [1, 5, 10, 20]:
                    self.check_over_configs(clip_sample_range=A       )




      def      _A	(       self						:				Any       ):
             for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(prediction_type=A       )




      def      _A	(       self						:				Dict       ):
             for time_step in [0, 500, 999]:
                    for prev_timestep in [None, 5, 100, 250, 500, 750]:
                           if prev_timestep is not None and prev_timestep >= time_step:
                                  continue

                           self.check_over_forward(time_step=A						,					prev_timestep=A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_scheduler_config(variance_type="fixed_small_log"       )
             _UpperCAmelCase     :  Any							      =    scheduler_class(**A       )

             assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.00_00E-10       )       ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1E-5




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Tuple							      =    self.get_scheduler_config(variance_type="learned_range"       )
             _UpperCAmelCase     :  int							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  int							      =    0.5

             assert scheduler._get_variance(1						,					predicted_variance=A       ) - -10.1_712_790 < 1E-5
             assert scheduler._get_variance(487						,					predicted_variance=A       ) - -5.7_998_052 < 1E-5
             assert scheduler._get_variance(999						,					predicted_variance=A       ) - -0.0_010_011 < 1E-5




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Union[str, Any]							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    scheduler.timesteps

             _UpperCAmelCase     :  int							      =    self.dummy_model()
             _UpperCAmelCase     :  Optional[int]							      =    self.dummy_sample_deter
             _UpperCAmelCase     :  Tuple							      =    torch.manual_seed(0       )

             for i, t in enumerate(A       ):
                    # 1. predict noise residual
                    _UpperCAmelCase     :  Dict							      =    model(A						,					A       )

                    # 2. predict previous mean of sample x_t-1
                    _UpperCAmelCase     :  List[str]							      =    scheduler.step(A						,					A						,					A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  str							      =    pred_prev_sample

             _UpperCAmelCase     :  Union[str, Any]							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  List[str]							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 252.2_682_495       ) < 1E-2
             assert abs(result_mean.item() - 0.3_284_743       ) < 1E-3




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Dict							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Dict							      =    scheduler_class(**A       )

             scheduler.set_timesteps(25       )

             _UpperCAmelCase     :  List[str]							      =    scheduler.timesteps

             _UpperCAmelCase     :  Union[str, Any]							      =    self.dummy_model()
             _UpperCAmelCase     :  List[str]							      =    self.dummy_sample_deter
             _UpperCAmelCase     :  Tuple							      =    torch.manual_seed(0       )

             for i, t in enumerate(A       ):
                    # 1. predict noise residual
                    _UpperCAmelCase     :  Any							      =    model(A						,					A       )

                    if i + 1 == timesteps.shape[0]:
                           _UpperCAmelCase     :  List[Any]							      =    None
                    else:
                           _UpperCAmelCase     :  Any							      =    timesteps[i + 1]

                    # 2. predict previous mean of sample x_t-1
                    _UpperCAmelCase     :  Union[str, Any]							      =    scheduler.step(
                        A						,					A						,					A						,					prev_timestep=A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  List[Any]							      =    pred_prev_sample

             _UpperCAmelCase     :  Optional[Any]							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  int							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 258.2_044_983       ) < 1E-2
             assert abs(result_mean.item() - 0.3_362_038       ) < 1E-3




      def      _A	(       self						:				Dict       ):
             pass





      def      _A	(       self						:				int       ):
             pass


'''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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             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 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _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     :  Dict							      =    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      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[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     :  Optional[Any]							      =    image_processing(A						,					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      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					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     :  Optional[int]							      =    image_processing(A						,					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      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  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
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					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"],
                 )						,					)

