

'''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 						__magic_name__   (							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  , ):
   lowercase									=size if size is not None else {'''shortest_edge''': 20}
   lowercase									=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
   lowercase									=parent
   lowercase									=batch_size
   lowercase									=num_channels
   lowercase									=image_size
   lowercase									=min_resolution
   lowercase									=max_resolution
   lowercase									=do_resize
   lowercase									=size
   lowercase									=do_center_crop
   lowercase									=crop_size







 def 						_A(       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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 UpperCamelCase__     =       MobileNetVaImageProcessor if is_vision_available() else None







 def 						_A(       self   ):
   lowercase									=MobileNetVaImageProcessingTester(self   )







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







 def 						_A(       self   ):
   lowercase									=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_  , '''crop_size'''   )   )







 def 						_A(       self   ):
   lowercase									=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}   )

   lowercase									=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   ):
   pass







 def 						_A(       self   ):
   # Initialize image_processing
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random PIL images
   lowercase									=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
   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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )  , )

   # Test batched
   lowercase									=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 						_A(       self   ):
   # Initialize image_processing
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random numpy tensors
   lowercase									=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
   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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )  , )

   # Test batched
   lowercase									=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 						_A(       self   ):
   # Initialize image_processing
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random PyTorch tensors
   lowercase									=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
   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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )  , )

   # Test batched
   lowercase									=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'''],
       )  , )


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self				)						->    Any:
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

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

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

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

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A						,       use_karras_sigmas=__A				)

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

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
