





'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _A (					__lowercase		):
		lowercase__:   Any				     =   (EulerDiscreteScheduler,)
		lowercase__:   Optional[Any]				     =   10

		def 	lowercase__     (   self					:   str  ,      **__magic_name__					:   str			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     {
					    """num_train_timesteps""": 11_00,
					    """beta_start""": 0.0001,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**__magic_name__			)
					return config

		def 	lowercase__     (   self					:   List[Any]			)					->    List[Any]:


					"""simple docstring"""

					for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__magic_name__			)

		def 	lowercase__     (   self					:   List[str]			)					->    int:


					"""simple docstring"""

					for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,      [0.0002, 0.002, 0.02]			):
								self.check_over_configs(beta_start=__magic_name__  ,      beta_end=__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]			)					->    List[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[str]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.scheduler_classes[0]
					__snake_case					:      List[str]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      List[Any]							      =     torch.manual_seed(0			)

					__snake_case					:      Union[str, Any]							      =     self.dummy_model()
					__snake_case					:      List[str]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Dict							      =     sample.to(__magic_name__			)

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

								__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      List[Any]							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Union[str, Any]							      =     output.prev_sample

					__snake_case					:      Union[str, Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      str							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 10.0807			) < 1E-2
					assert abs(result_mean.item() - 0.0131			) < 1E-3

		def 	lowercase__     (   self					:   Optional[int]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.scheduler_classes[0]
					__snake_case					:      Dict							      =     self.get_scheduler_config(prediction_type="""v_prediction"""			)
					__snake_case					:      Optional[int]							      =     scheduler_class(**__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      List[str]							      =     torch.manual_seed(0			)

					__snake_case					:      List[str]							      =     self.dummy_model()
					__snake_case					:      int							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Union[str, Any]							      =     sample.to(__magic_name__			)

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

								__snake_case					:      str							      =     model(__magic_name__  ,      __magic_name__			)

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

					__snake_case					:      List[Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      int							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 0.0002			) < 1E-2
					assert abs(result_mean.item() - 2.2_676E-06			) < 1E-3

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      int							      =     self.scheduler_classes[0]
					__snake_case					:      Union[str, Any]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__			)

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

					__snake_case					:      Dict							      =     torch.manual_seed(0			)

					__snake_case					:      Union[str, Any]							      =     self.dummy_model()
					__snake_case					:      Optional[int]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case					:      Any							      =     sample.to(__magic_name__			)

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

								__snake_case					:      Optional[int]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[int]							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Optional[int]							      =     output.prev_sample

					__snake_case					:      List[Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      List[Any]							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 10.0807			) < 1E-2
					assert abs(result_mean.item() - 0.0131			) < 1E-3

		def 	lowercase__     (   self					:   Optional[int]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.scheduler_classes[0]
					__snake_case					:      Optional[int]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__  ,      use_karras_sigmas=__magic_name__			)

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

					__snake_case					:      Dict							      =     torch.manual_seed(0			)

					__snake_case					:      int							      =     self.dummy_model()
					__snake_case					:      Optional[int]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case					:      int							      =     sample.to(__magic_name__			)

					for t in scheduler.timesteps:
								__snake_case					:      Any							      =     scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      int							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Tuple							      =     output.prev_sample

					__snake_case					:      str							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      Tuple							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 124.52299499511719			) < 1E-2
					assert abs(result_mean.item() - 0.16213932633399963			) < 1E-3





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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor

class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=7      , _UpperCamelCase=3      , _UpperCamelCase=10      , _UpperCamelCase=18      , _UpperCamelCase=30      , _UpperCamelCase=4_00      , _UpperCamelCase=True      , _UpperCamelCase=None      , _UpperCamelCase=True      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=None      , )      ->							str:






              """simple docstring"""


              __snake_case       	=							size if size is not None else {"""shortest_edge""": 18}
              __snake_case       	=							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							num_channels
              __snake_case       	=							num_frames
              __snake_case       	=							image_size
              __snake_case       	=							min_resolution
              __snake_case       	=							max_resolution
              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							do_normalize
              __snake_case       	=							image_mean
              __snake_case       	=							image_std
              __snake_case       	=							crop_size






       def 			a		(		self   )      ->							Optional[Any]:






              """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_size": self.crop_size,
              }






@require_torch
@require_vision
class     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VivitImageProcessor if is_vision_available() else None


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							VivitImageProcessingTester(self   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							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      , """do_center_crop"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """size"""   )   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class.from_dict(self.image_processor_dict   )
              self.assertEqual(image_processor.size      , {"""shortest_edge""": 18}   )
              self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}   )

              __snake_case       	=							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   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PIL videos
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , Image.Image   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      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]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random numpy tensors
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase      , numpify=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , np.ndarray   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PyTorch tensors
              __snake_case       	=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase      , torchify=_UpperCamelCase   )
              for video in video_inputs:
                     self.assertIsInstance(_UpperCamelCase      , _UpperCamelCase   )
                     self.assertIsInstance(video[0]      , torch.Tensor   )

              # Test not batched input
              __snake_case       	=							image_processing(video_inputs[0]      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      1,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values
              self.assertEqual(
                  encoded_videos.shape      , (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_frames,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["""height"""],
                      self.image_processor_tester.crop_size["""width"""],
                  )      , )

