
'''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 DeiTImageProcessor

class 				_a			(unittest.TestCase):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=7    ,							A__=3    ,							A__=18    ,							A__=30    ,							A__=4_00    ,							A__=True    ,							A__=None    ,							A__=True    ,							A__=None    ,							A__=True    ,							A__=[0.5, 0.5, 0.5]    ,							A__=[0.5, 0.5, 0.5]    ,							)      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			size if size is not None else {"""height""": 20, """width""": 20}
          _SCREAMING_SNAKE_CASE       	=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

          _SCREAMING_SNAKE_CASE       	=			parent
          _SCREAMING_SNAKE_CASE       	=			batch_size
          _SCREAMING_SNAKE_CASE       	=			num_channels
          _SCREAMING_SNAKE_CASE       	=			image_size
          _SCREAMING_SNAKE_CASE       	=			min_resolution
          _SCREAMING_SNAKE_CASE       	=			max_resolution
          _SCREAMING_SNAKE_CASE       	=			do_resize
          _SCREAMING_SNAKE_CASE       	=			size
          _SCREAMING_SNAKE_CASE       	=			do_center_crop
          _SCREAMING_SNAKE_CASE       	=			crop_size
          _SCREAMING_SNAKE_CASE       	=			do_normalize
          _SCREAMING_SNAKE_CASE       	=			image_mean
          _SCREAMING_SNAKE_CASE       	=			image_std






       def     UpperCamelCase			(							self )      ->     List[str]:
          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 				_a			(_lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					DeiTImageProcessor if is_vision_available() else None
       SCREAMING_SNAKE_CASE =					True






       def     UpperCamelCase			(							self )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			DeiTImageProcessingTester(self )






       @property
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			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_normalize""" ) )
          self.assertTrue(hasattr(A__    ,							"""image_mean""" ) )
          self.assertTrue(hasattr(A__    ,							"""image_std""" ) )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			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} )

          _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self )      ->     Any:
          pass






       def     UpperCamelCase			(							self )      ->     int:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          _SCREAMING_SNAKE_CASE       	=			prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=A__ )
          for image in image_inputs:
             self.assertIsInstance(A__    ,							Image.Image )

          # Test not batched input
          _SCREAMING_SNAKE_CASE       	=			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
          _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self )      ->     List[str]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE       	=			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
          _SCREAMING_SNAKE_CASE       	=			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
          _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self )      ->     str:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE       	=			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
          _SCREAMING_SNAKE_CASE       	=			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
          _SCREAMING_SNAKE_CASE       	=			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"""],
              )    ,							)








'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(DDIMScheduler,)
       SCREAMING_SNAKE_CASE =					(('eta', 0.0), ('num_inference_steps', 50))






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 10_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(**A__ )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			10, 0.0

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter

          scheduler.set_timesteps(A__ )

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

          return sample






       def     UpperCamelCase			(							self )      ->     str:
          for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__ )






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






       def     UpperCamelCase			(							self )      ->     Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          self.check_over_configs(thresholding=A__ )
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=A__    ,							prediction_type=A__    ,							sample_max_value=A__    ,							)






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00] ):
             self.check_over_forward(time_step=A__    ,							num_inference_steps=A__ )






       def     UpperCamelCase			(							self )      ->     str:
          for t, eta in zip([1, 10, 49]    ,							[0.0, 0.5, 1.0] ):
             self.check_over_forward(time_step=A__    ,							eta=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,							4_00 ) - 0.1_4771 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60 ) - 0.3_2460 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,							4_86 ) - 0.0_0979 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98 ) - 0.02 ) ) < 1E-5






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop()

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1E-2
          assert abs(result_mean.item() - 0.22_3967 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(prediction_type="""v_prediction""" )

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1E-2
          assert abs(result_mean.item() - 0.0684 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     List[Any]:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1E-2
          assert abs(result_mean.item() - 0.1951 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     Any:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1E-2
          assert abs(result_mean.item() - 0.1941 ) < 1E-3






