

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  (DDIMScheduler,)
 _UpperCamelCase        =  (("""eta""", 0.0), ("""num_inference_steps""", 50))
 def      UpperCamelCase__  (							self				,							**A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**A_ )
       return config
 def      UpperCamelCase__  (							self				,							**A_ ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.scheduler_classes[0]
       __lowerCAmelCase :						Any = self.get_scheduler_config(**A_ )
       __lowerCAmelCase :						Optional[Any] = scheduler_class(**A_ )

       __lowerCAmelCase,							__lowerCAmelCase :						str = 10, 0.0

       __lowerCAmelCase :						str = self.dummy_model()
       __lowerCAmelCase :						Optional[Any] = self.dummy_sample_deter

       scheduler.set_timesteps(A_ )

       for t in scheduler.timesteps:
             __lowerCAmelCase :						Union[str, Any] = model(A_				,							A_ )
             __lowerCAmelCase :						Union[str, Any] = scheduler.step(A_				,							A_				,							A_				,							A_ ).prev_sample

       return sample
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=A_ )
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





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

       __lowerCAmelCase :						Optional[Any] = self.scheduler_classes[0]
       __lowerCAmelCase :						List[str] = self.get_scheduler_config(steps_offset=1 )
       __lowerCAmelCase :						Tuple = scheduler_class(**A_ )
       scheduler.set_timesteps(5 )
       assert torch.equal(scheduler.timesteps				,							torch.LongTensor([801, 601, 401, 201, 1] ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       for beta_start, beta_end in zip([0.0_001, 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]:



       '''simple docstring'''





       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A_ )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A_ )
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A_ )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A_ )
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





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



       '''simple docstring'''





       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 ) ->List[Any]:



       '''simple docstring'''





       for t in [1, 10, 49]:
             self.check_over_forward(time_step=A_ )
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       for t, num_inference_steps in zip([1, 10, 50]				,							[10, 50, 500] ):
             self.check_over_forward(time_step=A_				,							num_inference_steps=A_ )
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       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 ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.scheduler_classes[0]
       __lowerCAmelCase :						str = self.get_scheduler_config()
       __lowerCAmelCase :						List[str] = 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(420				,							400 ) - 0.14_771 ) ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980				,							960 ) - 0.32_460 ) ) < 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 ) - 0.00_979 ) ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999				,							998 ) - 0.02 ) ) < 1e-5
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.full_loop()

       __lowerCAmelCase :						Any = torch.sum(torch.abs(A_ ) )
       __lowerCAmelCase :						Optional[Any] = torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 172.0_067 ) < 1e-2
       assert abs(result_mean.item() - 0.223_967 ) < 1e-3
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.full_loop(prediction_type='''v_prediction''' )

       __lowerCAmelCase :						Optional[int] = torch.sum(torch.abs(A_ ) )
       __lowerCAmelCase :						Union[str, Any] = torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 52.5_302 ) < 1e-2
       assert abs(result_mean.item() - 0.0_684 ) < 1e-3
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.full_loop(set_alpha_to_one=A_				,							beta_start=0.01 )
       __lowerCAmelCase :						int = torch.sum(torch.abs(A_ ) )
       __lowerCAmelCase :						List[str] = torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 149.8_295 ) < 1e-2
       assert abs(result_mean.item() - 0.1_951 ) < 1e-3





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.full_loop(set_alpha_to_one=A_				,							beta_start=0.01 )
       __lowerCAmelCase :						str = torch.sum(torch.abs(A_ ) )
       __lowerCAmelCase :						Optional[int] = torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 149.0_784 ) < 1e-2
       assert abs(result_mean.item() - 0.1_941 ) < 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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor







class   __lowercase				(unittest.TestCase			):
 def __init__(							self				,							A_				,							A_=7				,							A_=3				,							A_=18				,							A_=30				,							A_=400				,							A_=True				,							A_=None				,							A_=True				,							A_=False				,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							A_=True				,							) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						List[str] = batch_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[int] = min_resolution
       __lowerCAmelCase :						Optional[int] = max_resolution
       __lowerCAmelCase :						List[Any] = do_resize
       __lowerCAmelCase :						Dict = size
       __lowerCAmelCase :						Dict = do_normalize
       __lowerCAmelCase :						Any = image_mean
       __lowerCAmelCase :						Optional[int] = image_std
       __lowerCAmelCase :						Any = do_pad
       __lowerCAmelCase :						Any = do_convert_rgb
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





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





 def      UpperCamelCase__  (							self				,							A_=False				,							A_=False				,							A_=False ) ->Dict:



       '''simple docstring'''





       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
             __lowerCAmelCase :						int = []
             for i in range(self.batch_size ):
                   image_inputs.append(
                       np.random.randint(
                           255				,							size=(self.num_channels, self.max_resolution, self.max_resolution)				,							dtype=np.uinta ) )
       else:
             __lowerCAmelCase :						List[Any] = []
             for i in range(self.batch_size ):
                   __lowerCAmelCase,							__lowerCAmelCase :						Any = np.random.choice(np.arange(self.min_resolution				,							self.max_resolution )				,							2 )
                   image_inputs.append(np.random.randint(255				,							size=(self.num_channels, width, height)				,							dtype=np.uinta ) )

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             __lowerCAmelCase :						List[str] = [Image.fromarray(np.moveaxis(A_				,							0				,							-1 ) ) for x in image_inputs]

       if torchify:
             __lowerCAmelCase :						int = [torch.from_numpy(A_ ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = BlipImageProcessingTester(self )
 @property
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Optional[Any] = image_processor(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['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						str = image_processor(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __lowerCAmelCase :						Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							numpify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							np.ndarray )

       # Test not batched input
       __lowerCAmelCase :						Dict = image_processor(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['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[Any] = image_processor(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)





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



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __lowerCAmelCase :						str = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							torchify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							torch.Tensor )

       # Test not batched input
       __lowerCAmelCase :						List[Any] = image_processor(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['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						Tuple = image_processor(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = BlipImageProcessingTester(self				,							num_channels=4 )
       __lowerCAmelCase :						str = 3
 @property
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						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_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       pass





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Tuple = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[str] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)


