





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=7,__lowerCamelCase=3,__lowerCamelCase=18,__lowerCamelCase=30,__lowerCamelCase=400,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase=[0.5, 0.5, 0.5],__lowerCamelCase=[0.5, 0.5, 0.5],):
        A__											=						size if size is not None else {'''height''': 18, '''width''': 18}
        A__											=						parent
        A__											=						batch_size
        A__											=						num_channels
        A__											=						image_size
        A__											=						min_resolution
        A__											=						max_resolution
        A__											=						do_resize
        A__											=						size
        A__											=						do_normalize
        A__											=						image_mean
        A__											=						image_std




 def 				UpperCamelCase			(							self ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }




@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	DPTImageProcessor if is_vision_available() else None



 def 				UpperCamelCase			(							self ):
        A__											=						DPTImageProcessingTester(self )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(__lowerCamelCase,'''image_mean''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''image_std''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''do_normalize''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''do_resize''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''size''' ) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size,{'''height''': 18, '''width''': 18} )

        A__											=						self.image_processing_class.from_dict(self.image_processor_dict,size=42 )
        self.assertEqual(image_processor.size,{'''height''': 42, '''width''': 42} )



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

        # Test not batched input
        A__											=						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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            ),)

        # Test batched
        A__											=						image_processing(__lowerCamelCase,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 ):
        # Initialize image_processing
        A__											=						self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        A__											=						prepare_image_inputs(self.image_processor_tester,equal_resolution=__lowerCamelCase,numpify=__lowerCamelCase )
        for image in image_inputs:
               self.assertIsInstance(__lowerCamelCase,np.ndarray )

        # Test not batched input
        A__											=						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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            ),)

        # Test batched
        A__											=						image_processing(__lowerCamelCase,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 ):
        # Initialize image_processing
        A__											=						self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        A__											=						prepare_image_inputs(self.image_processor_tester,equal_resolution=__lowerCamelCase,torchify=__lowerCamelCase )
        for image in image_inputs:
               self.assertIsInstance(__lowerCamelCase,torch.Tensor )

        # Test not batched input
        A__											=						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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            ),)

        # Test batched
        A__											=						image_processing(__lowerCamelCase,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'''],
            ),)












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

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

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        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=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 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 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

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

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

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

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

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

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



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

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




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

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





