




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class __snake_case     (     a						):
   UpperCAmelCase__    :       Optional[int]		=    (KDPMaAncestralDiscreteScheduler,)
   UpperCAmelCase__    :       str		=    1_0







   def lowerCamelCase		( self						:		Any   ,		**_snake_case						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				{
            '''num_train_timesteps''': 1100,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
        }

        config.update(**_snake_case)
        return config







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_snake_case)







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,		[0.0_0_0_2, 0.0_0_2, 0.0_2]):
             self.check_over_configs(beta_start=_snake_case   ,		beta_end=_snake_case)







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        if torch_device == "mps":
             return
        UpperCAmelCase_		=				self.scheduler_classes[0]
        UpperCAmelCase_		=				self.get_scheduler_config()
        UpperCAmelCase_		=				scheduler_class(**_snake_case)

        scheduler.set_timesteps(self.num_inference_steps)

        UpperCAmelCase_		=				torch.manual_seed(0)

        UpperCAmelCase_		=				self.dummy_model()
        UpperCAmelCase_		=				self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase_		=				sample.to(_snake_case)

        for i, t in enumerate(scheduler.timesteps):
             UpperCAmelCase_		=				scheduler.scale_model_input(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				model(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				scheduler.step(_snake_case   ,		_snake_case   ,		_snake_case   ,		generator=_snake_case)
             UpperCAmelCase_		=				output.prev_sample

        UpperCAmelCase_		=				torch.sum(torch.abs(_snake_case))
        UpperCAmelCase_		=				torch.mean(torch.abs(_snake_case))

        assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7) < 1e-2
        assert abs(result_mean.item() - 1_8.0_3_3_1) < 5e-3







   def lowerCamelCase		( self						:		int):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


        if torch_device == "mps":
             return
        UpperCAmelCase_		=				self.scheduler_classes[0]
        UpperCAmelCase_		=				self.get_scheduler_config(prediction_type='''v_prediction''')
        UpperCAmelCase_		=				scheduler_class(**_snake_case)

        scheduler.set_timesteps(self.num_inference_steps)

        UpperCAmelCase_		=				self.dummy_model()
        UpperCAmelCase_		=				self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase_		=				sample.to(_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(0)

        for i, t in enumerate(scheduler.timesteps):
             UpperCAmelCase_		=				scheduler.scale_model_input(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				model(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				scheduler.step(_snake_case   ,		_snake_case   ,		_snake_case   ,		generator=_snake_case)
             UpperCAmelCase_		=				output.prev_sample

        UpperCAmelCase_		=				torch.sum(torch.abs(_snake_case))
        UpperCAmelCase_		=				torch.mean(torch.abs(_snake_case))

        assert abs(result_sum.item() - 3_2_8.9_9_7_0) < 1e-2
        assert abs(result_mean.item() - 0.4_2_8_4) < 1e-3







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        if torch_device == "mps":
             return
        UpperCAmelCase_		=				self.scheduler_classes[0]
        UpperCAmelCase_		=				self.get_scheduler_config()
        UpperCAmelCase_		=				scheduler_class(**_snake_case)

        scheduler.set_timesteps(self.num_inference_steps   ,		device=_snake_case)
        UpperCAmelCase_		=				torch.manual_seed(0)

        UpperCAmelCase_		=				self.dummy_model()
        UpperCAmelCase_		=				self.dummy_sample_deter.to(_snake_case) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
             UpperCAmelCase_		=				scheduler.scale_model_input(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				model(_snake_case   ,		_snake_case)

             UpperCAmelCase_		=				scheduler.step(_snake_case   ,		_snake_case   ,		_snake_case   ,		generator=_snake_case)
             UpperCAmelCase_		=				output.prev_sample

        UpperCAmelCase_		=				torch.sum(torch.abs(_snake_case))
        UpperCAmelCase_		=				torch.mean(torch.abs(_snake_case))

        assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8) < 1e-1
        assert abs(result_mean.item() - 1_8.0_3_3_1) < 1e-3












import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor







class __snake_case     (     unittest.TestCase						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any=7   ,		_snake_case						:		Any=3   ,		_snake_case						:		str=30   ,		_snake_case						:		str=400   ,		_snake_case						:		Any=True   ,		_snake_case						:		Dict=None   ,		_snake_case						:		List[str]=True   ,		_snake_case						:		Union[str, Any]=[0.5, 0.5, 0.5]   ,		_snake_case						:		Tuple=[0.5, 0.5, 0.5]   ,		_snake_case						:		str=False   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				size if size is not None else {'''height''': 30, '''width''': 30}
        UpperCAmelCase_		=				parent
        UpperCAmelCase_		=				batch_size
        UpperCAmelCase_		=				num_channels
        UpperCAmelCase_		=				min_resolution
        UpperCAmelCase_		=				max_resolution
        UpperCAmelCase_		=				do_resize
        UpperCAmelCase_		=				size
        UpperCAmelCase_		=				do_normalize
        UpperCAmelCase_		=				image_mean
        UpperCAmelCase_		=				image_std
        UpperCAmelCase_		=				do_reduce_labels







   def lowerCamelCase		( self						:		Tuple):

        """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_reduce_labels": self.do_reduce_labels,
        }







def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)

     return image, map


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[2]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       int		=    SegformerImageProcessor if is_vision_available() else None







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				SegformerImageProcessingTester(self)







   @property
   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				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_normalize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_mean'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_std'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_reduce_labels'''))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size   ,		{'''height''': 30, '''width''': 30})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)

        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		reduce_labels=_snake_case)
        self.assertEqual(image_processor.size   ,		{'''height''': 42, '''width''': 42})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        pass







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        UpperCAmelCase_		=				[]
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)
             maps.append(torch.zeros(image.shape[-2:]).long())

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		maps[0]   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].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'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test not batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_batch_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 150)

        UpperCAmelCase_		=				True
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)






