'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[Any]   				= (DDPMScheduler,)




      def 		lowerCAmelCase__       (					self       ,**lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	{
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**__a				)
         return config




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         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 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



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




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



         '''simple docstring'''



         for t in [0, 500, 999]:
            self.check_over_forward(time_step=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					List[str] 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Dict 					=	scheduler_class(**__a				)

         assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0979				)				) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1e-5




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Any 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Tuple 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					str 					=	len(__a				)

         UpperCAmelCase__				:					List[Any] 					=	self.dummy_model()
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_sample_deter
         UpperCAmelCase__				:					List[Any] 					=	torch.manual_seed(0				)

         for t in reversed(range(__a				)				):
            # 1. predict noise residual
            UpperCAmelCase__				:					Dict 					=	model(__a       ,__a				)

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase__				:					Dict 					=	scheduler.step(__a       ,__a       ,__a       ,generator=__a				).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase__				:					List[str] 					=	pred_prev_sample

         UpperCAmelCase__				:					Tuple 					=	torch.sum(torch.abs(__a				)				)
         UpperCAmelCase__				:					int 					=	torch.mean(torch.abs(__a				)				)

         assert abs(result_sum.item() - 258.9606				) < 1e-2
         assert abs(result_mean.item() - 0.3372				) < 1e-3




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Any 					=	self.get_scheduler_config(prediction_type='''v_prediction'''				)
         UpperCAmelCase__				:					Optional[Any] 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					str 					=	len(__a				)

         UpperCAmelCase__				:					int 					=	self.dummy_model()
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_sample_deter
         UpperCAmelCase__				:					Union[str, Any] 					=	torch.manual_seed(0				)

         for t in reversed(range(__a				)				):
            # 1. predict noise residual
            UpperCAmelCase__				:					Optional[Any] 					=	model(__a       ,__a				)

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase__				:					Optional[Any] 					=	scheduler.step(__a       ,__a       ,__a       ,generator=__a				).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase__				:					Optional[Any] 					=	pred_prev_sample

         UpperCAmelCase__				:					List[Any] 					=	torch.sum(torch.abs(__a				)				)
         UpperCAmelCase__				:					Tuple 					=	torch.mean(torch.abs(__a				)				)

         assert abs(result_sum.item() - 202.0296				) < 1e-2
         assert abs(result_mean.item() - 0.2631				) < 1e-3




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Dict 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Optional[int] 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					Optional[Any] 					=	[100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=__a				)

         UpperCAmelCase__				:					Dict 					=	scheduler.timesteps

         for i, timestep in enumerate(__a				):
            if i == len(__a				) - 1:
               UpperCAmelCase__				:					Tuple 					=	-1
            else:
               UpperCAmelCase__				:					List[str] 					=	timesteps[i + 1]

            UpperCAmelCase__				:					Optional[Any] 					=	scheduler.previous_timestep(__a				)
            UpperCAmelCase__				:					Any 					=	prev_t.item()

            self.assertEqual(__a       ,__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Optional[int] 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Optional[Any] 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					Any 					=	[100, 87, 50, 51, 0]

         with self.assertRaises(__a       ,msg='''`custom_timesteps` must be in descending order.'''				):
            scheduler.set_timesteps(timesteps=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Any 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					List[str] 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					str 					=	[100, 87, 50, 1, 0]
         UpperCAmelCase__				:					List[Any] 					=	len(__a				)

         with self.assertRaises(__a       ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
            scheduler.set_timesteps(num_inference_steps=__a       ,timesteps=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Any 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Dict 					=	scheduler_class(**__a				)

         UpperCAmelCase__				:					str 					=	[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __a       ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,):
            scheduler.set_timesteps(timesteps=__a				)






"""simple docstring"""





import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import UperNetForSemanticSegmentation
				from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=32							,						__a=3							,						__a=4							,						__a=[10, 20, 30, 40]							,						__a=[2, 2, 3, 2]							,						__a=True							,						__a=True							,						__a=37							,						__a="gelu"							,						__a=10							,						__a=0.02							,						__a=["stage2", "stage3", "stage4"]							,						__a=3							,						__a=None							,						)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    num_stages
							_UpperCamelCase									=    hidden_sizes
							_UpperCamelCase									=    depths
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    out_features
							_UpperCamelCase									=    num_labels
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    num_stages



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							return ConvNextConfig(
							    num_channels=self.num_channels							,						num_stages=self.num_stages							,						hidden_sizes=self.hidden_sizes							,						depths=self.depths							,						is_training=self.is_training							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						out_features=self.out_features							,						)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							return UperNetConfig(
							    backbone_config=self.get_backbone_config()							,						hidden_size=5_12							,						pool_scales=[1, 2, 3, 6]							,						use_auxiliary_head=__a							,						auxiliary_loss_weight=0.4							,						auxiliary_in_channels=40							,						auxiliary_channels=2_56							,						auxiliary_num_convs=1							,						auxiliary_concat_input=__a							,						loss_ignore_index=2_55							,						num_labels=self.num_labels							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    UperNetForSemanticSegmentation(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(
							    result.logits.shape							,						(self.batch_size, self.num_labels, self.image_size, self.image_size))




		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
		lowercase__         = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



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






							'''simple docstring'''
							_UpperCamelCase									=    UperNetModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()



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






							'''simple docstring'''
							return



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												_UpperCamelCase									=    inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase									=    [*signature.parameters.keys()]

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__a)



		@unittest.skip(reason='''UperNet does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							pass



		@unittest.skip(reason='''UperNet does not support input and output embeddings''')
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							pass



		@unittest.skip(reason='''UperNet does not have a base model''')
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							pass



		@unittest.skip(reason='''UperNet does not have a base model''')
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							pass



		@require_torch_multi_gpu
		@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							pass



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							pass



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






							'''simple docstring'''
							def check_hidden_states_output(__a							,						__a							,						__a):
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.eval()

												with torch.no_grad():
																	_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))

												_UpperCamelCase									=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												_UpperCamelCase									=    self.model_tester.num_stages
												self.assertEqual(len(__a)							,						expected_num_stages + 1)

												# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:])							,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,						)

							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    True
												check_hidden_states_output(__a							,						__a							,						__a)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCamelCase									=    True

												check_hidden_states_output(__a							,						__a							,						__a)



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






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							_UpperCamelCase									=    _config_zero_init(configs_no_init.backbone_config)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												for name, param in model.named_parameters():
																	if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)



		@unittest.skip(reason='''UperNet does not have tied weights''')
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							pass




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    UperNetForSemanticSegmentation.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    hf_hub_download(
					    repo_id='''hf-internal-testing/fixtures_ade20k''',	repo_type='''dataset''',	filename='''ADE_val_00000001.jpg'''      )
					_UpperCamelCase									=    Image.open(__snake_case      ).convert('''RGB'''      )
					return image






@require_torch
@require_vision
@slow
class        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
							_UpperCamelCase									=    UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)

							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    processor(images=__a							,						return_tensors='''pt''').to(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							_UpperCamelCase									=    torch.Size((1, model.config.num_labels, 5_12, 5_12))
							self.assertEqual(outputs.logits.shape							,						__a)

							_UpperCamelCase									=    torch.tensor(
							    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
							self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,						__a							,						atol=1e-4))




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






							'''simple docstring'''
							_UpperCamelCase									=    AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
							_UpperCamelCase									=    UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)

							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    processor(images=__a							,						return_tensors='''pt''').to(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							_UpperCamelCase									=    torch.Size((1, model.config.num_labels, 5_12, 5_12))
							self.assertEqual(outputs.logits.shape							,						__a)

							_UpperCamelCase									=    torch.tensor(
							    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
							self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,						__a							,						atol=1e-4))






