"""simple docstring"""



import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
   from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image






class       snake_case__	:


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase=3  ,						lowerCamelCase=32  ,						lowerCamelCase=3  ,						lowerCamelCase=10  ,						lowerCamelCase=[8, 16, 32, 64]  ,						lowerCamelCase=[1, 1, 2, 1]  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase="relu"  ,						lowerCamelCase=3  ,						lowerCamelCase=None  ,						lowerCamelCase=["stage2", "stage3", "stage4"]  ,						lowerCamelCase=[2, 3, 4]  ,						lowerCamelCase=1  ,						):
        __a	      =  parent
        __a	      =  batch_size
        __a	      =  image_size
        __a	      =  num_channels
        __a	      =  embeddings_size
        __a	      =  hidden_sizes
        __a	      =  depths
        __a	      =  is_training
        __a	      =  use_labels
        __a	      =  hidden_act
        __a	      =  num_labels
        __a	      =  scope
        __a	      =  len(lowercase_		)
        __a	      =  out_features
        __a	      =  out_indices
        __a	      =  num_groups


 def 		a__					(      self		):
        __a	      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        __a	      =  None
        if self.use_labels:
               __a	      =  ids_tensor([self.batch_size]  ,						self.num_labels		)

        __a	      =  self.get_config()

        return config, pixel_values, labels


 def 		a__					(      self		):
        return BitConfig(
            num_channels=self.num_channels  ,						embeddings_size=self.embeddings_size  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						hidden_act=self.hidden_act  ,						num_labels=self.num_labels  ,						out_features=self.out_features  ,						out_indices=self.out_indices  ,						num_groups=self.num_groups  ,						)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  BitModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        __a	      =  model(lowercase_		)
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,						)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  self.num_labels
        __a	      =  BitForImageClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        __a	      =  model(lowercase_  ,						labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        __a	      =  model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		)  ,						len(config.out_features		)		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,						[self.batch_size, self.hidden_sizes[1], 4, 4]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		)  ,						len(config.out_features		)		)
        self.parent.assertListEqual(model.channels  ,						config.hidden_sizes[1:]		)

        # verify backbone works with out_features=None
        __a	      =  None
        __a	      =  BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        __a	      =  model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		)  ,						1		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		)  ,						1		)
        self.parent.assertListEqual(model.channels  ,						[config.hidden_sizes[-1]]		)






 def 		a__					(      self		):
        __a	      =  self.prepare_config_and_inputs()
        __a	      =  config_and_inputs
        __a	      =  {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class       snake_case__	(    UpperCAmelCase__,		UpperCAmelCase__,		unittest.TestCase    ):
 _snake_case    : int				   =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
 _snake_case    : Dict				   =		(
     {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
     if is_torch_available()
     else {}
 )

 _snake_case    : List[Any]				   =		False
 _snake_case    : Any				   =		False
 _snake_case    : int				   =		False
 _snake_case    : Optional[int]				   =		False
 _snake_case    : List[Any]				   =		False


 def 		a__					(      self		):
        __a	      =  BitModelTester(self		)
        __a	      =  ConfigTester(self  ,						config_class=lowercase_  ,						has_text_modality=lowercase_		)


 def 		a__					(      self		):
        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 		a__					(      self		):
        return


 @unittest.skip(reason="Bit does not output attentions"		)
 def 		a__					(      self		):
        pass


 @unittest.skip(reason="Bit does not use inputs_embeds"		)
 def 		a__					(      self		):
        pass


 @unittest.skip(reason="Bit does not support input and output embeddings"		)
 def 		a__					(      self		):
        pass


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase_		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a	      =  model_class(config=lowercase_		)
               for name, module in model.named_modules():
                      if isinstance(lowercase_  ,						(nn.BatchNormad, nn.GroupNorm)		):
                             self.assertTrue(
                                 torch.all(module.weight == 1		)  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)
                             self.assertTrue(
                                 torch.all(module.bias == 0		)  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)


 def 		a__					(      self		):


        def check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
               __a	      =  model_class(lowercase_		)
               model.to(lowercase_		)
               model.eval()

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

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

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

               # Bit'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]  ,						)

        __a	      =  self.model_tester.prepare_config_and_inputs_for_common()
        __a	      =  ['''preactivation''', '''bottleneck''']
        for model_class in self.all_model_classes:
               for layer_type in layers_type:
                      __a	      =  layer_type
                      __a	      =  True
                      check_hidden_states_output(lowercase_  ,						lowercase_  ,						lowercase_		)

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

                      check_hidden_states_output(lowercase_  ,						lowercase_  ,						lowercase_		)


 @unittest.skip(reason="Bit does not use feedforward chunking"		)
 def 		a__					(      self		):
        pass


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)






 @slow
 def 		a__					(      self		):


        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a	      =  BitModel.from_pretrained(lowercase_		)
               self.assertIsNotNone(lowercase_		)



def      _lowerCamelCase(     ):
       __a	      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       return image






@require_torch
@require_vision
class       snake_case__	(    unittest.TestCase    ):


 @cached_property
 def 		a__					(      self		):
        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
        )






 @slow
 def 		a__					(      self		):
        __a	      =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowercase_		)

        __a	      =  self.default_image_processor
        __a	      =  prepare_img()
        __a	      =  image_processor(images=lowercase_  ,						return_tensors="pt"		).to(lowercase_		)

        # forward pass
        with torch.no_grad():
               __a	      =  model(**lowercase_		)

        # verify the logits
        __a	      =  torch.Size((1, 1000)		)
        self.assertEqual(outputs.logits.shape  ,						lowercase_		)

        __a	      =  torch.tensor([[-0.6526, -0.5263, -1.4398]]		).to(lowercase_		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						lowercase_  ,						atol=1E-4		)		)






@require_torch
class       snake_case__	(    UpperCAmelCase__,		unittest.TestCase    ):
 _snake_case    : Dict				   =		(BitBackbone,) if is_torch_available() else ()
 _snake_case    : int				   =		BitConfig

 _snake_case    : List[str]				   =		False


 def 		a__					(      self		):
        __a	      =  BitModelTester(self		)








"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (PNDMScheduler,)
      __UpperCamelCase	 =  (("num_inference_steps", 5_0),)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config(**lowercase_)
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class.from_pretrained(lowercase_)
                  new_scheduler.set_timesteps(lowercase_)
                  # copy over dummy past residuals
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Dict    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  int    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  str    						=		scheduler_class.from_pretrained(lowercase_)
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(lowercase_)

                  # copy over dummy past residual (must be after setting timesteps)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	**lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		10
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_)

          for i, t in enumerate(scheduler.prk_timesteps):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		dict(self.forward_default_kwargs)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		0.1 * sample

              if num_inference_steps is not None and hasattr(lowercase_						,	'''set_timesteps'''):
                  scheduler.set_timesteps(lowercase_)
              elif num_inference_steps is not None and not hasattr(lowercase_						,	'''set_timesteps'''):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_      :  str    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

              self.assertEqual(output_a.shape						,	sample.shape)
              self.assertEqual(output_a.shape						,	output_a.shape)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

              self.assertEqual(output_a.shape						,	sample.shape)
              self.assertEqual(output_a.shape						,	output_a.shape)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config(steps_offset=1)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler_class(**lowercase_)
          scheduler.set_timesteps(10)
          assert torch.equal(
              scheduler.timesteps						,	torch.LongTensor(
                  [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01]						,	[0.0_02, 0.02]):
              self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          for t in [1, 5, 10]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]):
              self.check_over_forward(num_inference_steps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		27

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  str    						=		0.1 * sample

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler_class(**lowercase_)

              scheduler.set_timesteps(lowercase_)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]):
                  SCREAMING_SNAKE_CASE_      :  int    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  int    						=		self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler_class(**lowercase_)

              scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.full_loop()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_98.13_18) < 1e-2
          assert abs(result_mean.item() - 0.25_80) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop(prediction_type='''v_prediction''')
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 67.39_86) < 1e-2
          assert abs(result_mean.item() - 0.08_78) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_30.03_99) < 1e-2
          assert abs(result_mean.item() - 0.29_95) < 1e-3



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


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

          assert abs(result_sum.item() - 1_86.94_82) < 1e-2
          assert abs(result_mean.item() - 0.24_34) < 1e-3


