
"""simple docstring"""




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class  snake_case     :

       def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=[10, 20, 30, 40]       ,       SCREAMING_SNAKE_CASE_=[1, 1, 2, 1]       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_="relu"       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=None       ,       ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = parent
         SCREAMING_SNAKE_CASE_						 = batch_size
         SCREAMING_SNAKE_CASE_						 = image_size
         SCREAMING_SNAKE_CASE_						 = num_channels
         SCREAMING_SNAKE_CASE_						 = embeddings_size
         SCREAMING_SNAKE_CASE_						 = hidden_sizes
         SCREAMING_SNAKE_CASE_						 = depths
         SCREAMING_SNAKE_CASE_						 = is_training
         SCREAMING_SNAKE_CASE_						 = use_labels
         SCREAMING_SNAKE_CASE_						 = hidden_act
         SCREAMING_SNAKE_CASE_						 = num_labels
         SCREAMING_SNAKE_CASE_						 = scope
         SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

         SCREAMING_SNAKE_CASE_						 = self.get_config()
         return config, pixel_values, labels

       def _lowercase					(self    ):

         """simple docstring"""







         return RegNetConfig(
             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       ,       )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFRegNetModel(config=SCREAMING_SNAKE_CASE_    )
         SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       training=SCREAMING_SNAKE_CASE_    )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,       )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.num_labels
         SCREAMING_SNAKE_CASE_						 = TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_    )
         SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_       ,       training=SCREAMING_SNAKE_CASE_    )
         self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels)    )







       def _lowercase					(self    ):

         """simple docstring"""







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







@require_tf
class  snake_case     (							__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
       UpperCAmelCase__						     =      (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
       UpperCAmelCase__						     =      (
           {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
           if is_tf_available()
           else {}
       )

       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFRegNetModelTester(self    )
         SCREAMING_SNAKE_CASE_						 = ConfigTester(self       ,       config_class=SCREAMING_SNAKE_CASE_       ,       has_text_modality=SCREAMING_SNAKE_CASE_    )

       def _lowercase					(self    ):

         """simple docstring"""







         return

       @unittest.skip(reason='''RegNet does not use inputs_embeds'''    )
       def _lowercase					(self    ):

         """simple docstring"""







         pass

       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''    )    ) == 0       ,       reason='''TF does not support backprop for grouped convolutions on CPU.'''       ,       )
       @slow
       def _lowercase					(self    ):

         """simple docstring"""







         super().test_keras_fit()

       @unittest.skip(reason='''RegNet does not support input and output embeddings'''    )
       def _lowercase					(self    ):

         """simple docstring"""







         pass

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

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

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

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_    )

       def _lowercase					(self    ):

         """simple docstring"""







         def check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
           SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       training=SCREAMING_SNAKE_CASE_    )

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

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

           # RegNet'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 // 2, self.model_tester.image_size // 2]       ,       )

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

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

             check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

         def check_equivalence(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_={}    ):
           SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ).to_tuple()

           def recursive_check(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
             if isinstance(SCREAMING_SNAKE_CASE_       ,       (List, Tuple)    ):
               for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
                 recursive_check(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
             elif tuple_object is None:
               return
             else:
               self.assertTrue(
                   all(tf.equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )       ,       msg=(
                       '''Tuple and dict output are not equal. Difference:'''
                       f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}'
                   )       ,       )

             recursive_check(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

         for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
           check_equivalence(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
           check_equivalence(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
           check_equivalence(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       {'''output_hidden_states''': True}    )

           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
           SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
           check_equivalence(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       {'''output_hidden_states''': True}    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_    )







       @slow
       def _lowercase					(self    ):

         """simple docstring"""







         for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE_						 = TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







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





@require_tf
@require_vision
class  snake_case     (							unittest.TestCase   ):

       @cached_property
       def _lowercase					(self    ):

         """simple docstring"""







         return (
             AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
             if is_vision_available()
             else None
         )







       @slow
       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )

         SCREAMING_SNAKE_CASE_						 = self.default_image_processor
         SCREAMING_SNAKE_CASE_						 = prepare_img()
         SCREAMING_SNAKE_CASE_						 = image_processor(images=SCREAMING_SNAKE_CASE_       ,       return_tensors='''tf'''    )

         # forward pass
         SCREAMING_SNAKE_CASE_						 = model(**SCREAMING_SNAKE_CASE_       ,       training=SCREAMING_SNAKE_CASE_    )

         # verify the logits
         SCREAMING_SNAKE_CASE_						 = tf.TensorShape((1, 10_00)    )
         self.assertEqual(outputs.logits.shape       ,       SCREAMING_SNAKE_CASE_    )

         SCREAMING_SNAKE_CASE_						 = tf.constant([-0.41_80, -1.50_51, -3.48_36]    )

         tf.debugging.assert_near(outputs.logits[0, :3]       ,       SCREAMING_SNAKE_CASE_       ,       atol=1e-4    )

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

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

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

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

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

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

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3