





"""simple docstring"""
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=13				,       _snake_case=30				,       _snake_case=2				,       _snake_case=3				,       _snake_case=True				,       _snake_case=True				,       _snake_case=32				,       _snake_case=2				,       _snake_case=4				,       _snake_case=37				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=10				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=0.6				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= image_size
  lowerCAmelCase    				= patch_size
  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= type_sequence_label_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= mask_ratio
  lowerCAmelCase    				= scope

  # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
  # (we add 1 for the [CLS] token)
  lowerCAmelCase    				= (image_size // patch_size) ** 2
  lowerCAmelCase    				= int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

  lowerCAmelCase    				= self.get_config()

  return config, pixel_values, labels



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return ViTMAEConfig(
      image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       decoder_hidden_size=self.hidden_size				,       decoder_num_hidden_layers=self.num_hidden_layers				,       decoder_num_attention_heads=self.num_attention_heads				,       decoder_intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=_SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       mask_ratio=self.mask_ratio				,       )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFViTMAEModel(config=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       training=_SCREAMING_SNAKE_CASE     )
  self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       training=_SCREAMING_SNAKE_CASE     )
  # expected sequence length = num_patches
  lowerCAmelCase    				= (self.image_size // self.patch_size) ** 2
  lowerCAmelCase    				= self.patch_size**2 * self.num_channels
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)     )

  # test greyscale images
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
  lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       training=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= self.patch_size**2
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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







@require_tf
class       a							(   a_   , a_   , unittest.TestCase  ):
 snake_case__             =       (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
 snake_case__             =       {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFViTMAEModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



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







  """simple docstring"""






  pass



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase    				= model_class(_SCREAMING_SNAKE_CASE     )
   self.assertIsInstance(model.get_input_embeddings()				,       (tf.keras.layers.Layer)     )
   lowerCAmelCase    				= model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE				,       tf.keras.layers.Layer     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

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

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase    				= int((config.image_size // config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

  for model_class in self.all_model_classes:
   lowerCAmelCase    				= model_class(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )     )
   lowerCAmelCase    				= model(**_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= outputs_dict[0].numpy()
   lowerCAmelCase    				= outputs_keywords[0].numpy()

   self.assertLess(np.sum(np.abs(output_dict - output_keywords     )     )				,       1E-6     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase    				= int((config.image_size // config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

  def prepare_numpy_arrays(_snake_case     ):
   lowerCAmelCase    				= {}
   for k, v in inputs_dict.items():
    if tf.is_tensor(_SCREAMING_SNAKE_CASE     ):
     lowerCAmelCase    				= v.numpy()
    else:
     lowerCAmelCase    				= np.array(_SCREAMING_SNAKE_CASE     )

   return inputs_np_dict

  for model_class in self.all_model_classes:
   lowerCAmelCase    				= model_class(_SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= prepare_numpy_arrays(_SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= model(**_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )
   self.assert_outputs_same(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase    				= int((tf_model.config.image_size // tf_model.config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
  lowerCAmelCase    				= tf.constant(_SCREAMING_SNAKE_CASE     )

  # Add `noise` argument.
  # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
  lowerCAmelCase    				= tf_noise

  super().check_pt_tf_models(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase    				= {
      module_member
      for model_class in self.all_model_classes
      for module in (import_module(model_class.__module__     ),)
      for module_member_name in dir(_SCREAMING_SNAKE_CASE     )
      if module_member_name.endswith('MainLayer'     )
      # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
      and module_member_name[: -len('MainLayer'     )] == model_class.__name__[: -len('Model'     )]
      for module_member in (getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     ),)
      if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
      and tf.keras.layers.Layer in module_member.__bases__
      and getattr(_SCREAMING_SNAKE_CASE				,       '_keras_serializable'				,       _SCREAMING_SNAKE_CASE     )
  }

  lowerCAmelCase    				= int((config.image_size // config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
  lowerCAmelCase    				= tf.convert_to_tensor(_SCREAMING_SNAKE_CASE     )
  inputs_dict.update({'noise': noise}     )

  for main_layer_class in tf_main_layer_classes:
   lowerCAmelCase    				= main_layer_class(_SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= {
       name: tf.keras.Input(tensor.shape[1:]				,       dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
   }

   lowerCAmelCase    				= tf.keras.Model(_SCREAMING_SNAKE_CASE				,       outputs=main_layer(_SCREAMING_SNAKE_CASE     )     )
   lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE     )

   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCAmelCase    				= os.path.join(_SCREAMING_SNAKE_CASE				,       'keras_model.h5'     )
    model.save(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase    				= tf.keras.models.load_model(
        _SCREAMING_SNAKE_CASE				,       custom_objects={main_layer_class.__name__: main_layer_class}     )
    assert isinstance(_SCREAMING_SNAKE_CASE				,       tf.keras.Model     )
    lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE     )
    self.assert_outputs_same(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase    				= int((config.image_size // config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

  for model_class in self.all_model_classes:
   lowerCAmelCase    				= model_class(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )

   if model_class.__name__ == "TFViTMAEModel":
    lowerCAmelCase    				= outputs.last_hidden_state.numpy()
    lowerCAmelCase    				= 0
   else:
    lowerCAmelCase    				= outputs.logits.numpy()
    lowerCAmelCase    				= 0

   with tempfile.TemporaryDirectory() as tmpdirname:
    model.save_pretrained(_SCREAMING_SNAKE_CASE				,       saved_model=_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase    				= model_class.from_pretrained(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )

    if model_class.__name__ == "TFViTMAEModel":
     lowerCAmelCase    				= after_outputs['last_hidden_state'].numpy()
     lowerCAmelCase    				= 0
    else:
     lowerCAmelCase    				= after_outputs['logits'].numpy()
     lowerCAmelCase    				= 0

    lowerCAmelCase    				= np.amax(np.abs(out_a - out_a     )     )
    self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1E-5     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase    				= int((config.image_size // config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

  for model_class in self.all_model_classes:
   lowerCAmelCase    				= model_class(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= model.get_config()
   # make sure that returned config is jsonifiable, which is required by keras
   json.dumps(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= model_class.from_config(model.get_config()     )
   # make sure it also accepts a normal config
   lowerCAmelCase    				= model_class.from_config(model.config     )
   lowerCAmelCase    				= new_model(_SCREAMING_SNAKE_CASE     )  # Build model
   new_model.set_weights(model.get_weights()     )
   lowerCAmelCase    				= new_model(_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )

   self.assert_outputs_same(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )



 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass



 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'     )
  self.assertIsNotNone(_SCREAMING_SNAKE_CASE     )


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







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



 @cached_property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'     ) if is_vision_available() else None



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  np.random.seed(2     )

  lowerCAmelCase    				= TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'     )

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

  # prepare a noise vector that will be also used for testing the TF model
  # (this way we can ensure that the PT and TF models operate on the same inputs)
  lowerCAmelCase    				= ViTMAEConfig()
  lowerCAmelCase    				= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
  lowerCAmelCase    				= np.random.uniform(size=(1, num_patches)     )

  # forward pass
  lowerCAmelCase    				= model(**_SCREAMING_SNAKE_CASE				,       noise=_SCREAMING_SNAKE_CASE     )

  # verify the logits
  lowerCAmelCase    				= tf.convert_to_tensor([1, 1_96, 7_68]     )
  self.assertEqual(outputs.logits.shape				,       _SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= tf.convert_to_tensor(
      [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]     )

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








'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (DDPMParallelScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				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", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

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

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

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
      assert abs(result_mean.item() - 0.5_005	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

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





