code: 
  
  
  
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, 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 numpy as np
        import tensorflow as tf
  
        from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
        from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import MobileViTImageProcessor
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    self.config_class(**self.inputs_dict )
          self.parent.assertTrue(hasattr(A				,	"""hidden_sizes""" ) )
          self.parent.assertTrue(hasattr(A				,	"""neck_hidden_sizes""" ) )
          self.parent.assertTrue(hasattr(A				,	"""num_attention_heads""" ) )
  
  
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=32				,	A=2				,	A=3				,	A=640				,	A=4				,	A="silu"				,	A=3				,	A=32				,	A=0.1				,	A=0.1				,	A=0.1				,	A=0.02				,	A=True				,	A=True				,	A=10				,	A=None				,	)	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    batch_size
          _SCREAMING_SNAKE_CASE             =    image_size
          _SCREAMING_SNAKE_CASE             =    patch_size
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    last_hidden_size
          _SCREAMING_SNAKE_CASE             =    num_attention_heads
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    conv_kernel_size
          _SCREAMING_SNAKE_CASE             =    output_stride
          _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
          _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE             =    classifier_dropout_prob
          _SCREAMING_SNAKE_CASE             =    use_labels
          _SCREAMING_SNAKE_CASE             =    is_training
          _SCREAMING_SNAKE_CASE             =    num_labels
          _SCREAMING_SNAKE_CASE             =    initializer_range
          _SCREAMING_SNAKE_CASE             =    scope
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.num_labels )
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.image_size, self.image_size]				,	self.num_labels )
  
          _SCREAMING_SNAKE_CASE             =    self.get_config()
  
          return config, pixel_values, labels, pixel_labels
  
  
      def     snake_case_(	self )	->     Any:
          return MobileViTConfig(
              image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	num_attention_heads=self.num_attention_heads				,	hidden_act=self.hidden_act				,	conv_kernel_size=self.conv_kernel_size				,	output_stride=self.output_stride				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	classifier_dropout_prob=self.classifier_dropout_prob				,	initializer_range=self.initializer_range				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    TFMobileViTModel(config=A )
          _SCREAMING_SNAKE_CASE             =    model(A				,	training=A )
          _SCREAMING_SNAKE_CASE             =    _SCREAMING_SNAKE_CASE             =    self.image_size // self.output_stride
          self.parent.assertEqual(
              result.last_hidden_state.shape				,	(self.batch_size, self.last_hidden_size, expected_height, expected_width) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    TFMobileViTForImageClassification(A )
          _SCREAMING_SNAKE_CASE             =    model(A				,	labels=A				,	training=A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    TFMobileViTForSemanticSegmentation(A )
          _SCREAMING_SNAKE_CASE             =    _SCREAMING_SNAKE_CASE             =    self.image_size // self.output_stride
  
          _SCREAMING_SNAKE_CASE             =    model(A				,	training=A )
          self.parent.assertEqual(
              result.logits.shape				,	(self.batch_size, self.num_labels, expected_height, expected_width) )
  
          _SCREAMING_SNAKE_CASE             =    model(A				,	labels=A				,	training=A )
          self.parent.assertEqual(
              result.logits.shape				,	(self.batch_size, self.num_labels, expected_height, expected_width) )
  
  
  
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE       , _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       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				(
          (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
          if is_tf_available()
          else ()
      )
      UpperCamelCase									=				(
          {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
          if is_tf_available()
          else {}
      )
  
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    TFMobileViTModelTester(self )
          _SCREAMING_SNAKE_CASE             =    TFMobileViTConfigTester(self				,	config_class=A				,	has_text_modality=A )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          self.config_tester.run_common_tests()
  
  
      @unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
      def     snake_case_(	self )	->     Any:
          pass
  
  
      @unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
      def     snake_case_(	self )	->     int:
          pass
  
  
      @unittest.skip(reason="""MobileViT does not output attentions""" )
      def     snake_case_(	self )	->     List[Any]:
          pass
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _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(A )
              _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]				,	A )
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          def check_hidden_states_output(A				,	A				,	A ):
              _SCREAMING_SNAKE_CASE             =    model_class(A )
  
              _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.hidden_states
  
              _SCREAMING_SNAKE_CASE             =    5
              self.assertEqual(len(A )				,	A )
  
              # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              _SCREAMING_SNAKE_CASE             =    2
              for i in range(len(A ) ):
                  self.assertListEqual(
                      list(hidden_states[i].shape[-2:] )				,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]				,	)
                  divisor *= 2
  
              self.assertEqual(self.model_tester.output_stride				,	divisor // 2 )
  
          _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             =    True
              check_hidden_states_output(A				,	A				,	A )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _SCREAMING_SNAKE_CASE             =    True
  
              check_hidden_states_output(A				,	A				,	A )
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A )
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A )
  
  
      @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."""				,	)
      def     snake_case_(	self )	->     Any:
          super().test_dataset_conversion()
  
  
      def     snake_case_(	self				,	A				,	A				,	A=2e-1				,	A=2e-1 )	->     List[str]:
          self.assertTrue(np.allclose(A				,	A				,	atol=A				,	rtol=A ) )
  
  
      @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     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
              if model_class.__name__ != "TFMobileViTModel":
                  _SCREAMING_SNAKE_CASE             =    model_class(A )
                  if getattr(A				,	"""hf_compute_loss"""				,	A ):
                      super().test_keras_fit()
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              # set an ignore index to correctly test the masked loss used in
              # `TFMobileViTForSemanticSegmentation`.
              if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                  _SCREAMING_SNAKE_CASE             =    5
  
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              if getattr(A				,	"""hf_compute_loss"""				,	A ):
                  # The number of elements in the loss should be the same as the number of elements in the label
                  _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(inputs_dict.copy()				,	A				,	return_labels=A )
                  _SCREAMING_SNAKE_CASE             =    prepared_for_class[
                      sorted(prepared_for_class.keys() - inputs_dict.keys()				,	reverse=A )[0]
                  ]
                  _SCREAMING_SNAKE_CASE             =    added_label.shape.as_list()[:1]
  
                  # Test that model correctly compute the loss with kwargs
                  _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(inputs_dict.copy()				,	A				,	return_labels=A )
                  _SCREAMING_SNAKE_CASE             =    {"""input_ids""", """pixel_values""", """input_features"""}
                  _SCREAMING_SNAKE_CASE             =    possible_input_names.intersection(set(A ) ).pop()
                  _SCREAMING_SNAKE_CASE             =    prepared_for_class.pop(A )
  
                  _SCREAMING_SNAKE_CASE             =    model(A				,	**A )[0]
                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
  
                  # Test that model correctly compute the loss when we mask some positions
                  _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(inputs_dict.copy()				,	A				,	return_labels=A )
                  _SCREAMING_SNAKE_CASE             =    {"""input_ids""", """pixel_values""", """input_features"""}
                  _SCREAMING_SNAKE_CASE             =    possible_input_names.intersection(set(A ) ).pop()
                  _SCREAMING_SNAKE_CASE             =    prepared_for_class.pop(A )
                  if "labels" in prepared_for_class:
                      _SCREAMING_SNAKE_CASE             =    prepared_for_class["""labels"""].numpy()
                      if len(labels.shape ) > 1 and labels.shape[1] != 1:
                          # labels[0] = -100
                          _SCREAMING_SNAKE_CASE             =    tf.convert_to_tensor(A )
                          _SCREAMING_SNAKE_CASE             =    model(A				,	**A )[0]
                          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
                          self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
  
                  # Test that model correctly compute the loss with a dict
                  _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(inputs_dict.copy()				,	A				,	return_labels=A )
                  _SCREAMING_SNAKE_CASE             =    model(A )[0]
                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
  
                  # Test that model correctly compute the loss with a tuple
                  _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(inputs_dict.copy()				,	A				,	return_labels=A )
  
                  # Get keys that were added with the _prepare_for_class function
                  _SCREAMING_SNAKE_CASE             =    prepared_for_class.keys() - inputs_dict.keys()
                  _SCREAMING_SNAKE_CASE             =    inspect.signature(model.call ).parameters
                  _SCREAMING_SNAKE_CASE             =    list(signature.keys() )
  
                  # Create a dictionary holding the location of the tensors in the tuple
                  _SCREAMING_SNAKE_CASE             =    {0: input_name}
                  for label_key in label_keys:
                      _SCREAMING_SNAKE_CASE             =    signature_names.index(A )
                      _SCREAMING_SNAKE_CASE             =    label_key
                  _SCREAMING_SNAKE_CASE             =    sorted(tuple_index_mapping.items() )
                  # Initialize a list with their default values, update the values and convert to a tuple
                  _SCREAMING_SNAKE_CASE             =    []
  
                  for name in signature_names:
                      if name != "kwargs":
                          list_input.append(signature[name].default )
  
                  for index, value in sorted_tuple_index_mapping:
                      _SCREAMING_SNAKE_CASE             =    prepared_for_class[value]
  
                  _SCREAMING_SNAKE_CASE             =    tuple(A )
  
                  # Send to model
                  _SCREAMING_SNAKE_CASE             =    model(tuple_input[:-1] )[0]
  
                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     List[str]:
          for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _SCREAMING_SNAKE_CASE             =    TFMobileViTModel.from_pretrained(A )
              self.assertIsNotNone(A )
  
  
  
  
  
  def lowerCamelCase		(  )		->Optional[int]:
      _SCREAMING_SNAKE_CASE             =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image
  
  
  @require_tf
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      @slow
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" )
  
          _SCREAMING_SNAKE_CASE             =    MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" )
          _SCREAMING_SNAKE_CASE             =    prepare_img()
          _SCREAMING_SNAKE_CASE             =    image_processor(images=A				,	return_tensors="""tf""" )
  
          # forward pass
          _SCREAMING_SNAKE_CASE             =    model(**A				,	training=A )
  
          # verify the logits
          _SCREAMING_SNAKE_CASE             =    tf.TensorShape((1, 1000) )
          self.assertEqual(outputs.logits.shape				,	A )
  
          _SCREAMING_SNAKE_CASE             =    tf.constant([-1.9364, -1.2327, -0.4653] )
  
          tf.debugging.assert_near(outputs.logits[0, :3]				,	A				,	atol=1e-4				,	rtol=1e-04 )
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     Union[str, Any]:
          # `from_pt` will be removed
          _SCREAMING_SNAKE_CASE             =    TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
  
          _SCREAMING_SNAKE_CASE             =    MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
  
          _SCREAMING_SNAKE_CASE             =    prepare_img()
          _SCREAMING_SNAKE_CASE             =    image_processor(images=A				,	return_tensors="""tf""" )
  
          # forward pass
          _SCREAMING_SNAKE_CASE             =    model(inputs.pixel_values				,	training=A )
          _SCREAMING_SNAKE_CASE             =    outputs.logits
  
          # verify the logits
          _SCREAMING_SNAKE_CASE             =    tf.TensorShape((1, 21, 32, 32) )
          self.assertEqual(logits.shape				,	A )
  
          _SCREAMING_SNAKE_CASE             =    tf.constant(
              [
                  [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                  [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                  [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
              ] )
  
          tf.debugging.assert_near(logits[0, :3, :3, :3]				,	A				,	rtol=1e-4				,	atol=1e-4 )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    CommonSchedulerState.create(self )
  
          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _SCREAMING_SNAKE_CASE             =    (
              jnp.array(1.0				,	dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )
  
          # standard deviation of the initial noise distribution
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = True				,	)	->     Union[FlaxDDIMSchedulerOutput, Tuple]:
          if state.num_inference_steps is None:
              raise ValueError(
                  """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
  
          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding
  
          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"
  
          # 1. get previous step value (=t-1)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
  
          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                  """ `v_prediction`""" )
  
          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  