
'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXLMForMultipleChoice,
       TFXLMForQuestionAnsweringSimple,
       TFXLMForSequenceClassification,
       TFXLMForTokenClassification,
       TFXLMModel,
       TFXLMWithLMHeadModel,
       XLMConfig,
   )







class 	__lowerCAmelCase    :



     def __init__(self						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    parent
          _UpperCAmelCase					:  Dict         =    1_3
          _UpperCAmelCase					:  Optional[Any]         =    7
          _UpperCAmelCase					:  Optional[Any]         =    True
          _UpperCAmelCase					:  str         =    True
          _UpperCAmelCase					:  Optional[Any]         =    True
          _UpperCAmelCase					:  Dict         =    True
          _UpperCAmelCase					:  Tuple         =    True
          _UpperCAmelCase					:  int         =    False
          _UpperCAmelCase					:  Optional[Any]         =    False
          _UpperCAmelCase					:  List[Any]         =    False
          _UpperCAmelCase					:  Any         =    2
          _UpperCAmelCase					:  int         =    9_9
          _UpperCAmelCase					:  List[Any]         =    0
          _UpperCAmelCase					:  Optional[Any]         =    3_2
          _UpperCAmelCase					:  Tuple         =    2
          _UpperCAmelCase					:  int         =    4
          _UpperCAmelCase					:  Dict         =    0.1
          _UpperCAmelCase					:  Any         =    0.1
          _UpperCAmelCase					:  List[Any]         =    5_1_2
          _UpperCAmelCase					:  Optional[int]         =    1_6
          _UpperCAmelCase					:  List[Any]         =    2
          _UpperCAmelCase					:  Optional[int]         =    0.0_2
          _UpperCAmelCase					:  Dict         =    3
          _UpperCAmelCase					:  Union[str, Any]         =    4
          _UpperCAmelCase					:  Union[str, Any]         =    """last"""
          _UpperCAmelCase					:  List[Any]         =    True
          _UpperCAmelCase					:  str         =    None
          _UpperCAmelCase					:  Union[str, Any]         =    0



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[Any]         =    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size	)
          _UpperCAmelCase					:  Union[str, Any]         =    random_attention_mask([self.batch_size, self.seq_length]						,	dtype=tf.floataa	)

          _UpperCAmelCase					:  Optional[Any]         =    None
          if self.use_input_lengths:
               _UpperCAmelCase					:  str         =    (
                   ids_tensor([self.batch_size]						,	vocab_size=2	) + self.seq_length - 2
               )  # small variation of seq_length

          _UpperCAmelCase					:  List[str]         =    None
          if self.use_token_type_ids:
               _UpperCAmelCase					:  str         =    ids_tensor([self.batch_size, self.seq_length]						,	self.n_langs	)

          _UpperCAmelCase					:  str         =    None
          _UpperCAmelCase					:  Optional[Any]         =    None
          _UpperCAmelCase					:  Tuple         =    None
          if self.use_labels:
               _UpperCAmelCase					:  Optional[int]         =    ids_tensor([self.batch_size]						,	self.type_sequence_label_size	)
               _UpperCAmelCase					:  List[str]         =    ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels	)
               _UpperCAmelCase					:  str         =    ids_tensor([self.batch_size]						,	2						,	dtype=tf.floataa	)
               _UpperCAmelCase					:  str         =    ids_tensor([self.batch_size]						,	self.num_choices	)

          _UpperCAmelCase					:  Any         =    XLMConfig(
              vocab_size=self.vocab_size						,	n_special=self.n_special						,	emb_dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	gelu_activation=self.gelu_activation						,	sinusoidal_embeddings=self.sinusoidal_embeddings						,	asm=self.asm						,	causal=self.causal						,	n_langs=self.n_langs						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	summary_type=self.summary_type						,	use_proj=self.use_proj						,	bos_token_id=self.bos_token_id						,	)

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Tuple         =    TFXLMModel(config=lowerCAmelCase__	)
          _UpperCAmelCase					:  Union[str, Any]         =    {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
          _UpperCAmelCase					:  Tuple         =    model(lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    [input_ids, input_mask]
          _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__	)
          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Tuple         =    TFXLMWithLMHeadModel(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
          _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__	)

          _UpperCAmelCase					:  Tuple         =    outputs

          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Tuple         =    TFXLMForQuestionAnsweringSimple(lowerCAmelCase__	)

          _UpperCAmelCase					:  Any         =    {"""input_ids""": input_ids, """lengths""": input_lengths}

          _UpperCAmelCase					:  Any         =    model(lowerCAmelCase__	)

          self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)	)
          self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    TFXLMForSequenceClassification(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    {"""input_ids""": input_ids, """lengths""": input_lengths}

          _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__	)

          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Optional[Any]         =    self.num_labels
          _UpperCAmelCase					:  Tuple         =    TFXLMForTokenClassification(config=lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          _UpperCAmelCase					:  int         =    model(lowerCAmelCase__	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Any         =    self.num_choices
          _UpperCAmelCase					:  Union[str, Any]         =    TFXLMForMultipleChoice(config=lowerCAmelCase__	)
          _UpperCAmelCase					:  List[str]         =    tf.tile(tf.expand_dims(lowerCAmelCase__						,	1	)						,	(1, self.num_choices, 1)	)
          _UpperCAmelCase					:  Optional[Any]         =    tf.tile(tf.expand_dims(lowerCAmelCase__						,	1	)						,	(1, self.num_choices, 1)	)
          _UpperCAmelCase					:  Union[str, Any]         =    tf.tile(tf.expand_dims(lowerCAmelCase__						,	1	)						,	(1, self.num_choices, 1)	)
          _UpperCAmelCase					:  Tuple         =    {
              """input_ids""": multiple_choice_inputs_ids,
              """attention_mask""": multiple_choice_input_mask,
              """token_type_ids""": multiple_choice_token_type_ids,
          }
          _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    self.prepare_config_and_inputs()
          (
              (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     (
              _UpperCAmelCase
          )  ,     
          )					:  Optional[int]         =    config_and_inputs
          _UpperCAmelCase					:  List[Any]         =    {
              """input_ids""": input_ids,
              """token_type_ids""": token_type_ids,
              """langs""": token_type_ids,
              """lengths""": input_lengths,
          }
          return config, inputs_dict







@require_tf
class 	__lowerCAmelCase    (  __a       ,					__a       ,					unittest.TestCase				):
     snake_case   :    int							      =      (
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     snake_case   :    int							      =      (
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     snake_case   :    int							      =      (
         {
             """feature-extraction""": TFXLMModel,
             """fill-mask""": TFXLMWithLMHeadModel,
             """question-answering""": TFXLMForQuestionAnsweringSimple,
             """text-classification""": TFXLMForSequenceClassification,
             """text-generation""": TFXLMWithLMHeadModel,
             """token-classification""": TFXLMForTokenClassification,
             """zero-shot""": TFXLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     snake_case   :    Dict							      =      False
     snake_case   :    Tuple							      =      False



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("""Fast"""	)
          ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

          return False



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    TFXLMModelTester(self	)
          _UpperCAmelCase					:  Any         =    ConfigTester(self						,	config_class=lowerCAmelCase__						,	emb_dim=3_7	)



     def     snake_case_ (self	):
          self.config_tester.run_common_tests()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCAmelCase__	)



     @slow
     def     snake_case_ (self	):
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase					:  Optional[Any]         =    TFXLMModel.from_pretrained(lowerCAmelCase__	)
               self.assertIsNotNone(lowerCAmelCase__	)







@require_tf
class 	__lowerCAmelCase    (  unittest.TestCase				):



     @slow
     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""	)
          _UpperCAmelCase					:  str         =    tf.convert_to_tensor([[1_4, 4_4_7]]						,	dtype=tf.intaa	)  # the president
          _UpperCAmelCase					:  Optional[int]         =    [
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
              1_4,
              4_4_7,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          _UpperCAmelCase					:  int         =    model.generate(lowerCAmelCase__						,	do_sample=lowerCAmelCase__	)
          self.assertListEqual(output_ids[0].numpy().tolist()						,	lowerCAmelCase__	)




'''simple docstring'''

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[str]							      =      (HeunDiscreteScheduler,)
     snake_case   :    Optional[Any]							      =      1_0



     def     snake_case_ (self						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  Union[str, Any]         =    {
              """num_train_timesteps""": 1_1_0_0,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**lowerCAmelCase__	)
          return config



     def     snake_case_ (self	):
          for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,	[0.0_0_0_2, 0.0_0_2, 0.0_2]	):
               self.check_over_configs(beta_start=lowerCAmelCase__						,	beta_end=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase__						,	clip_sample=lowerCAmelCase__	)



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



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Optional[int]         =    self.get_scheduler_config()
          _UpperCAmelCase					:  List[str]         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps	)

          _UpperCAmelCase					:  str         =    self.dummy_model()
          _UpperCAmelCase					:  List[Any]         =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Tuple         =    sample.to(lowerCAmelCase__	)

          for i, t in enumerate(scheduler.timesteps	):
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Any         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Tuple         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    output.prev_sample

          _UpperCAmelCase					:  Optional[Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  int         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Any         =    self.get_scheduler_config(prediction_type="""v_prediction"""	)
          _UpperCAmelCase					:  Any         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps	)

          _UpperCAmelCase					:  Optional[Any]         =    self.dummy_model()
          _UpperCAmelCase					:  Any         =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Any         =    sample.to(lowerCAmelCase__	)

          for i, t in enumerate(scheduler.timesteps	):
               _UpperCAmelCase					:  List[str]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Union[str, Any]         =    output.prev_sample

          _UpperCAmelCase					:  Union[str, Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  str         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_934e-07	) < 1e-2
               assert abs(result_mean.item() - 6.1_112e-10	) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 4.693_428_650_170_972e-07	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Any         =    self.get_scheduler_config()
          _UpperCAmelCase					:  Optional[int]         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps						,	device=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    self.dummy_model()
          _UpperCAmelCase					:  Dict         =    self.dummy_sample_deter.to(lowerCAmelCase__	) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[int]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  int         =    output.prev_sample

          _UpperCAmelCase					:  Any         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  Optional[Any]         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if str(lowerCAmelCase__	).startswith("""cpu"""	):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3
          elif str(lowerCAmelCase__	).startswith("""mps"""	):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-2
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  str         =    self.get_scheduler_config()
          _UpperCAmelCase					:  Union[str, Any]         =    scheduler_class(**lowerCAmelCase__						,	use_karras_sigmas=lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps						,	device=lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[int]         =    self.dummy_model()
          _UpperCAmelCase					:  Optional[Any]         =    self.dummy_sample_deter.to(lowerCAmelCase__	) * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sample.to(lowerCAmelCase__	)

          for t in scheduler.timesteps:
               _UpperCAmelCase					:  Dict         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[int]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    output.prev_sample

          _UpperCAmelCase					:  Union[str, Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  List[Any]         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          assert abs(result_sum.item() - 0.0_0_0_1_5	) < 1e-2
          assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07	) < 1e-2


