



"""simple docstring"""





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      MobileBertForMaskedLM,
      MobileBertForMultipleChoice,
      MobileBertForNextSentencePrediction,
      MobileBertForPreTraining,
      MobileBertForQuestionAnswering,
      MobileBertForSequenceClassification,
      MobileBertForTokenClassification,
      MobileBertModel,
  )
class  lowercase__  :







       '''simple docstring'''


       def __init__(				self     :    List[str]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[str]=13						,							_UpperCAmelCase     :    List[Any]=7						,							_UpperCAmelCase     :    Optional[Any]=True						,							_UpperCAmelCase     :    Any=True						,							_UpperCAmelCase     :    List[Any]=True						,							_UpperCAmelCase     :    List[str]=True						,							_UpperCAmelCase     :    List[Any]=99						,							_UpperCAmelCase     :    int=64						,							_UpperCAmelCase     :    int=32						,							_UpperCAmelCase     :    int=5						,							_UpperCAmelCase     :    Any=4						,							_UpperCAmelCase     :    Tuple=37						,							_UpperCAmelCase     :    List[str]="gelu"						,							_UpperCAmelCase     :    str=0.1						,							_UpperCAmelCase     :    Tuple=0.1						,							_UpperCAmelCase     :    Any=512						,							_UpperCAmelCase     :    Optional[int]=16						,							_UpperCAmelCase     :    Union[str, Any]=2						,							_UpperCAmelCase     :    Union[str, Any]=0.02						,							_UpperCAmelCase     :    Any=3						,							_UpperCAmelCase     :    Optional[int]=4						,							_UpperCAmelCase     :    str=None						,							)					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				parent
             UpperCAmelCase_     					=				batch_size
             UpperCAmelCase_     					=				seq_length
             UpperCAmelCase_     					=				is_training
             UpperCAmelCase_     					=				use_input_mask
             UpperCAmelCase_     					=				use_token_type_ids
             UpperCAmelCase_     					=				use_labels
             UpperCAmelCase_     					=				vocab_size
             UpperCAmelCase_     					=				hidden_size
             UpperCAmelCase_     					=				embedding_size
             UpperCAmelCase_     					=				num_hidden_layers
             UpperCAmelCase_     					=				num_attention_heads
             UpperCAmelCase_     					=				intermediate_size
             UpperCAmelCase_     					=				hidden_act
             UpperCAmelCase_     					=				hidden_dropout_prob
             UpperCAmelCase_     					=				attention_probs_dropout_prob
             UpperCAmelCase_     					=				max_position_embeddings
             UpperCAmelCase_     					=				type_vocab_size
             UpperCAmelCase_     					=				type_sequence_label_size
             UpperCAmelCase_     					=				initializer_range
             UpperCAmelCase_     					=				num_labels
             UpperCAmelCase_     					=				num_choices
             UpperCAmelCase_     					=				scope



       def 	lowercase__							(				self     :    Dict       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size       )

             UpperCAmelCase_     					=				None
             if self.use_input_mask:
                   UpperCAmelCase_     					=				random_attention_mask([self.batch_size, self.seq_length]       )

             UpperCAmelCase_     					=				None
             if self.use_token_type_ids:
                   UpperCAmelCase_     					=				ids_tensor([self.batch_size, self.seq_length]						,							self.type_vocab_size       )

             UpperCAmelCase_     					=				None
             UpperCAmelCase_     					=				None
             UpperCAmelCase_     					=				None
             if self.use_labels:
                   UpperCAmelCase_     					=				ids_tensor([self.batch_size]						,							self.type_sequence_label_size       )
                   UpperCAmelCase_     					=				ids_tensor([self.batch_size, self.seq_length]						,							self.num_labels       )
                   UpperCAmelCase_     					=				ids_tensor([self.batch_size]						,							self.num_choices       )

             UpperCAmelCase_     					=				self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 	lowercase__							(				self     :    List[Any]       )					->		Any:


             '''simple docstring'''




             return MobileBertConfig(
                 vocab_size=self.vocab_size						,							hidden_size=self.hidden_size						,							num_hidden_layers=self.num_hidden_layers						,							num_attention_heads=self.num_attention_heads						,							intermediate_size=self.intermediate_size						,							embedding_size=self.embedding_size						,							hidden_act=self.hidden_act						,							hidden_dropout_prob=self.hidden_dropout_prob						,							attention_probs_dropout_prob=self.attention_probs_dropout_prob						,							max_position_embeddings=self.max_position_embeddings						,							type_vocab_size=self.type_vocab_size						,							is_decoder=_UpperCAmelCase						,							initializer_range=self.initializer_range						,							)



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Any       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertModel(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase       )
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase       )
             UpperCAmelCase_     					=				model(_UpperCAmelCase       )

             self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)       )
             self.parent.assertEqual(result.pooler_output.shape						,							(self.batch_size, self.hidden_size)       )



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Any       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertForMaskedLM(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase       )
             self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)       )



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Any       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertForNextSentencePrediction(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(
                 _UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase						,							)
             self.parent.assertEqual(result.logits.shape						,							(self.batch_size, 2)       )



       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Any       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertForPreTraining(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(
                 _UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase						,							next_sentence_label=_UpperCAmelCase						,							)
             self.parent.assertEqual(result.prediction_logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)       )
             self.parent.assertEqual(result.seq_relationship_logits.shape						,							(self.batch_size, 2)       )



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Optional[int]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertForQuestionAnswering(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(
                 _UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							start_positions=_UpperCAmelCase						,							end_positions=_UpperCAmelCase						,							)
             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 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Optional[Any]       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.num_labels
             UpperCAmelCase_     					=				MobileBertForSequenceClassification(_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase       )
             self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels)       )



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Tuple       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.num_labels
             UpperCAmelCase_     					=				MobileBertForTokenClassification(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase       )
             self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels)       )



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    Tuple       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.num_choices
             UpperCAmelCase_     					=				MobileBertForMultipleChoice(config=_UpperCAmelCase       )
             model.to(_UpperCAmelCase       )
             model.eval()
             UpperCAmelCase_     					=				input_ids.unsqueeze(1       ).expand(-1						,							self.num_choices						,							-1       ).contiguous()
             UpperCAmelCase_     					=				token_type_ids.unsqueeze(1       ).expand(-1						,							self.num_choices						,							-1       ).contiguous()
             UpperCAmelCase_     					=				input_mask.unsqueeze(1       ).expand(-1						,							self.num_choices						,							-1       ).contiguous()
             UpperCAmelCase_     					=				model(
                 _UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							labels=_UpperCAmelCase						,							)
             self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_choices)       )







       def 	lowercase__							(				self     :    Optional[Any]       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.prepare_config_and_inputs()
             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				config_and_inputs
             UpperCAmelCase_     					=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict



@require_torch
class  lowercase__  (       SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	(
           (
               MobileBertModel,
               MobileBertForMaskedLM,
               MobileBertForMultipleChoice,
               MobileBertForNextSentencePrediction,
               MobileBertForPreTraining,
               MobileBertForQuestionAnswering,
               MobileBertForSequenceClassification,
               MobileBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase		=	(
           {
               '''feature-extraction''': MobileBertModel,
               '''fill-mask''': MobileBertForMaskedLM,
               '''question-answering''': MobileBertForQuestionAnswering,
               '''text-classification''': MobileBertForSequenceClassification,
               '''token-classification''': MobileBertForTokenClassification,
               '''zero-shot''': MobileBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase		=	True



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Optional[int]=False       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				super()._prepare_for_class(_UpperCAmelCase						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )

             if return_labels:
                   if model_class in get_values(_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.seq_length)						,							dtype=torch.long						,							device=_UpperCAmelCase       )
                         UpperCAmelCase_     					=				torch.zeros(
                             self.model_tester.batch_size						,							dtype=torch.long						,							device=_UpperCAmelCase       )
             return inputs_dict



       def 	lowercase__							(				self     :    List[str]       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertModelTester(self       )
             UpperCAmelCase_     					=				ConfigTester(self						,							config_class=_UpperCAmelCase						,							hidden_size=37       )



       def 	lowercase__							(				self     :    List[Any]       )					->		Dict:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       def 	lowercase__							(				self     :    str       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_model(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    int       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_masked_lm(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Any       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    List[Any]       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Tuple       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_pretraining(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Dict       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_question_answering(*_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Optional[Any]       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Optional[int]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mobilebert_for_token_classification(*_UpperCAmelCase       )



def   a__      ( lowerCAmelCase__ ):
      return torch.tensor(
          lowerCAmelCase__      ,     dtype=torch.long      ,     device=lowerCAmelCase__      ,     )


lowerCamelCase  			=		1e-3


@require_torch
@require_sentencepiece
@require_tokenizers
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       @slow
       def 	lowercase__							(				self     :    Dict       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				MobileBertModel.from_pretrained("google/mobilebert-uncased"       ).to(_UpperCAmelCase       )
             UpperCAmelCase_     					=				_long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]       )
             with torch.no_grad():
                   UpperCAmelCase_     					=				model(_UpperCAmelCase       )[0]
             UpperCAmelCase_     					=				torch.Size((1, 9, 512)       )
             self.assertEqual(output.shape						,							_UpperCAmelCase       )
             UpperCAmelCase_     					=				torch.tensor(
                 [
                     [
                         [-2.4736526e07, 8.2691656e04, 1.6521838e05],
                         [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
                         [2.6047359e00, 1.5677652e00, -1.7324188e-01],
                     ]
                 ]						,							device=_UpperCAmelCase						,							)

             # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
             # ~1 difference, it's therefore not a good idea to measure using addition.
             # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
             # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
             UpperCAmelCase_     					=				torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE       )
             UpperCAmelCase_     					=				torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE       )

             self.assertTrue(lower_bound and upper_bound       )







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase      :					Union[str, Any]					    =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase      :					str					    =      '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =tempfile.mkdtemp()
             os.makedirs(os.path.join(self.diffusers_dir ,   '''schedulers/'''     )     )
             __A    =self.diffusers_dir
             shutil.copy(
                 os.path.join(lowercase__ ,   '''src/diffusers/schedulers/scheduling_ddpm.py'''     ) ,   os.path.join(self.diffusers_dir ,   '''schedulers/scheduling_ddpm.py'''     ) ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''src/diffusers'''
             shutil.rmtree(self.diffusers_dir     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
             if overwrite_result is not None:
                   __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
             __A    =black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=1_1_9     )
             __A    =black.format_str(lowercase__ ,   mode=lowercase__     )
             __A    =os.path.join(self.diffusers_dir ,   '''new_code.py'''     )
             with open(lowercase__ ,   '''w''' ,   newline='''\n'''     ) as f:
                   f.write(lowercase__     )
             if overwrite_result is None:
                   self.assertTrue(len(check_copies.is_copy_consistent(lowercase__     )     ) == 0     )
             else:
                   check_copies.is_copy_consistent(f.name ,   overwrite=lowercase__     )
                   with open(lowercase__ ,   '''r'''     ) as f:
                         self.assertTrue(f.read() ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     )
             self.assertEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   REFERENCE_CODE + '''\n''' ,   )

             # With no empty line at the end
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   lowercase__ ,   )

             # Copy consistency with rename
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )

             # Copy consistency with a really long name
             __A    ='''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
             self.check_copy_consistency(
                 f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,   f'''{long_class_name}SchedulerOutput''' ,   re.sub('''Bert''' ,   lowercase__ ,   lowercase__     ) ,   )

             # Copy consistency with overwrite
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   lowercase__ ,   overwrite_result=re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )


