

'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			CycleDiffusionPipeline
  SCREAMING_SNAKE_CASE    :  Any							=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      'negative_prompt',
      'height',
      'width',
      'negative_prompt_embeds',
  }
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			PipelineTesterMixin.required_optional_params - {'latents'}
  SCREAMING_SNAKE_CASE    :  List[Any]							=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}      )
  SCREAMING_SNAKE_CASE    :  List[Any]							=			IMAGE_TO_IMAGE_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE    :  List[str]							=			IMAGE_TO_IMAGE_IMAGE_PARAMS






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   torch.manual_seed(0			)
   __lowercase							  = UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)					,layers_per_block=2					,sample_size=3_2					,in_channels=4					,out_channels=4					,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,cross_attention_dim=3_2					,)
   __lowercase							  = DDIMScheduler(
       beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='''scaled_linear'''					,num_train_timesteps=1_0_0_0					,clip_sample=lowercase__					,set_alpha_to_one=lowercase__					,)
   torch.manual_seed(0			)
   __lowercase							  = AutoencoderKL(
       block_out_channels=[3_2, 6_4]					,in_channels=3					,out_channels=3					,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,latent_channels=4					,)
   torch.manual_seed(0			)
   __lowercase							  = CLIPTextConfig(
       bos_token_id=0					,eos_token_id=2					,hidden_size=3_2					,intermediate_size=3_7					,layer_norm_eps=1e-0_5					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_0_0_0					,)
   __lowercase							  = CLIPTextModel(lowercase__			)
   __lowercase							  = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

   __lowercase							  = {
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''vae''': vae,
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''safety_checker''': None,
       '''feature_extractor''': None,
   }
   return components






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Union[str, Any]					,lowercase__   :     List[str]=0			):
   __lowercase							  = floats_tensor((1, 3, 3_2, 3_2)					,rng=random.Random(lowercase__			)			).to(lowercase__			)
   __lowercase							  = image / 2 + 0.5
   if str(lowercase__			).startswith('''mps'''			):
    __lowercase							  = torch.manual_seed(lowercase__			)
   else:
    __lowercase							  = torch.Generator(device=lowercase__			).manual_seed(lowercase__			)
   __lowercase							  = {
       '''prompt''': '''An astronaut riding an elephant''',
       '''source_prompt''': '''An astronaut riding a horse''',
       '''image''': image,
       '''generator''': generator,
       '''num_inference_steps''': 2,
       '''eta''': 0.1,
       '''strength''': 0.8,
       '''guidance_scale''': 3,
       '''source_guidance_scale''': 1,
       '''output_type''': '''numpy''',
   }
   return inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = '''cpu'''  # ensure determinism for the device-dependent torch.Generator

   __lowercase							  = self.get_dummy_components()
   __lowercase							  = CycleDiffusionPipeline(**lowercase__			)
   __lowercase							  = pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe(**lowercase__			)
   __lowercase							  = output.images

   __lowercase							  = images[0, -3:, -3:, -1]

   assert images.shape == (1, 3_2, 3_2, 3)
   __lowercase							  = np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2






  @unittest.skipIf(torch_device != '''cuda'''					,'''This test requires a GPU'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_dummy_components()
   for name, module in components.items():
    if hasattr(lowercase__					,'''half'''			):
     __lowercase							  = module.half()
   __lowercase							  = CycleDiffusionPipeline(**lowercase__			)
   __lowercase							  = pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe(**lowercase__			)
   __lowercase							  = output.images

   __lowercase							  = images[0, -3:, -3:, -1]

   assert images.shape == (1, 3_2, 3_2, 3)
   __lowercase							  = np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return super().test_save_load_local()






  @unittest.skip('''non-deterministic pipeline'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return super().test_inference_batch_single_identical()






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   return super().test_dict_tuple_outputs_equivalent()






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return super().test_save_load_optional_components()






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
   __lowercase							  = init_image.resize((5_1_2, 5_1_2)			)

   __lowercase							  = '''CompVis/stable-diffusion-v1-4'''
   __lowercase							  = DDIMScheduler.from_pretrained(lowercase__					,subfolder='''scheduler'''			)
   __lowercase							  = CycleDiffusionPipeline.from_pretrained(
       lowercase__					,scheduler=lowercase__					,safety_checker=lowercase__					,torch_dtype=torch.floataa					,revision='''fp16'''			)

   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)
   pipe.enable_attention_slicing()

   __lowercase							  = '''A black colored car'''
   __lowercase							  = '''A blue colored car'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,source_prompt=lowercase__					,image=lowercase__					,num_inference_steps=1_0_0					,eta=0.1					,strength=0.8_5					,guidance_scale=3					,source_guidance_scale=1					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images

   # the values aren't exactly equal, but the images look the same visually
   assert np.abs(image - expected_image			).max() < 5e-1






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
   __lowercase							  = init_image.resize((5_1_2, 5_1_2)			)

   __lowercase							  = '''CompVis/stable-diffusion-v1-4'''
   __lowercase							  = DDIMScheduler.from_pretrained(lowercase__					,subfolder='''scheduler'''			)
   __lowercase							  = CycleDiffusionPipeline.from_pretrained(lowercase__					,scheduler=lowercase__					,safety_checker=lowercase__			)

   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)
   pipe.enable_attention_slicing()

   __lowercase							  = '''A black colored car'''
   __lowercase							  = '''A blue colored car'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,source_prompt=lowercase__					,image=lowercase__					,num_inference_steps=1_0_0					,eta=0.1					,strength=0.8_5					,guidance_scale=3					,source_guidance_scale=1					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images

   assert np.abs(image - expected_image			).max() < 2e-2




'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[int]=1_3					,lowercase__   :     Any=7					,lowercase__   :     Union[str, Any]=True					,lowercase__   :     Optional[int]=True					,lowercase__   :     List[str]=True					,lowercase__   :     str=True					,lowercase__   :     Dict=9_9					,lowercase__   :     Union[str, Any]=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     int=4					,lowercase__   :     Dict=3_7					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     str=0.1					,lowercase__   :     List[str]=0.1					,lowercase__   :     Any=5_1_2					,lowercase__   :     Optional[int]=1_6					,lowercase__   :     Optional[int]=2					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=False					,lowercase__   :     Optional[int]=True					,lowercase__   :     str="None"					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[Any]=4					,lowercase__   :     Union[str, Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = seq_length
   __lowercase							  = is_training
   __lowercase							  = use_input_mask
   __lowercase							  = use_token_type_ids
   __lowercase							  = use_labels
   __lowercase							  = vocab_size
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = max_position_embeddings
   __lowercase							  = type_vocab_size
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = num_labels
   __lowercase							  = num_choices
   __lowercase							  = relative_attention
   __lowercase							  = position_biased_input
   __lowercase							  = pos_att_type
   __lowercase							  = scope






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size			)

   __lowercase							  = None
   if self.use_input_mask:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,vocab_size=2			)

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

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

   __lowercase							  = self.get_config()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return DebertaConfig(
       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					,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					,initializer_range=self.initializer_range					,relative_attention=self.relative_attention					,position_biased_input=self.position_biased_input					,pos_att_type=self.pos_att_type					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_config()
   __lowercase							  = 3_0_0
   return config






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]			):
   self.parent.assertListEqual(list(result.loss.size()			)					,[]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = DebertaModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__			)[0]

   self.parent.assertListEqual(list(sequence_output.size()			)					,[self.batch_size, self.seq_length, self.hidden_size]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     str					,lowercase__   :     Tuple					,lowercase__   :     Optional[Any]					,lowercase__   :     Dict					,lowercase__   :     int					,lowercase__   :     Tuple					,lowercase__   :     int			):
   __lowercase							  = DebertaForMaskedLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     Union[str, Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForSequenceClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertListEqual(list(result.logits.size()			)					,[self.batch_size, self.num_labels]			)
   self.check_loss_output(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     Optional[int]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForTokenClassification(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Any					,lowercase__   :     Any					,lowercase__   :     Dict					,lowercase__   :     Any					,lowercase__   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     List[str]			):
   __lowercase							  = DebertaForQuestionAnswering(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,start_positions=lowercase__					,end_positions=lowercase__					,)
   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 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	
   )							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Tuple							=			(
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			(
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Tuple							=			True
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = DebertaModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_question_answering(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = DebertaModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






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




  """simple docstring"""




  @unittest.skip(reason='''Model not available yet'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = DebertaModel.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
   __lowercase							  = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
   with torch.no_grad():
    __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)[0]
   # compare the actual values for a slice.
   __lowercase							  = torch.tensor(
       [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]			)
   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,lowercase__					,atol=1e-4			)					,F"{output[:, 1:4, 1:4]}"			)

