




"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      StableDiffusionXLImgaImgPipeline
 lowercase__				      =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 lowercase__				      =      PipelineTesterMixin.required_optional_params - {"latents"}
 lowercase__				      =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowercase__				      =      IMAGE_TO_IMAGE_IMAGE_PARAMS
 lowercase__				      =      IMAGE_TO_IMAGE_IMAGE_PARAMS


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   torch.manual_seed(0	)
   _snake_case							:	Any			=						UNetaDConditionModel(
       block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),   attention_head_dim=(2, 4),   use_linear_projection=a_,   addition_embed_type="""text_time""",   addition_time_embed_dim=8,   transformer_layers_per_block=(1, 2),   projection_class_embeddings_input_dim=80,   cross_attention_dim=64,   )
   _snake_case							:	List[str]			=						EulerDiscreteScheduler(
       beta_start=0.00_085,   beta_end=0.012,   steps_offset=1,   beta_schedule="""scaled_linear""",   timestep_spacing="""leading""",   )
   torch.manual_seed(0	)
   _snake_case							:	Optional[Any]			=						AutoencoderKL(
       block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],   latent_channels=4,   sample_size=128,   )
   torch.manual_seed(0	)
   _snake_case							:	Union[str, Any]			=						CLIPTextConfig(
       bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1_000,   hidden_act="""gelu""",   projection_dim=32,   )
   _snake_case							:	Dict			=						CLIPTextModel(a_	)
   _snake_case							:	Dict			=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""",   local_files_only=a_	)

   _snake_case							:	Tuple			=						CLIPTextModelWithProjection(a_	)
   _snake_case							:	int			=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""",   local_files_only=a_	)

   _snake_case							:	Union[str, Any]			=						{
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """text_encoder_2""": text_encoder_a,
       """tokenizer_2""": tokenizer_a,
       # "safety_checker": None,
       # "feature_extractor": None,
   }
   return components


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	str,   a_:	Dict=0	):




   '''simple docstring'''



   _snake_case							:	int			=						floats_tensor((1, 3, 32, 32),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	Optional[int]			=						image / 2 + 0.5
   if str(a_	).startswith("""mps"""	):
     _snake_case							:	Optional[int]			=						torch.manual_seed(a_	)
   else:
     _snake_case							:	str			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	str			=						{
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 5.0,
       """output_type""": """numpy""",
       """strength""": 0.75,
   }
   return inputs


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
   _snake_case							:	Tuple			=						self.get_dummy_components()
   _snake_case							:	List[Any]			=						StableDiffusionXLImgaImgPipeline(**a_	)
   _snake_case							:	Optional[Any]			=						sd_pipe.to(a_	)
   sd_pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	str			=						self.get_dummy_inputs(a_	)
   _snake_case							:	List[Any]			=						sd_pipe(**a_	).images
   _snake_case							:	int			=						image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)

   _snake_case							:	List[Any]			=						np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]	)

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


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=3E-3	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.get_dummy_components()
   _snake_case							:	Tuple			=						StableDiffusionXLImgaImgPipeline(**a_	)
   _snake_case							:	Union[str, Any]			=						sd_pipe.to(a_	)
   _snake_case							:	int			=						sd_pipe.to(a_	)
   sd_pipe.set_progress_bar_config(disable=a_	)

   # forward without prompt embeds
   _snake_case							:	str			=						self.get_dummy_inputs(a_	)
   _snake_case							:	Any			=						3 * ["""this is a negative prompt"""]
   _snake_case							:	Optional[Any]			=						negative_prompt
   _snake_case							:	Optional[Any]			=						3 * [inputs["""prompt"""]]

   _snake_case							:	Any			=						sd_pipe(**a_	)
   _snake_case							:	List[str]			=						output.images[0, -3:, -3:, -1]

   # forward with prompt embeds
   _snake_case							:	Optional[int]			=						self.get_dummy_inputs(a_	)
   _snake_case							:	Any			=						3 * ["""this is a negative prompt"""]
   _snake_case							:	List[str]			=						3 * [inputs.pop("""prompt"""	)]

   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	str			=						sd_pipe.encode_prompt(a_,   negative_prompt=a_	)

   _snake_case							:	Any			=						sd_pipe(
       **a_,   prompt_embeds=a_,   negative_prompt_embeds=a_,   pooled_prompt_embeds=a_,   negative_pooled_prompt_embeds=a_,   )
   _snake_case							:	Optional[Any]			=						output.images[0, -3:, -3:, -1]

   # make sure that it's equal
   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4







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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def 				UpperCamelCase_     (       self:	List[str],   a_:	str,   a_:	Dict="cpu",   a_:	str=torch.floataa,   a_:	str=0	):




   '''simple docstring'''



   _snake_case							:	str			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	Any			=						np.random.RandomState(a_	).standard_normal((1, 4, 64, 64)	)
   _snake_case							:	str			=						torch.from_numpy(a_	).to(device=a_,   dtype=a_	)
   _snake_case							:	int			=						{
       """prompt""": """a photograph of an astronaut riding a horse""",
       """latents""": latents,
       """generator""": generator,
       """num_inference_steps""": 3,
       """guidance_scale""": 7.5,
       """output_type""": """numpy""",
   }
   return inputs


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	int			=						self.get_inputs(a_	)
   _snake_case							:	Dict			=						pipe(**a_	).images
   _snake_case							:	Optional[int]			=						image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   _snake_case							:	Optional[int]			=						np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]	)
   assert np.abs(image_slice - expected_slice	).max() < 7E-3








"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[str],   a_:	List[Any],   a_:	str=13,   a_:	Dict=32,   a_:	Union[str, Any]=3,   a_:	Union[str, Any]=4,   a_:	Tuple=[10, 20, 30, 40],   a_:	Dict=[2, 2, 3, 2],   a_:	Tuple=True,   a_:	Optional[Any]=True,   a_:	Any=37,   a_:	Any="gelu",   a_:	int=10,   a_:	Tuple=0.02,   a_:	str=["stage2", "stage3", "stage4"],   a_:	List[str]=[2, 3, 4],   a_:	List[str]=None,   ):




   '''simple docstring'''



   _snake_case							:	int			=						parent
   _snake_case							:	int			=						batch_size
   _snake_case							:	List[Any]			=						image_size
   _snake_case							:	List[str]			=						num_channels
   _snake_case							:	Tuple			=						num_stages
   _snake_case							:	Union[str, Any]			=						hidden_sizes
   _snake_case							:	List[Any]			=						depths
   _snake_case							:	Tuple			=						is_training
   _snake_case							:	List[str]			=						use_labels
   _snake_case							:	Tuple			=						intermediate_size
   _snake_case							:	List[str]			=						hidden_act
   _snake_case							:	Optional[Any]			=						num_labels
   _snake_case							:	Tuple			=						initializer_range
   _snake_case							:	Tuple			=						out_features
   _snake_case							:	Tuple			=						out_indices
   _snake_case							:	Dict			=						scope


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Any			=						None
   if self.use_labels:
     _snake_case							:	Dict			=						ids_tensor([self.batch_size],   self.num_labels	)

   _snake_case							:	Optional[Any]			=						self.get_config()

   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return ConvNextVaConfig(
       num_channels=self.num_channels,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   num_stages=self.num_stages,   hidden_act=self.hidden_act,   is_decoder=a_,   initializer_range=self.initializer_range,   out_features=self.out_features,   out_indices=self.out_indices,   num_labels=self.num_labels,   )


 def 				UpperCamelCase_     (       self:	int,   a_:	Tuple,   a_:	Any,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						ConvNextVaModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Any			=						model(a_	)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),   )


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	List[str],   a_:	Tuple,   a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						ConvNextVaForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[int]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)	)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Tuple,   a_:	Tuple,   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						model(a_	)

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps	),   len(config.out_features	)	)
   self.parent.assertListEqual(list(result.feature_maps[0].shape	),   [self.batch_size, self.hidden_sizes[1], 4, 4]	)

   # verify channels
   self.parent.assertEqual(len(model.channels	),   len(config.out_features	)	)
   self.parent.assertListEqual(model.channels,   config.hidden_sizes[1:]	)

   # verify backbone works with out_features=None
   _snake_case							:	Tuple			=						None
   _snake_case							:	Tuple			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[Any]			=						model(a_	)

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps	),   1	)
   self.parent.assertListEqual(list(result.feature_maps[0].shape	),   [self.batch_size, self.hidden_sizes[-1], 1, 1]	)

   # verify channels
   self.parent.assertEqual(len(model.channels	),   1	)
   self.parent.assertListEqual(model.channels,   [config.hidden_sizes[-1]]	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	Any			=						config_and_inputs
   _snake_case							:	str			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	List[str]			=						config_and_inputs
   _snake_case							:	Any			=						{"""pixel_values""": pixel_values, """labels""": labels}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (
         ConvNextVaModel,
         ConvNextVaForImageClassification,
         ConvNextVaBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ConvNextVaModelTester(self	)
   _snake_case							:	int			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return


 @unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""	)
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	List[Any]			=						True

     if model_class.__name__ in [
         *get_values(a_	),
         *get_values(a_	),
     ]:
       continue

     _snake_case							:	Tuple			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	Optional[Any]			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Any			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	Any			=						False
     _snake_case							:	List[Any]			=						True

     if (
         model_class.__name__
         in [*get_values(a_	), *get_values(a_	)]
         or not model_class.supports_gradient_checkpointing
     ):
       continue

     _snake_case							:	Dict			=						model_class(a_	)
     model.to(a_	)
     model.gradient_checkpointing_enable()
     model.train()
     _snake_case							:	str			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Optional[int]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	List[str]			=						model_class(a_	)
     _snake_case							:	Union[str, Any]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	int			=						[*signature.parameters.keys()]

     _snake_case							:	Union[str, Any]			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   def check_hidden_states_output(a_:	str,   a_:	Tuple,   a_:	Tuple	):
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Any			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Optional[int]			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _snake_case							:	Optional[int]			=						self.model_tester.num_stages
     self.assertEqual(len(a_	),   expected_num_stages + 1	)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	),   [self.model_tester.image_size // 4, self.model_tester.image_size // 4],   )

   _snake_case	,      _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Optional[Any]			=						True
     check_hidden_states_output(a_,   a_,   a_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _snake_case							:	List[str]			=						True

     check_hidden_states_output(a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	str			=						ConvNextVaModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	List[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






@require_torch
@require_vision
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 @cached_property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	) if is_vision_available() else None


 @slow
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	).to(a_	)

   _snake_case							:	Union[str, Any]			=						self.default_image_processor
   _snake_case							:	List[Any]			=						prepare_img()
   _snake_case							:	Optional[int]			=						preprocessor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   # verify the logits
   _snake_case							:	Optional[int]			=						torch.Size((1, 1_000)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						torch.tensor([0.9_996, 0.1_966, -0.4_386]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, :3],   a_,   atol=1E-4	)	)


