


"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Dict							      =    KandinskyVaaControlnetPipeline
							__lowercase:		str							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		List[str]							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		Union[str, Any]							      =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__lowercase:		Tuple							      =    False



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      int       )					->List[str]:



														"""simple docstring"""



														return self.time_input_dim



							@property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return self.time_input_dim * 4



							@property
							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														return 100



							@property
							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														torch.manual_seed(0       )

														snake_case_           =		{
														    """in_channels""": 8,
														    # Out channels is double in channels because predicts mean and variance
														    """out_channels""": 8,
														    """addition_embed_type""": """image_hint""",
														    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
														    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
														    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
														    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
														    """layers_per_block""": 1,
														    """encoder_hid_dim""": self.text_embedder_hidden_size,
														    """encoder_hid_dim_type""": """image_proj""",
														    """cross_attention_dim""": self.cross_attention_dim,
														    """attention_head_dim""": 4,
														    """resnet_time_scale_shift""": """scale_shift""",
														    """class_embed_type""": None,
														}

														snake_case_           =		UNetaDConditionModel(**UpperCAmelCase_       )
														return model



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    "block_out_channels": [32, 32, 64, 64],
														    "down_block_types": [
														        "DownEncoderBlock2D",
														        "DownEncoderBlock2D",
														        "DownEncoderBlock2D",
														        "AttnDownEncoderBlock2D",
														    ],
														    "in_channels": 3,
														    "latent_channels": 4,
														    "layers_per_block": 1,
														    "norm_num_groups": 8,
														    "norm_type": "spatial",
														    "num_vq_embeddings": 12,
														    "out_channels": 3,
														    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
														    "vq_embed_dim": 4,
														}



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														torch.manual_seed(0       )
														snake_case_           =		VQModel(**self.dummy_movq_kwargs       )
														return model



							def 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		self.dummy_unet
														snake_case_           =		self.dummy_movq

														snake_case_           =		DDIMScheduler(
														    num_train_timesteps=1_000		,       beta_schedule="""linear"""		,       beta_start=0.00_085		,       beta_end=0.012		,       clip_sample=UpperCAmelCase_		,       set_alpha_to_one=UpperCAmelCase_		,       steps_offset=1		,       prediction_type="""epsilon"""		,       thresholding=UpperCAmelCase_		,       )

														snake_case_           =		{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]=0       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )
														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(seed + 1       )       ).to(
														    UpperCAmelCase_       )

														# create hint
														snake_case_           =		floats_tensor((1, 3, 64, 64)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )

														if str(UpperCAmelCase_       ).startswith("""mps"""       ):
																					snake_case_           =		torch.manual_seed(UpperCAmelCase_       )
														else:
																					snake_case_           =		torch.Generator(device=UpperCAmelCase_       ).manual_seed(UpperCAmelCase_       )
														snake_case_           =		{
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """hint""": hint,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """guidance_scale""": 4.0,
														    """num_inference_steps""": 2,
														    """output_type""": """np""",
														}
														return inputs



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



														"""simple docstring"""



														snake_case_           =		"""cpu"""

														snake_case_           =		self.get_dummy_components()

														snake_case_           =		self.pipeline_class(**UpperCAmelCase_       )
														snake_case_           =		pipe.to(UpperCAmelCase_       )

														pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		pipe(**self.get_dummy_inputs(UpperCAmelCase_       )       )
														snake_case_           =		output.images

														snake_case_           =		pipe(
														    **self.get_dummy_inputs(UpperCAmelCase_       )		,       return_dict=UpperCAmelCase_		,       )[0]

														snake_case_           =		image[0, -3:, -3:, -1]
														snake_case_           =		image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														snake_case_           =		np.array(
														    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

														assert (
														    np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
														), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

														assert (
														    np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2
														), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





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





							'''simple docstring'''

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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

														snake_case_           =		load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/hint_image_cat.png"""       )
														snake_case_           =		torch.from_numpy(np.array(UpperCAmelCase_       )       ).float() / 255.0
														snake_case_           =		hint.permute(2		,       0		,       1       ).unsqueeze(0       )

														snake_case_           =		KandinskyVaaPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-prior"""		,       torch_dtype=torch.floataa       )
														pipe_prior.to(UpperCAmelCase_       )

														snake_case_           =		KandinskyVaaControlnetPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-controlnet-depth"""		,       torch_dtype=torch.floataa       )
														snake_case_           =		pipeline.to(UpperCAmelCase_       )
														pipeline.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		"""A robot, 4k photo"""

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_       ,						snake_case_           =		pipe_prior(
														    UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=5		,       negative_prompt=""""""		,       ).to_tuple()

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_           =		pipeline(
														    image_embeds=UpperCAmelCase_		,       negative_image_embeds=UpperCAmelCase_		,       hint=UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=100		,       output_type="""np"""		,       )

														snake_case_           =		output.images[0]

														assert image.shape == (512, 512, 3)

														assert_mean_pixel_difference(UpperCAmelCase_		,       UpperCAmelCase_       )







"""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      __A     :





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		image_size
														snake_case_           =		num_channels
														snake_case_           =		num_stages
														snake_case_           =		hidden_sizes
														snake_case_           =		depths
														snake_case_           =		is_training
														snake_case_           =		use_labels
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		num_labels
														snake_case_           =		initializer_range
														snake_case_           =		out_features
														snake_case_           =		out_indices
														snake_case_           =		scope



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



														"""simple docstring"""



														snake_case_           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														snake_case_           =		None
														if self.use_labels:
																					snake_case_           =		ids_tensor([self.batch_size]		,       self.num_labels       )

														snake_case_           =		self.get_config()

														return config, pixel_values, labels



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""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=UpperCAmelCase_		,       initializer_range=self.initializer_range		,       out_features=self.out_features		,       out_indices=self.out_indices		,       num_labels=self.num_labels		,       )



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		ConvNextVaModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )
														# 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 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Optional[Any]       )					->Any:



														"""simple docstring"""



														snake_case_           =		ConvNextVaForImageClassification(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		ConvNextVaBackbone(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														# 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_           =		None
														snake_case_           =		ConvNextVaBackbone(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														# 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 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														snake_case_       ,						snake_case_       ,						snake_case_           =		config_and_inputs
														snake_case_           =		{"""pixel_values""": pixel_values}
														return config, inputs_dict



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



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														snake_case_       ,						snake_case_       ,						snake_case_           =		config_and_inputs
														snake_case_           =		{"""pixel_values""": pixel_values, """labels""": labels}
														return config, inputs_dict





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[Any]							      =    (
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Union[str, Any]							      =    (
							    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Optional[Any]							      =    False
							__lowercase:		Any							      =    False
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Dict							      =    False



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



														"""simple docstring"""



														snake_case_           =		ConvNextVaModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       has_text_modality=UpperCAmelCase_		,       hidden_size=37       )



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



														"""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 		lowerCAmelCase       (				self							:      str       )					->Optional[Any]:



														"""simple docstring"""



														return



							@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""       )
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""       )
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_with_labels()
																					snake_case_           =		True

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

																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.train()
																					snake_case_           =		self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_		,       return_labels=UpperCAmelCase_       )
																					snake_case_           =		model(**UpperCAmelCase_       ).loss
																					loss.backward()



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



														"""simple docstring"""



														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_with_labels()
																					snake_case_           =		False
																					snake_case_           =		True

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

																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.gradient_checkpointing_enable()
																					model.train()
																					snake_case_           =		self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_		,       return_labels=UpperCAmelCase_       )
																					snake_case_           =		model(**UpperCAmelCase_       ).loss
																					loss.backward()



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					snake_case_           =		inspect.signature(model.forward       )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					snake_case_           =		[*signature.parameters.keys()]

																					snake_case_           =		["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														def check_hidden_states_output(UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      str       ):
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()

																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

																					snake_case_           =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					snake_case_           =		self.model_tester.num_stages
																					self.assertEqual(len(UpperCAmelCase_       )		,       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_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_           =		True

																					check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		ConvNextVaModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )







def 						_a				(      )    ->				str:
							snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





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





							'''simple docstring'''

							@cached_property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



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



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



														snake_case_           =		ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""       ).to(UpperCAmelCase_       )

														snake_case_           =		self.default_image_processor
														snake_case_           =		prepare_img()
														snake_case_           =		preprocessor(images=UpperCAmelCase_		,       return_tensors="""pt"""       ).to(UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(**UpperCAmelCase_       )

														# verify the logits
														snake_case_           =		torch.Size((1, 1_000)       )
														self.assertEqual(outputs.logits.shape		,       UpperCAmelCase_       )

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



