


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   (	UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			KandinskyImgaImgPipeline
					A__      =			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
					A__      =			[
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					A__      =			[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					A__      =			False

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




							"""simple docstring"""


							return 32

					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Dict:




							"""simple docstring"""


							return 32

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




							"""simple docstring"""


							return self.time_input_dim

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




							"""simple docstring"""


							return self.time_input_dim * 4

					@property
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							return 100

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




							"""simple docstring"""


							lowercase__					      =		XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
							return tokenizer

					@property
					def   lowerCamelCase__					(self   :						Dict		)						->       str:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		MCLIPConfig(
							    numDims=self.cross_attention_dim				, transformerDimensions=self.text_embedder_hidden_size				, hidden_size=self.text_embedder_hidden_size				, intermediate_size=37				, num_attention_heads=4				, num_hidden_layers=5				, vocab_size=1005				, )

							lowercase__					      =		MultilingualCLIP(_UpperCAmelCase		)
							lowercase__					      =		text_encoder.eval()

							return text_encoder

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




							"""simple docstring"""


							torch.manual_seed(0		)

							lowercase__					      =		{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """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""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							lowercase__					      =		UNetaDConditionModel(**_UpperCAmelCase		)
							return model

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




							"""simple docstring"""


							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["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",
							    ],
							    "vq_embed_dim": 4,
							}

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




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Tuple		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		self.dummy_text_encoder
							lowercase__					      =		self.dummy_tokenizer
							lowercase__					      =		self.dummy_unet
							lowercase__					      =		self.dummy_movq

							lowercase__					      =		{
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.00_085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowercase__					      =		DDIMScheduler(**_UpperCAmelCase		)

							lowercase__					      =		{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components

					def   lowerCamelCase__					(self   :						Tuple				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Union[str, Any]=0		)						->       int:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, self.cross_attention_dim)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		floats_tensor((1, self.cross_attention_dim)				, rng=random.Random(seed + 1		)		).to(_UpperCAmelCase		)
							# create init_image
							lowercase__					      =		floats_tensor((1, 3, 64, 64)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((256, 256)		)

							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """prompt""": """horse""",
							    """image""": init_image,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 10,
							    """guidance_scale""": 7.0,
							    """strength""": 0.2,
							    """output_type""": """np""",
							}
							return inputs


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




							"""simple docstring"""


							lowercase__					      =		"""cpu"""

							lowercase__					      =		self.get_dummy_components()

							lowercase__					      =		self.pipeline_class(**_UpperCAmelCase		)
							lowercase__					      =		pipe.to(_UpperCAmelCase		)

							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

							lowercase__					      =		pipe(
							    **self.get_dummy_inputs(_UpperCAmelCase		)				, return_dict=_UpperCAmelCase				, )[0]

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

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

							lowercase__					      =		np.array(
							    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
							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   :						Union[str, Any]		)						->       Union[str, Any]:




							"""simple docstring"""


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


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




							"""simple docstring"""


							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_img2img_frog.npy"""		)

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
							lowercase__					      =		"""A red cartoon frog, 4k"""

							lowercase__					      =		KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""				, torch_dtype=torch.floataa		)
							pipe_prior.to(_UpperCAmelCase		)

							lowercase__					      =		KandinskyImgaImgPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1"""				, torch_dtype=torch.floataa		)
							lowercase__					      =		pipeline.to(_UpperCAmelCase		)

							pipeline.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		torch.Generator(device="""cpu"""		).manual_seed(0		)
							lowercase__      ,     lowercase__					      =		pipe_prior(
							    _UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=5				, negative_prompt=""""""				, ).to_tuple()

							lowercase__					      =		pipeline(
							    _UpperCAmelCase				, image=_UpperCAmelCase				, image_embeds=_UpperCAmelCase				, negative_image_embeds=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=100				, height=768				, width=768				, strength=0.2				, output_type="""np"""				, )

							lowercase__					      =		output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCAmelCase				, _UpperCAmelCase		)





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




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Any		)						->       Dict:




							"""simple docstring"""


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

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

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""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   :						Union[str, Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		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   :						int				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]		)						->       Any:




							"""simple docstring"""


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

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




							"""simple docstring"""


							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		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
							lowercase__					      =		None
							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		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   :						List[Any]		)						->       int:




							"""simple docstring"""


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


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




							"""simple docstring"""


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






@require_torch
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






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

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""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   :						Tuple		)						->       int:




							"""simple docstring"""


							return

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




							"""simple docstring"""


							pass

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




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""		)
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

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

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

									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.train()
									lowercase__					      =		self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase				, return_labels=_UpperCAmelCase		)
									lowercase__					      =		model(**_UpperCAmelCase		).loss
									loss.backward()

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




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

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

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

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

					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       str:




							"""simple docstring"""


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

									with torch.no_grad():
											lowercase__					      =		model(**self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase		)		)

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

									lowercase__					      =		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]				, )

							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		True
									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

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

									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)


					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Any:




							"""simple docstring"""


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






def 			UpperCamelCase (     )       ->  int:
		"""simple docstring"""




		lowercase__					      =		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   :						Optional[Any]		)						->       Optional[Any]:




							"""simple docstring"""


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


					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


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

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		preprocessor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 1000)		)
							self.assertEqual(outputs.logits.shape				, _UpperCAmelCase		)

							lowercase__					      =		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		)		)

